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diff --git a/buch/chapters/10-vektorenmatrizen/linear.tex b/buch/chapters/10-vektorenmatrizen/linear.tex index 10b5a7e..3ad51f1 100644..100755 --- a/buch/chapters/10-vektorenmatrizen/linear.tex +++ b/buch/chapters/10-vektorenmatrizen/linear.tex @@ -33,7 +33,7 @@ aber mit Punkten kann man trotzdem noch nicht rechnen. Ein Vektor fasst die Koordinaten eines Punktes in einem Objekt zusammen, mit dem man auch rechnen und zum Beispiel Parallelverschiebungen algebraisieren kann. -Um auch Streckungen ausdrücken zu können, wird auch eine Menge von +Um auch Streckungen ausdrücken zu können, wird auch eine Menge von Streckungsfaktoren benötigt, mit denen alle Komponenten eines Vektors multipliziert werden können. Sie heissen auch {\em Skalare} und liegen in $\Bbbk$. @@ -73,7 +73,7 @@ a+b = \begin{pmatrix}\lambda a_1\\\vdots\\\lambda a_n\end{pmatrix}. \] -Die üblichen Rechenregeln sind erfüllt, nämlich +Die üblichen Rechenregeln sind erfüllt, nämlich \begin{equation} \begin{aligned} &\text{Kommutativität:} @@ -149,7 +149,7 @@ kann als (abstrakter) Vektor betrachtet werden. \begin{definition} Eine Menge $V$ von Objekten, auf der zwei Operationen definiert, nämlich die Addition, geschrieben $a+b$ für $a,b\in V$ und die -Multiplikation mit Skalaren, geschrieben $\lambda a$ für $a\in V$ und +Multiplikation mit Skalaren, geschrieben $\lambda a$ für $a\in V$ und $\lambda\in \Bbbk$, heisst ein {\em $\Bbbk$-Vektorraum} oder {\em Vektorraum über $\Bbbk$} (oder einfach nur {\em Vektorraum}, wenn $\Bbbk$ aus dem Kontext klar sind), @@ -172,7 +172,7 @@ $\mathbb{C}$ ein Vektorraum über $\mathbb{R}$. \end{beispiel} \begin{beispiel} -Die Menge $C([a,b])$ der stetigen Funktionen $[a,b]\to\mathbb{Re}$ +Die Menge $C([a,b])$ der stetigen Funktionen $[a,b]\to\mathbb{Re}$ bildet ein Vektorraum. Funktionen können addiert und mit reellen Zahlen multipliziert werden: \[ @@ -188,7 +188,7 @@ Die Vektorraum-Rechenregeln \end{beispiel} Die Beispiele zeigen, dass der Begriff des Vektorraums die algebraischen -Eigenschaften eine grosse Zahl sehr verschiedenartiger mathematischer +Eigenschaften eine grosse Zahl sehr verschiedenartiger mathematischer Objekte beschreiben kann. Alle Erkenntnisse, die man ausschliesslich aus Vekotorraumeigenschaften gewonnen hat, sind auf alle diese Objekte übertragbar. @@ -300,7 +300,7 @@ folgt, dass alle $\lambda_1,\dots,\lambda_n=0$ sind. Lineare Abhängigkeit der Vektoren $a_1,\dots,a_n$ bedeutet auch, dass man einzelne der Vektoren durch andere ausdrücken kann. Hat man nämlich eine -Linearkombination~\eqref{buch:vektoren-und-matrizen:eqn:linabhdef} und +Linearkombination~\eqref{buch:vektoren-und-matrizen:eqn:linabhdef} und ist der Koeffizient $\lambda_k\ne 0$, dann kann man nach $a_k$ auflösen: \[ a_k = -\frac{1}{\lambda_k}(\lambda_1a_1+\dots+\widehat{\lambda_ka_k}+\dots+\lambda_na_n). @@ -323,7 +323,7 @@ offenbar eine besondere Bedeutung. Eine linear unabhängig Menge von Vektoren $\mathcal{B}=\{a_1,\dots,a_n\}\subset V$ heisst {\em Basis} von $V$. -Die maximale Anzahl linear unabhängiger Vektoren in $V$ heisst +Die maximale Anzahl linear unabhängiger Vektoren in $V$ heisst {\em Dimension} von $V$. \end{definition} @@ -331,7 +331,7 @@ Die Standardbasisvektoren bilden eine Basis von $V=\Bbbk^n$. \subsubsection{Unterräume} Die Mengen $\langle a_1,\dots,a_n\rangle$ sind Teilmengen -von $V$, in denen die Addition von Vektoren und die Multiplikation mit +von $V$, in denen die Addition von Vektoren und die Multiplikation mit Skalaren immer noch möglich ist. \begin{definition} @@ -352,7 +352,7 @@ gilt. % \subsection{Matrizen \label{buch:grundlagen:subsection:matrizen}} -Die Koeffizienten eines linearen Gleichungssystems finden in einem +Die Koeffizienten eines linearen Gleichungssystems finden in einem Zeilen- oder Spaltenvektor nicht Platz. Wir erweitern das Konzept daher in einer Art, dass Zeilen- und Spaltenvektoren Spezialfälle sind. @@ -378,14 +378,14 @@ M_{m\times n}(\Bbbk) = \{ A\;|\; \text{$A$ ist eine $m\times n$-Matrix}\}. \] Falls $m=n$ gilt, heisst die Matrix $A$ auch {\em quadratisch} \index{quadratische Matrix}% -Man kürzt die Menge der quadratischen Matrizen als +Man kürzt die Menge der quadratischen Matrizen als $M_n(\Bbbk) = M_{n\times n}(\Bbbk)$ ab. \end{definition} -Die $m$-dimensionalen Spaltenvektoren $v\in \Bbbk^m$ sind $m\times 1$-Matrizen +Die $m$-dimensionalen Spaltenvektoren $v\in \Bbbk^m$ sind $m\times 1$-Matrizen $v\in M_{n\times 1}(\Bbbk)$, die $n$-dimensionalen Zeilenvetoren $u\in\Bbbk^n$ sind $1\times n$-Matrizen $v\in M_{1\times n}(\Bbbk)$. -Eine $m\times n$-Matrix $A$ mit den Koeffizienten $a_{ij}$ besteht aus +Eine $m\times n$-Matrix $A$ mit den Koeffizienten $a_{ij}$ besteht aus den $n$ Spaltenvektoren \[ a_1 = \begin{pmatrix} a_{11} \\ a_{21} \\ \vdots \\ a_{m1} \end{pmatrix},\quad @@ -435,7 +435,7 @@ werden kann. \begin{definition} Eine $m\times n$-Matrix $A\in M_{m\times n}(\Bbbk)$ und eine $n\times l$-Matrix $B\in M_{n\times l}(\Bbbk)$ haben als Produkt -eine $n\times l$-Matrix $C=AB\in M_{n\times l}(\Bbbk)$ mit den +eine $m\times l$-Matrix $C=AB\in M_{m\times l}(\Bbbk)$ mit den Koeffizienten \begin{equation} c_{ij} = \sum_{k=1}^n a_{ik} b_{kj}. @@ -483,7 +483,7 @@ I 1 &0 &\dots &0 \\ 0 &1 &\dots &0 \\[-2pt] \vdots&\vdots&\ddots&\vdots\\ -0 &0 &\dots &1 +0 &0 &\dots &1 \end{pmatrix}. \] @@ -521,10 +521,10 @@ Ein Gleichungssystem mit $0$ auf der rechten Seite ist also bereits ausreichend um zu entscheiden, ob die Lösung eindeutig ist. Ein Gleichungssystem mit rechter Seite $0$ heisst {\em homogen}. \index{homogenes Gleichungssystem}% -Zu jedem {\em inhomogenen} Gleichungssystem $Ax=b$ mit $b\ne 0$ +Zu jedem {\em inhomogenen} Gleichungssystem $Ax=b$ mit $b\ne 0$ ist $Ax=0$ das zugehörige homogene Gleichungssystem. -Ein homogenes Gleichungssytem $Ax=0$ hat immer mindestens die +Ein homogenes Gleichungssytem $Ax=0$ hat immer mindestens die Lösung $x=0$, man nennt sie auch die {\em triviale} Lösung. Eine Lösung $x\ne 0$ heisst auch eine nichttriviale Lösung. Die Lösungen eines inhomgenen Gleichungssystem $Ax=b$ ist also nur dann @@ -535,7 +535,7 @@ Lösung hat. Der Gauss-Algorithmus oder genauer Gausssche Eliminations-Algorithmus löst ein lineare Gleichungssystem der Form~\eqref{buch:vektoren-und-matrizen:eqn:vektorform}. -Die Koeffizienten werden dazu in das Tableau +Die Koeffizienten werden dazu in das Tableau \[ \begin{tabular}{|>{$}c<{$}>{$}c<{$}>{$}c<{$}|>{$}c<{$}|} \hline @@ -552,7 +552,7 @@ Der Algorithmus is so gestaltet, dass er nicht mehr Speicher als das Tableau benötigt, alle Schritte operieren direkt auf den Daten des Tableaus. -In jedem Schritt des Algorithmus wird zunächst eine Zeile $i$ und +In jedem Schritt des Algorithmus wird zunächst eine Zeile $i$ und Spalte $j$ ausgewählt, das Elemente $a_{ij}$ heisst das Pivotelement. \index{Pivotelement}% Die {\em Pivotdivision} @@ -646,7 +646,7 @@ In der Phase der {\em Vorwärtsreduktion} werden Pivotelemente von links nach rechts möglichst auf der Diagonale gewählt und mit Zeilensubtraktionen die darunterliegenden Spalten freigeräumt. \index{Vorwärtsreduktion}% -Während des Rückwärtseinsetzens werden die gleichen Pivotelemente von +Während des Rückwärtseinsetzens werden die gleichen Pivotelemente von rechts nach links genutzt, um mit Zeilensubtraktionen auch die Spalten über den Pivotelemnten frei zu räumen. \index{Rückwärtseinsetzen}% @@ -800,7 +800,7 @@ $x = b_1c_1+b_2c_2+\dots+b_nc_n$ konstruieren. Tatsächlich gilt \begin{align*} Ax -&= +&= A( b_1c_1+b_2c_2+\dots+b_nc_n) \\ &= @@ -851,10 +851,10 @@ für eine Gleichungssystem mit quadratischer Koeffizientenmatrix $A$ heisst die Determinante $\det(A)$ der Matrix $A$. \end{definition} -Aus den Regeln für die Durchführung des Gauss-Algorithmus kann man die +Aus den Regeln für die Durchführung des Gauss-Algorithmus kann man die folgenden Regeln für die Determinante ableiten. Wir stellen die Eigenschaften hier nur zusammen, detaillierte Herleitungen -kann man in jedem Kurs zur linearen Algebra finden, zum Beispiel im +kann man in jedem Kurs zur linearen Algebra finden, zum Beispiel im Kapitel~2 des Skripts \cite{buch:linalg}. \begin{enumerate} \item @@ -877,11 +877,11 @@ wird auch der Wert der Determinanten mit $\lambda$ multipliziert. \item \label{buch:linear:determinante:asymetrisch} Die Determinante ist eine lineare Funktion der Zeilen von $A$. -Zusammen mit der Eigeschaft~\ref{buch:linear:determinante:vorzeichen} +Zusammen mit der Eigeschaft~\ref{buch:linear:determinante:vorzeichen} folgt, dass die Determinante eine antisymmetrische lineare Funktion der Zeilen ist. \item -Die Determinante ist durch die Eigenschaften +Die Determinante ist durch die Eigenschaften \ref{buch:linear:determinante:einheitsmatrix} und \ref{buch:linear:determinante:asymetrisch} @@ -895,7 +895,7 @@ Die Determinante der $n\times n$-Matrix $A$ kann mit der Formel = \sum_{i=1}^n (-1)^{i+j} a_{ij} \cdot \det(A_{ij}) \end{equation} -wobei die $(n-1)\times(n-1)$-Matrix $A_{ij}$ die Matrix $A$ ist, aus der +wobei die $(n-1)\times(n-1)$-Matrix $A_{ij}$ die Matrix $A$ ist, aus der man Zeile $i$ und Spalte $j$ entfernt hat. $A_{ij}$ heisst ein {\em Minor} der Matrix $A$. \index{Minor einer Matrix}% @@ -949,7 +949,7 @@ der rechten Seiten ersetzt worden ist. \end{satz} Die Cramersche Formel ist besonders nützlich, wenn die Abhängigkeit -einer Lösungsvariablen von den Einträgen der Koeffizientenmatrix +einer Lösungsvariablen von den Einträgen der Koeffizientenmatrix untersucht werden soll. Für die Details der Herleitung sei wieder auf \cite{buch:linalg} verwiesen. @@ -993,7 +993,7 @@ heisst die {\em Adjunkte} $\operatorname{adj}A$ von $A$. \end{satz} Der Satz~\ref{buch:linalg:inverse:adjoint} liefert eine algebraische -Formel für die Elemente der inversen Matrix. +Formel für die Elemente der inversen Matrix. Für kleine Matrizen wie im nachfolgenden Beispiel ist die Formel~\eqref{buch:linalg:inverse:formel} oft einfachter anzuwenden. Besonders einfach wird die Formel für eine $2\times 2$-Matrix, @@ -1035,7 +1035,7 @@ Die Adjunkte ist \begin{pmatrix*}[r] \det A_{11} & -\det A_{21} & \det A_{31} \\ -\det A_{12} & \det A_{22} & -\det A_{32} \\ - \det A_{13} & -\det A_{23} & \det A_{33} + \det A_{13} & -\det A_{23} & \det A_{33} \end{pmatrix*} \intertext{und damit ist die inverse Matrix} A^{-1} @@ -1084,10 +1084,19 @@ A^{-1} \end{pmatrix}. \label{buch:vektoren-und-matrizen:abeispiel:eqn2} \end{equation} -für die Inverse einer Matrix der Form +für die Inverse einer Matrix der Form \eqref{buch:vektoren-und-matrizen:abeispiel:eqn1}. \end{beispiel} +\subsubsection{Produktregel für die Determinante} +Aus der Charakterisierung der Determinanten kann man auch ableiten, +dass die Produktregel +\[ +\det (AB) = \det(A) \cdot \det(B) +\] +gilt. +Daraus folgt auch, dass $\det(A^{-1})=\det(A)^{-1}$. + % % Lineare Abbildungen % @@ -1109,7 +1118,7 @@ Eine Abbildung $f\colon V\to U$ zwischen Vektorräumen $V$ und $U$ heisst linear, wenn \[ \begin{aligned} -f(v+w) &= f(v) + f(w)&&\forall v,w\in V +f(v+w) &= f(v) + f(w)&&\forall v,w\in V \\ f(\lambda v) &= \lambda f(v) &&\forall v\in V,\lambda \in \Bbbk \end{aligned} @@ -1120,16 +1129,16 @@ gilt. Lineare Abbildungen sind in der Mathematik sehr verbreitet. \begin{beispiel} -Sie $V=C^1([a,b])$ die Menge der stetig differenzierbaren Funktionen +Sie $V=C^1([a,b])$ die Menge der stetig differenzierbaren Funktionen auf dem Intervall $[a,b]$ und $U=C([a,b])$ die Menge der -stetigen Funktion aif $[a,b]$. +stetigen Funktion aif $[a,b]$. Die Ableitung $\frac{d}{dx}$ macht aus einer Funktion $f(x)$ die Ableitung $f'(x)$. -Die Rechenregeln für die Ableitung stellen sicher, dass +Die Rechenregeln für die Ableitung stellen sicher, dass \[ \frac{d}{dx} \colon -C^1([a,b]) \to C([a,b]) +C^1([a,b]) \to C([a,b]) : f \mapsto f' \] @@ -1148,7 +1157,7 @@ eine lineare Abbildung. \end{beispiel} \subsubsection{Matrix} -Um mit linearen Abbildungen rechnen zu können, ist eine Darstellung +Um mit linearen Abbildungen rechnen zu können, ist eine Darstellung mit Hilfe von Matrizen nötig. Sei also $\mathcal{B}=\{b_1,\dots,b_n\}$ eine Basis von $V$ und $\mathcal{C} = \{ c_1,\dots,c_m\}$ eine Basis von $U$. @@ -1156,12 +1165,12 @@ Das Bild des Basisvektors $b_i$ kann als Linearkombination der Vektoren $c_1,\dots,c_m$ dargestellt werden. Wir verwenden die Bezeichnung \[ -f(b_i) +f(b_i) = a_{1i} c_1 + \dots + a_{mi} c_m. \] Die lineare Abbildung $f$ bildet den Vektor $x$ mit Koordinaten -$x_1,\dots,x_n$ ab auf +$x_1,\dots,x_n$ ab auf \begin{align*} f(x) &= @@ -1184,7 +1193,7 @@ x_n(a_{1n} c_1 + \dots + a_{mn} c_m) + ( a_{m1} x_1 + \dots + a_{mn} x_n ) c_m \end{align*} -Die Koordinaten von $f(x)$ in der Basis $\mathcal{C}$ in $U$ sind +Die Koordinaten von $f(x)$ in der Basis $\mathcal{C}$ in $U$ sind also gegeben durch das Matrizenprodukt $Ax$, wenn $x$ der Spaltenvektor aus den Koordinaten in der Basis $\mathcal{B}$ in $V$ ist. @@ -1222,7 +1231,7 @@ b_{m1}x_1&+& \dots &+&b_{mn}x_n&=&b_{m1}'x_1'&+& \dots &+&b_{mn}'x_n' \end{linsys} \] Dieses Gleichungssystem kann man mit Hilfe eines Gauss-Tableaus lösen. -Wir schreiben die zugehörigen Variablen +Wir schreiben die zugehörigen Variablen \[ \renewcommand{\arraystretch}{1.1} \begin{tabular}{|>{$}c<{$} >{$}c<{$} >{$}c<{$}|>{$}c<{$}>{$}c<{$}>{$}c<{$}|} @@ -1268,7 +1277,7 @@ Für zwei Vektoren $u$ und $w$ in $U$ gibt es daher Vektoren $a=g(u)$ und $b=g(w)$ in $V$ derart, dass $f(a)=u$ und $f(b)=w$. Weil $f$ linear ist, folgt daraus $f(a+b)=u+w$ und $f(\lambda a)=\lambda a$ für jedes $\lambda\in\Bbbk$. -Damit kann man jetzt +Damit kann man jetzt \begin{align*} g(u+w)&=g(f(a)+f(b)) = g(f(a+b)) = a+b = g(u)+g(w) \\ @@ -1306,7 +1315,7 @@ Der Kern oder Nullraum der Matrix $A$ ist die Menge \] \end{definition} -Der Kern ist ein Unterraum, denn für zwei Vektoren $u,w\in \ker f$ +Der Kern ist ein Unterraum, denn für zwei Vektoren $u,w\in \ker f$ \[ \begin{aligned} f(u+v)&=f(u) + f(v) = 0+0 = 0 &&\Rightarrow& u+v&\in\ker f\\ @@ -1322,7 +1331,7 @@ Wir definieren daher das Bild einer linearen Abbildung oder Matrix. \begin{definition} Ist $f\colon V\to U$ eine lineare Abbildung dann ist das Bild von $f$ -der Unterraum +der Unterraum \[ \operatorname{im}f = \{ f(v)\;|\;v\in V\} \subset U \] @@ -1366,7 +1375,7 @@ $\operatorname{def}A=\dim\ker A$. \end{definition} Da der Kern mit Hilfe des Gauss-Algorithmus bestimmt werden kann, -können Rang und Defekt aus dem Schlusstableau +können Rang und Defekt aus dem Schlusstableau eines homogenen Gleichungssystems mit $A$ als Koeffizientenmatrix abgelesen werden. @@ -1382,8 +1391,3 @@ n-\operatorname{def}A. \subsubsection{Quotient} TODO: $\operatorname{im} A \simeq \Bbbk^m/\ker A$ - - - - - diff --git a/buch/chapters/50-permutationen/determinante.tex b/buch/chapters/50-permutationen/determinante.tex index c440caf..805235d 100644 --- a/buch/chapters/50-permutationen/determinante.tex +++ b/buch/chapters/50-permutationen/determinante.tex @@ -7,3 +7,105 @@ \section{Determinante \label{buch:section:determinante}} \rhead{Determinante} +Das Signum einer Permutationsmatrizen lässt sich +gemäss~\eqref{buch:permutationen:determinante} +mit der Determinanten berechnen. +Umgekehrt sollte es auch möglich sein, eine Formel +für die Determinante zu finden. +Die Basis dafür ist der +Entwicklungssatz +\begin{equation} +\det(A) += +\sum_{i=1}^n (-1)^{i+j} a_{ij} \cdot \det(A_{ij}) +\label{buch:permutationen:entwicklungssatz} +\end{equation} +von Laplace für die Determinante. +In den Produkten $a_{ij}\cdot\det(A_{ij})$ enthält +die Untermatrix $A_{ij}$ weder Elemente der Zeile $i$ noch der +Zeile $j$. +Die Summanden auf der rechten Seite von +\eqref{buch:permutationen:entwicklungssatz} +sind daher Produkte der Form +\[ +a_{1i_1} +a_{2i_2} +a_{3i_3} +\dots +a_{ni_n}, +\] +in denen nur Faktoren aus verschiedenen Spalten der Matrix $A$ +vorkommen. +Das ist gleichbedeutend damit, dass unter den Spaltenindizes +$i_1,i_2,i_3,\dots,i_n$ keine zwei gleich sind, dass also +\[ +\sigma += +\begin{pmatrix} +1&2&3&\dots&n\\ +i_1&i_2&i_3&\dots&i_n +\end{pmatrix} +\] +eine Permutation ist. + +Die Determinante muss sich daher als Summe über alle Permutationen +in der Form +\begin{equation} +\det(A) += +\sum_{\sigma\in S_n} +c(\sigma) +a_{1\sigma(1)} +a_{2\sigma(2)} +\dots +a_{n\sigma(n)} +\label{buch:permutationen:cformel} +\end{equation} +schreiben lassen, wobei die Koeffizienten $c(\sigma)$ noch zu bestimmen +sind. +Setzt man in +\eqref{buch:permutationen:cformel} +eine Permutationsmatrix $P_\tau$ ein, dann verschwinden alle +Terme auf der rechten Seite ausser dem zur Permutation $\tau$, +also +\[ +\det(P_\tau) += +\sum_{\sigma \in S_n} +c(\sigma) +(P_\tau)_{1\sigma(1)} +(P_\tau)_{2\sigma(2)} +\dots +(P_\tau)_{n\sigma(n)} += +c(\tau) +1\cdot 1\cdot\dots\cdot 1 += +c(\tau). +\] +Der Koeffizientn $c(\tau)$ ist also genau das Vorzeichen +der Permutation $\tau$. +Damit erhalten wir den folgenden Satz: + +\begin{satz} +Die Determinante einer $n\times n$-Matrix $A$ kann berechnet werden als +\[ +\det(A) += +\sum_{\sigma\in S_n} +\operatorname{sgn}(\sigma) +a_{1\sigma(1)} +a_{2\sigma(2)} +\dots +a_{n\sigma(n)} += +\sum_{\tau\in S_n} +\operatorname{sgn}(\tau) +a_{\tau(1)1} +a_{\tau(2)2} +\dots +a_{\tau(n)n}. +\] +Insbesondere folgt auch $\det(A)=\det(A^t)$. +\end{satz} + diff --git a/buch/chapters/50-permutationen/matrizen.tex b/buch/chapters/50-permutationen/matrizen.tex index 7e55364..f7e9e31 100644 --- a/buch/chapters/50-permutationen/matrizen.tex +++ b/buch/chapters/50-permutationen/matrizen.tex @@ -181,7 +181,7 @@ Die Determinante einer solchen Permutationsmatrix ist Nach der Produktregel für die Determinante folgt für eine Darstellung der Permutation $\sigma=\tau_1\dots\tau_l$ als Produkt von Transpositionen, dass -\[ +\begin{equation} \det P_{\sigma} = \det P_{\tau_1} \dots \det P_{\tau_l} @@ -189,7 +189,8 @@ dass (-1)^l = \operatorname{sgn}(\sigma). -\] +\label{buch:permutationen:determinante} +\end{equation} Das Vorzeichen einer Permutation ist also identisch mit der Determinante der zugehörigen Permutationsmatrix. diff --git a/buch/chapters/60-gruppen/symmetrien.tex b/buch/chapters/60-gruppen/symmetrien.tex index 7364c85..aee3b41 100644 --- a/buch/chapters/60-gruppen/symmetrien.tex +++ b/buch/chapters/60-gruppen/symmetrien.tex @@ -714,8 +714,8 @@ Kurve so zu definieren, dass dabei Längen und Winkel erhalten bleiben. Dieser Ansatz ist die Basis der Theorie der Krümmung sogenannter Riemannscher Mannigfaltigkeiten. -\subsection{Der Satz von Noether -\label{buch:subsection:noether}} +%\subsection{Der Satz von Noether +%\label{buch:subsection:noether}} diff --git a/buch/chapters/70-graphen/wavelets.tex b/buch/chapters/70-graphen/wavelets.tex index ef1520e..8baa88c 100644 --- a/buch/chapters/70-graphen/wavelets.tex +++ b/buch/chapters/70-graphen/wavelets.tex @@ -10,7 +10,7 @@ In Abschnitt~\ref{buch:subsection:standardbasis-und-eigenbasis} wurde gezeigt dass die Standardbasis den Zusammenhang zwischen den einzelnen Teilen des Graphen völlig ignoriert, während die Eigenbasis Wellen beschreibt, die mit vergleichbarer Amplitude sich über den ganzen -Graphen entsprechen. +Graphen erstrecken. Die Eigenbasis unterdrückt also die ``Individualität'' der einzelnen Knoten fast vollständig. diff --git a/buch/chapters/90-crypto/Makefile.inc b/buch/chapters/90-crypto/Makefile.inc index 9543ce1..508add5 100644 --- a/buch/chapters/90-crypto/Makefile.inc +++ b/buch/chapters/90-crypto/Makefile.inc @@ -8,5 +8,4 @@ CHAPTERFILES = $(CHAPTERFILES) \ chapters/90-crypto/arith.tex \ chapters/90-crypto/ff.tex \ chapters/90-crypto/aes.tex \ - chapters/90-crypto/rs.tex \ chapters/90-crypto/chapter.tex diff --git a/buch/chapters/90-crypto/arith.tex b/buch/chapters/90-crypto/arith.tex index dcc31b8..b05110f 100644 --- a/buch/chapters/90-crypto/arith.tex +++ b/buch/chapters/90-crypto/arith.tex @@ -91,6 +91,7 @@ Die Berechnung der Quadratwurzel lässt sich in Hardware effizient implementieren. \begin{algorithmus} +\label{buch:crypto:teile-und-hersche} Der folgende Algorithmus berechnet $a^k$ in $O(\log_2(k))$ Multiplikationen \begin{enumerate} diff --git a/buch/chapters/90-crypto/ff.tex b/buch/chapters/90-crypto/ff.tex index 535b359..a1cb747 100644 --- a/buch/chapters/90-crypto/ff.tex +++ b/buch/chapters/90-crypto/ff.tex @@ -7,6 +7,15 @@ \section{Kryptographie und endliche Körper \label{buch:section:kryptographie-und-endliche-koerper}} \rhead{Kryptographie und endliche Körper} +In diesem Abschnitt soll illustriert werden, wie die Arithmetik in +endlichen Körpern Algorithmen zu konstruieren erlaubt, mit denen sich +zum Beispiel sehr effizient kryptographische Schlüssel aushandeln +lassen. +Der klassische Diffie-Hellmann-Algorithmus in einem Galois-Körper +$\mathbb{F}_p$ wird in Abschnitt~\ref{buch:subsection:elliptische-kurven} +verallgemeinert auf eine sogenannte elliptische Kurve. +Diese Version des Algorithmus ist sehr effizient was die Bitlänge der +Schlüssel betrifft. \subsection{Potenzen in $\mathbb{F}_p$ und diskreter Logarithmus \label{buch:subsection:potenzen-diskreter-logarithmus}} @@ -439,6 +448,7 @@ Das Polynom ist \[ p(t) = +XXX \] Nach Division durch $t(t-1)$ erhält man als den Quotienten \begin{align*} @@ -652,13 +662,44 @@ Diese Operationen machen $E_{a,b}(\mathbb{F}_{p^l})$ zu einer endlichen abelschen Gruppe. \end{satz} -\subsubsection{Beispiele} -% XXX -TODO: elliptische Kurven in IPsec: Oakley Gruppen - \subsubsection{Diffie-Hellman in einer elliptischen Kurve} -% XXX -TODO: $g^x$ in einer elliptischen Kurve +Der klassische Diffie-Hellmann-Schlüsselalgorithmus in einem Körper +$\mathbb{F}_p$ basiert darauf, dass man beliebige Potenzen eines +Elementes berechnen kann, und dass es schwierig ist, diese Operation +umzukehren. +Die Addition in $\mathbb{F}_p$ wird für diesen Algorithmus überhaupt +nicht benötigt. + +In einer elliptischen Kurve gibt es ebenfalls eine Multiplikation, +aus der sich mit dem +Algorithmus~\ref{buch:crypto:teile-und-hersche} eine effizienter +Potenzieralgorithmus konstruieren lässt. + +Die im Internet Key Exchange Protokol +in RFC 2409 +\cite{buch:rfc2409} +definierte Oakley-Gruppe 4 +zum Beispiel verwendet einen Galois-Körper $\mathbb{F}_{2^{185}}$ +mit dem Minimalpolynom $m(x)=x^{185}+x^{69}+1\in \mathbb{F}_2[x]$ +und den Koeffizienten +\begin{align*} +a&=0\\ +b&=x^{12}+x^{11} + x^{10} + x^9 + x^7 + x^6 + x^5 + x^3 +1, +\end{align*} +die die elliptische Kurve definieren. + +Als Elemente $g$ für den Diffie-Hellmann-Algorithmus wird ein Punkt +der elliptischen Kurve verwendet, dessen $X$-Koordinaten durch das +Polynom $g_x = x^4+x^3$ gegeben ist. +Der Standard spezifiziert die $Y$-Koordinate nicht, diese kann aus +den gegebenen Daten abgeleitet werden. +Die entstehende Gruppe hat etwa $4.9040\cdot10^{55}$ Elemente, die +für einen brute-force-Angriff durchprobiert werden müssten. + + + + + diff --git a/buch/chapters/90-crypto/rs.tex b/buch/chapters/90-crypto/rs.tex deleted file mode 100644 index ec8ec8c..0000000 --- a/buch/chapters/90-crypto/rs.tex +++ /dev/null @@ -1,41 +0,0 @@ -% -% rs.tex -- Reed-Solomon-Code -% -% (c) 2020 Prof Dr Andreas Müller, Hochschule Rapperswil -% -\section{Fehlerkorrigierende Codes nach Reed-Solomon -\label{buch:section:reed-solomon}} -\rhead{Fehlerkorrigierende Codes} -Jede Art von Datenübertragung muss sich mit dem Problem der Fehler befassen, -die auf dem Übertragungskanal entstehen können. -Die einfachste Lösung dieses Problem versucht, Fehler zu erkennen und -dann eine erneute Übermittelung zu veranlassen. -Dies ist zum Beispiel bei der Datenübertragung von einer Raumsonde -wie Voyager~1 nicht möglich, die Signallaufzeit von der Sonde und wieder -zurück ist über 40 Stunden. -Es ist auch nicht sinnvoll beim Lesen eines optischen Mediums wie einer -CD oder DVD, wenn ein Fehler durch eine Beschädigung der Oberfläche -des Mediums verursacht wird. -Erneutes Lesen würde das Resultat auch nicht ändern. -Es wird also eine Möglichkeit gesucht, die Daten so zu codieren, dass -ein Fehler nicht nur erkannt sondern auch korrigiert werden kann. - -In diesem Abschnitt werden die algebraisch besonders interessanten -Reed-Solmon-Codes beschrieben. -Ihren ersten Einsatz hatten Sie bei den Voyager-Raumsonden, die 1977 -gestartet wurden. -Sie befinden sich im Moment in einer Entfernung von -Zum ersten mal kommerziell verwendet wurden sie für die optischen -Medien CD und DVD. - -% https://www.youtube.com/watch?v=uOLW43OIZJ0 -% https://www.youtube.com/watch?v=4BfCmZgOKP8 - -\subsection{Was ist ein Code? -\label{buch:subsection:was-ist-ein-code}} - -\subsection{Reed-Solomon-Code -\label{buch:subsection:reed-solomon-code}} - -\subsection{Decodierung -\label{buch:subsection:decodierung}} diff --git a/buch/chapters/95-homologie/Makefile.inc b/buch/chapters/95-homologie/Makefile.inc index 7e6f1e7..41b1569 100644 --- a/buch/chapters/95-homologie/Makefile.inc +++ b/buch/chapters/95-homologie/Makefile.inc @@ -8,7 +8,6 @@ CHAPTERFILES = $(CHAPTERFILES) \ chapters/95-homologie/simplex.tex \ chapters/95-homologie/komplex.tex \ chapters/95-homologie/homologie.tex \ - chapters/95-homologie/mayervietoris.tex \ chapters/95-homologie/fixpunkte.tex \ chapters/95-homologie/chapter.tex diff --git a/buch/chapters/95-homologie/chapter.tex b/buch/chapters/95-homologie/chapter.tex index eaa56c4..994c400 100644 --- a/buch/chapters/95-homologie/chapter.tex +++ b/buch/chapters/95-homologie/chapter.tex @@ -38,7 +38,7 @@ Damit wird es möglich, das Dreieck vom Rand des Dreiecks zu unterschieden. \input{chapters/95-homologie/simplex.tex} \input{chapters/95-homologie/komplex.tex} \input{chapters/95-homologie/homologie.tex} -\input{chapters/95-homologie/mayervietoris.tex} +%\input{chapters/95-homologie/mayervietoris.tex} \input{chapters/95-homologie/fixpunkte.tex} diff --git a/buch/chapters/95-homologie/fixpunkte.tex b/buch/chapters/95-homologie/fixpunkte.tex index 1ed51ef..a03d4b5 100644 --- a/buch/chapters/95-homologie/fixpunkte.tex +++ b/buch/chapters/95-homologie/fixpunkte.tex @@ -11,15 +11,78 @@ selbst gehört die zugehörige lineare Abbildung $f_*\colon H_*(X)\to H_*(X)$ der Homologiegruppen. Diese linearen Abbildungen sind im Allgemeinen viel einfacher zu analysieren. -Zum Beispiel soll in Abschnitt~\ref{buch:subsection:lefshetz} -die Lefshetz-Spurformel abgeleitet werden, die eine Aussagen darüber -ermöglicht, ob eine Abbildung einen Fixpunkt haben kann. -In Abschnitt~\ref{buch:subsection:brower} wird gezeigt wie man damit -den Browerschen Fixpunktsatz beweisen kann, der besagt, dass jede -Abbildung eines Einheitsballs in sich selbst immer einen Fixpunkt hat. - -\subsection{Lefshetz-Spurformel -\label{buch:subsection:lefshetz}} - -\subsection{Brower-Fixpunktsatz -\label{buch:subsection:brower}} +%Zum Beispiel soll in Abschnitt~\ref{buch:subsection:lefshetz} +%die Lefshetz-Spurformel abgeleitet werden, die eine Aussagen darüber +%ermöglicht, ob eine Abbildung einen Fixpunkt haben kann. +%In Abschnitt~\ref{buch:subsection:brower} wird gezeigt wie man damit +%den Browerschen Fixpunktsatz beweisen kann, der besagt, dass jede +%Abbildung eines Einheitsballs in sich selbst immer einen Fixpunkt hat. + +%\subsection{Brower-Fixpunktsatz +%\label{buch:subsection:brower}} +% +%\begin{satz}[Brower] +%\end{satz} + +%\subsection{Lefshetz-Fixpunktsatz +%\label{buch:subsection:lefshetz}} +Eine Selbstabbildung $f_*\colon C_*\to C_*$ von Kettenkomplexen führt auf +eine Selbstabbiludng der Homologiegruppen $H(f)\colon H(C)\to H(C)$. +Da sowohl $H_k$ wie auch $C_k$ endlichdimensionale Vektorräume sind, +ist die Spur von $H_k(f)$ wohldefiniert. + +\begin{definition} +Die {\em Lefshetz-Zahl} einer Abbildung $f$ von Kettenkomplexen ist +\[ +\lambda(f) += +\sum_{k=0}^\infty +(-1)^k \operatorname{Spur}f_k += +\sum_{k=0}^\infty +(-1)^k \operatorname{Spur}(H_k(f)). +\] +\end{definition} + +Die zweite Darstellung der Lefshetz-Zahl auf der rechten Seite ist +meistens viel leichter zu berechnen als die erste. +Die einzelnen Vektorräume eines Kettenkomplexes können haben typischerweise +eine hohe Dimension, so hoch wie die Anzahl der Simplizes der Triangulation. +Die Homologiegruppen dagegen haben typischerweise sehr viel kleinere +Dimension, die Matrizen $H_k(F)$ sind also relativ klein. +Es ist aber nicht klar, dass beide Berechnungsmethoden für die +Lefshetz-Zahl auf das gleiche Resultat führen müssen. + +\begin{proof}[Beweis] +\end{proof} + +Die Lefshetz-Zahl ist eine Invariante einer topologischen Abbildung, +die Aussagen über Fixpunkte zu machen erlaubt. + +\begin{satz} +Ist $f\colon X\to X$ eine Selbstabbildung eines kompakten Polyeders und +ist $\lambda(f) \ne 0$, dann hat $f$ einen Fixpunkt. +\end{satz} + +Im Folgenden soll nur ein heuristisches Argument gegeben werden, warum +ein solcher Satz wahr sein könnte. + +Wenn eine Abbildung keinen Fixpunkt hat, dann ist $f(x) \ne x$ für alle +Punkte von $X$. +Da $X$ kompakt ist, gibt es einen minimalen Abstand $d$ zwischen $f(x)$ und $x$. +Wenn man also für $X$ eine Triangulation wählt, die wesentlich feiner ist +als dieser minimale Abstand, dann wird kein Simplex der Triangulation auf +Punkte im selben Simplex oder in einem Nachbarsimplex abgebildet wird. +Indem man nötigenfalls die Triangulation nochmals verfeinert, kann man auch +genügend Platz schaffen, dass man die Abbildung $f$ etwas modifizieren kann, +so dass auch die deformierte Abbildung immer noch diese Eigenschaft hat. + +Die zugehörige Abbildung des Kettenkomplexes der Triangulation hat damit +die Eigenschaft, dass kein Basisvektor auf sich selbst abgebildet wird. +Die Matrix der Abbildung hat daher keine Nullen auf der Diagonalen, und +damit ist auch die Spur dieser Abbildung Null: $\operatorname{Spur}(H_k(f))=0$ +für alle $k$. +Erst recht ist die Lefshetz-Zahl $\lambda(f)=0$. +Wenn also die Lefshetz-Zahl verschieden ist von Null, dann muss $f$ +notwendigerweise einen Fixpunkt haben. + diff --git a/buch/chapters/95-homologie/homologie.tex b/buch/chapters/95-homologie/homologie.tex index 2b80a17..905ecc3 100644 --- a/buch/chapters/95-homologie/homologie.tex +++ b/buch/chapters/95-homologie/homologie.tex @@ -6,13 +6,349 @@ \section{Homologie \label{buch:section:homologie}} \rhead{Homologie} +Die Idee der Trangulation ermöglicht, komplizierte geometrische +Objekte mit einem einfachen ``Gerüst'' auszustatten und so zu +analysieren. +Projiziert man ein mit einer Kugel konzentrisches Tetraeder auf die +Kugel, entsteht eine Triangulation der Kugeloberfläche. +Statt eine Kugel zu studieren, kann man also auch ein Tetraeder untersuchen. + +Das Gerüst kann natürlich nicht mehr alle Eigenschaften des ursprünglichen +Objektes wiedergeben. +Im Beispiel der Kugel geht die Information darüber, dass es sich um eine +glatte Mannigfaltigkeit handelt, verloren. +Was aber bleibt, sind Eigenschaften des Zusammenhangs. +Wenn sich zwei Punkte mit Wegen verbinden lassen, dann gibt es auch eine +Triangulation mit eindimensionalen Simplices, die diese Punkte als Ecken +enthalten, die sich in der Triangulation mit einer Folge von Kanten +verbinden lassen. +Algebraisch bedeutet dies, dass die beiden Punkte der Rand eines +Weges sind. +Fragen der Verbindbarkeit von Punkten mit Wegen lassen sich also +dadurch studieren, dass man das geometrische Objekt auf einen Graphen +reduziert. + +In diesem Abschnitt soll gezeigt werden, wie diese Idee auf höhere +Dimensionen ausgedehnt werden. +Es soll möglich werden, kompliziertere Fragen des Zusammenhangs, zum +Beispiel das Vorhandensein von Löchern mit algebraischen Mitteln +zu analysieren. \subsection{Homologie eines Kettenkomplexes \label{buch:subsection:homologie-eines-kettenkomplexes}} +Wegzusammenhang lässt sich untersuchen, indem man in der Triangulation +nach Linearkombinationen von Kanten sucht, die als Rand die beiden Punkte +haben. +Zwei Punkte sind also nicht verbindbar und liegen damit in verschiedenen +Komponenten, wenn die beiden Punkte nicht Rand irgend einer +Linearkombination von Kanten sind. +Komponenten können also identifiziert werden, indem man unter allen +Linearkombinationen von Punkten, also $C_0$ all diejenigen ignoriert, +die Rand einer Linearkombinationv on Kanten sind, also $\partial_1C_1$. +Der Quotientenraum $H_0=C_0/\partial_1C_1$ enthält also für jede Komponente +eine Dimension. + +Eine Dimension höher könnten wir danach fragen, ob sich ein geschlossener +Weg zusammenziehen lässt. +In der Triangulation zeichnet sich ein geschlossener Weg dadurch aus, +dass jedes Ende einer Kante auch Anfang einer Folgekante ist, dass also +der Rand der Linearkombination von Kanten 0 ist. +Algebraisch bedeutet dies, dass wir uns für diejenigen Linearkombinationen +$z\in C_1$ interessieren, die keinen Rand haben, für die also $\partial_1z=0$ +gilt. + +\begin{definition} +Die Elemente von +\[ +Z_k += +Z_k^C += +\{z\in C_k\;|\; \partial_k z = 0\} += +\ker \partial_k +\] +heissen die {\em ($k$-dimensionalen) Zyklen} von $C_*$. +\end{definition} + +In einem Dreieck ist der Rand ein geschlossener Weg, der sich zusammenziehen +lässt, indem man ihn durch die Dreiecksfläche deformiert. +Entfernt man aber die Dreiecksfläche, ist diese Deformation nicht mehr +möglich. +Einen zusammenziehbaren Weg kann man sich also als den Rand eines Dreiecks +einer vorstellen. +``Löcher'' sind durch geschlossene Wege erkennbar, die nicht Rand eines +Dreiecks sein können. +Wir müssen also ``Ränder'' ignorieren. + +\begin{definition} +Die Elemente von +\[ +B_k += +B_k^C += +\{\partial_{k+1}z\;|\; C_{k+1}\} += +\operatorname{im} \partial_{k+1} +\] +heissen die {\em ($k$-dimensionalen) Ränder} von $C_*$. +\end{definition} + +Algebraisch ausgedrückt interessieren uns also nur Zyklen, die selbst +keine Ränder sind. +Der Quotientenraum $Z_1/B_1$ ignoriert unter den Zyklen diejenigen, die +Ränder sind, drückt also algebraisch die Idee des eindimensionalen +Zusammenhangs aus. +Wir definieren daher + +\begin{definition} +Die $k$-dimensionale Homologiegruppe des Kettenkomplexes $C_*$ ist +\[ +H_k(C) = Z_k/B_k = \ker \partial_k / \operatorname{im} \partial_{k+1}. +\] +Wenn nur von einem Kettenkomplex die Rede ist, kann auch $H_k(C)=H_k$ +abgekürzt werden. +\end{definition} + +Die folgenden zwei ausführlichen Beispiele sollen zeigen, wie die +Homologiegruppe $H_2$ die Anwesenheit eines Hohlraumes detektieren kann, +der entsteht, wenn man aus einem Tetraeder das innere entfernt. + +\begin{beispiel} +\begin{figure} +\centering +XXX Bild eines Tetraeders mit Bezeichnung der Ecken und Kanten +\caption{Triangulation eines Tetraeders, die Orientierung von Kanten +und Seitenflächen ist immer so gewählt, dass die Nummern der Ecken +aufsteigend sind. +\label{buch:homologie:tetraeder:fig}} +\end{figure} +Ein Tetraeder ist ein zweidmensionales Simplex, wir untersuchen seinen +Kettenkomplex und bestimmen die zugehörigen Homologiegruppen. +Zunächst müssen wir die einzelnen Mengen $C_k$ beschreiben und verwenden +dazu die Bezeichnungen gemäss Abbildung~\ref{buch:homologie:tetraeder:fig}. +$C_0$ ist der vierdimensionale Raum aufgespannt von den vier Ecken +$0$, $1$, $2$ und $3$ des Tetraeders. +$C_1$ ist der sechsdimensionale Vektorraum der Kanten +\[ +k_0 = [0,1],\quad +k_1 = [0,2],\quad +k_2 = [0,3],\quad +k_3 = [1,2],\quad +k_4 = [1,3],\quad +k_5 = [2,3] +\] +Der Randoperator $\partial_1$ hat die Matrix +\[ +\partial_1 += +\begin{pmatrix*}[r] +-1&-1&-1& 0& 0& 0\\ + 1& 0& 0&-1&-1& 0\\ + 0& 1& 0& 1& 0&-1\\ + 0& 0& 1& 0& 1& 1 +\end{pmatrix*}. +\] + +Wir erwarten natürlich, dass sich zwei beliebige Ecken verbinden lassen, +dass es also nur eine Komponente gibt und dass damit $H_1=\Bbbk$ ist. +Dazu beachten wir, dass das Bild von $\partial_1$ genau aus den Vektoren +besteht, deren Komponentensumme $0$ ist. +Das Bild $B_0$ von $\partial_1$ ist daher die Lösungsmenge der einen +Gleichung +\( +x_0+x_1+x_2+x_3=0. +\) +Der Quotientenraum $H_0=Z_0/B_0 = C_0/\operatorname{im}\partial_1$ +ist daher wie erwartet eindimensional. + +Wir bestimmen jetzt die Homologiegruppe $H_1$. +Da sich im Tetraeder jeder geschlossene Weg zusammenziehen lässt, +erwarten wir $H_1=0$. + +Die Menge der Zyklen $Z_1$ wird bestimmt, indem man die Lösungsmenge +des Gleichungssystems $\partial_1z=0$ bestimmt. +Der Gauss-Algorithmus für die Matrix $\partial_1$ liefert das +Schlusstableau +\[ +\begin{tabular}{|>{$}c<{$}>{$}c<{$}>{$}c<{$}>{$}c<{$}>{$}c<{$}>{$}c<{$}|} +\hline +k_0&k_1&k_2&k_3&k_4&k_5\\ +\hline + 1& 0& 0& -1& -1& 0\\ + 0& 1& 0& 1& 0& -1\\ + 0& 0& 1& 0& 1& 1\\ + 0& 0& 0& 0& 0& 0\\ +\hline +\end{tabular} +\] +Daraus lassen sich drei linear unabhängig eindimensionale Zyklen ablesen, +die zu den Lösungsvektoren +\[ +z_1 += +\begin{pmatrix*}[r] +1\\ +-1\\ +0\\ +1\\ +0\\ +0 +\end{pmatrix*}, +\qquad +z_2 += +\begin{pmatrix*}[r] +1\\ +0\\ +-1\\ +0\\ +1\\ +0 +\end{pmatrix*}, +\qquad +z_3 += +\begin{pmatrix*}[r] +0\\ +1\\ +-1\\ +0\\ +0\\ +1 +\end{pmatrix*} +\] +gehören. + +$C_2$ hat die vier Seitenflächen +\[ +f_0=[0,1,2],\quad +f_1=[0,1,3],\quad +f_2=[0,2,3],\quad +f_3=[1,2,3] +\] +als Basis. +Der zweidimensionale Randoperator ist die $6\times 4$-Matrix +\[ +\partial_2 += +\begin{pmatrix*}[r] + 1& 1& 0& 0\\ +-1& 0& 1& 0\\ + 0&-1&-1& 0\\ + 1& 0& 0& 1\\ + 0& 1& 0&-1\\ + 0& 0& 1& 1 +\end{pmatrix*}. +\] +Man kann leicht nachrechnen, dass $\partial_1\partial_2=0$ ist, wie es +für einen Kettenkomplex sein muss. + +Um nachzurechnen, dass die Homologiegruppe $H_1=0$ ist, müssen wir jetzt +nachprüfen, ob jeder Zyklus in $Z_1$ auch Bild der Randabbildung $\partial_2$ +ist. +Die ersten drei Spalten von $\partial_2$ sind genau die drei Zyklen +$z_1$, $z_2$ und $z_3$. +Insbesondere lassen sich alle Zyklen als Ränder darstellen, die +Homologiegruppe $H_1=0$ verschwindet. + +Die Zyklen in $C_2$ sind die Lösungen von $\partial_2z=0$. +Der Gauss-Algorithmus für $\partial_2$ liefert das -Tableau +\[ +\begin{tabular}{|>{$}c<{$}>{$}c<{$}>{$}c<{$}>{$}c<{$}|} +\hline +f_0&f_1&f_2&f_3\\ +\hline +1&0&0& 1\\ +0&1&0&-1\\ +0&0&1& 1\\ +0&0&0& 0\\ +0&0&0& 0\\ +0&0&0& 0\\ +\hline +\end{tabular} +\] +Daraus liest man ab, dass es genau einen Zyklus nämlich +\[ +z += +\begin{pmatrix} +-1\\1\\-1\\1 +\end{pmatrix} +\] +$Z_2$ besteht also aus Vielfachen des Vektors $z$. + +Da es nur ein zweidimensionales Simplex gibt, ist $C_3$ eindimensional. +Die Randabbildung $\partial_3$ hat die Matrix +\[ +\partial_3 += +\begin{pmatrix} +1\\ +-1\\ +1\\ +-1 +\end{pmatrix}. +\] +Die Zyklen $Z_2$ und die Ränder $B_2$ bilden also dieselbe Menge, auch +die Homologie-Gruppe $H_2$ ist $0$. + +Da es keine vierdimensionalen Simplizes gibt, ist $B_3=0$. +Die Zyklen $Z_3$ bestehen aus den Lösungen von $\partial_3w=0$, da +aber $\partial_3$ injektiv ist, ist $Z_3=0$. +Daher ist auch $H_3=0$. +\end{beispiel} + +\begin{beispiel} +Für dieses Beispiel entfernen wir das Innere des Tetraeders, es entsteht +ein Hohlraum. +Am Kettenkomplex der Triangulation ändert sich nur, dass $C_3$ jetzt +nur noch den $0$-Vektor enthält. +Das Bild $B_2=\operatorname{im}\partial_3$ wird damit auch $0$-dimensional, +während es im vorigen Beispiel eindimensional war. +Die einzige Änderung ist also in der Homologiegruppe +$H_2 = Z_2/B_2 = Z_2 / \{0\} \simeq \Bbbk$. +Die Homologiegruppe $H_2$ hat jetzt Dimension $1$ und zeigt damit den +Hohlraum an. +\end{beispiel} \subsection{Induzierte Abbildung \label{buch:subsection:induzierte-abbildung}} +Früher haben wurde eine Abbildung $f_*$ zwischen Kettenkomplexen $C_*$ und +$D_*$ so definiert, +dass sie mit den Randoperatoren verträglich sein muss. +Diese Forderung bewirkt, dass sich auch eine lineare Abbildung +\[ +H_k(f) \colon H_k(C) \to H_k(D) +\] +zwischen den Homologiegruppen ergibt, wie wir nun zeigen wollen. + +Um eine Abbildung von $H_k(C)$ nach $H_k(D)$ zu definieren, müssen wir +zu einem Element von $H_k(C)$ ein Bildelement konstruieren. +Ein Element in $H_k(C)$ ist eine Menge von Zyklen in $Z^C_k$, die sich +nur um einen Rand in $B_k$ unterscheiden. +Wir wählen also einen Zyklus $z\in Z_k$ und bilden ihn auf $f_k(z)$ ab. +Wegen $\partial^D_kf(z)=f\partial^C_kz = f(0) =0 $ ist auch $f_k(z)$ +ein Zyklus. +Wir müssen jetzt aber noch zeigen, dass eine andere Wahl des Zyklus +das gleiche Element in $H_k(D)$ ergibt. +Dazu genügt es zu sehen, dass sich $f(z)$ höchstens um einen Rand +ändert, wenn man $z$ um einen Rand ändert. +Sei also $b\in B^C_k$ ein Rand, es gibt also ein $w\in C_{k+1}$ mit +$\partial^C_{k+1}w=b$. +Dann gilt aber auch +\[ +f_k(z+b) += +f_k(z) + f_k(b) += +f_k(z) + f_k(\partial^C_{k+1}w) += +f_k(z) + \partial^D_{k+1}(f_k(w)). +\] +Der letzte Term ist ein Rand in $D_k$, somit ändert sich $f_k(z)$ nur +um diesen Rand, wenn man $z$ um einen Rand ändert. +$f_k(z)$ und $f_k(z+b)$ führen auf die selbe Homologieklasse. -\subsection{Homologie eines simplizialen Komplexes -\label{buch:subsection:simplizialekomplexe}} diff --git a/buch/chapters/95-homologie/images/Makefile b/buch/chapters/95-homologie/images/Makefile index 82f1285..ac964ff 100644 --- a/buch/chapters/95-homologie/images/Makefile +++ b/buch/chapters/95-homologie/images/Makefile @@ -3,8 +3,11 @@ # # (c) 2021 Prof Dr Andreas Müller, OST Ostschweizer Fachhochschule # -all: dreieck.pdf +all: dreieck.pdf polyeder.pdf dreieck.pdf: dreieck.tex pdflatex dreieck.tex +polyeder.pdf: polyeder.tex + pdflatex polyeder.tex + diff --git a/buch/chapters/95-homologie/images/polyeder.pdf b/buch/chapters/95-homologie/images/polyeder.pdf Binary files differnew file mode 100644 index 0000000..3a8ba60 --- /dev/null +++ b/buch/chapters/95-homologie/images/polyeder.pdf diff --git a/buch/chapters/95-homologie/images/polyeder.tex b/buch/chapters/95-homologie/images/polyeder.tex new file mode 100644 index 0000000..9a900cc --- /dev/null +++ b/buch/chapters/95-homologie/images/polyeder.tex @@ -0,0 +1,109 @@ +% +% tikztemplate.tex -- template for standalon tikz images +% +% (c) 2021 Prof Dr Andreas Müller, OST Ostschweizer Fachhochschule +% +\documentclass[tikz]{standalone} +\usepackage{amsmath} +\usepackage{times} +\usepackage{txfonts} +\usepackage{pgfplots} +\usepackage{csvsimple} +\usetikzlibrary{arrows,intersections,math,calc} +\begin{document} +\def\skala{1} +\begin{tikzpicture}[>=latex,thick,scale=\skala] + +% add image content here +\begin{scope}[xshift=-3.5cm,scale=0.5] +\coordinate (A) at (0,0); +\coordinate (B) at (4,0); +\coordinate (C) at (5,-2); +\coordinate (D) at (8,-1); +\coordinate (E) at (7,1); +\coordinate (F) at (7,3); +\coordinate (G) at (1,3); +\coordinate (H) at (5,4); +\coordinate (I) at (9,5); +\coordinate (J) at (4,7); +\coordinate (K) at (-1,9); +\coordinate (L) at (7,11); +\coordinate (M) at (6,-0.5); + +\fill[color=gray,opacity=0.5] (A)--(B)--(H)--(G)--cycle; +\fill[color=gray,opacity=0.5] (G)--(I)--(K)--cycle; +\fill[color=gray,opacity=0.5] (G)--(L)--(K)--cycle; + +\draw (K)--(G)--(A)--(B)--(D); +\draw (C)--(E); +\draw (G)--(I)--(K); +\draw (G)--(L)--(K); +\draw (B)--(H); +\draw (B)--(F); + +\fill (A) circle[radius=0.1]; +\fill (B) circle[radius=0.1]; +\fill (C) circle[radius=0.1]; +\fill (D) circle[radius=0.1]; +\fill (E) circle[radius=0.1]; +\fill (F) circle[radius=0.1]; +\fill (G) circle[radius=0.1]; +\fill (H) circle[radius=0.1]; +\fill (I) circle[radius=0.1]; +%\fill (J) circle[radius=0.1]; +\fill (K) circle[radius=0.1]; +\fill (L) circle[radius=0.1]; +%\fill (M) circle[radius=0.1]; + +\draw[color=red] (H) circle[radius=0.5]; +\draw[color=red] (J) circle[radius=0.5]; +\draw[color=red] (M) circle[radius=0.5]; +\draw[color=red] ($0.25*(A)+0.25*(B)+0.25*(G)+0.25*(H)$) circle[radius=0.5]; + +\end{scope} + +\begin{scope}[xshift=3.5cm,scale=0.5] +\coordinate (A) at (0,0); +\coordinate (B) at (4,0); +\coordinate (C) at (5,-2); +\coordinate (D) at (8,-1); +\coordinate (E) at (7,1); +\coordinate (F) at (7,3); +\coordinate (G) at (1,3); +\coordinate (H) at (5,4); +\coordinate (I) at (9,5); +\coordinate (J) at (4,7); +\coordinate (K) at (-1,9); +\coordinate (L) at (7,11); +\coordinate (M) at (6,-0.5); + +\fill[color=gray!50] (A)--(B)--(H)--(I)--(J)--(L)--(K)--(G)--cycle; + +\draw (K)--(G)--(A)--(B)--(D); +\draw (C)--(E); +\draw (G)--(I)--(K); +\draw (G)--(L)--(K); +\draw (B)--(H); +\draw (B)--(F); +\draw (H)--(J); +\draw (A)--(H); + +\fill (A) circle[radius=0.1]; +\fill (B) circle[radius=0.1]; +\fill (C) circle[radius=0.1]; +\fill (D) circle[radius=0.1]; +\fill (E) circle[radius=0.1]; +\fill (F) circle[radius=0.1]; +\fill (G) circle[radius=0.1]; +\fill (H) circle[radius=0.1]; +\fill (I) circle[radius=0.1]; +\fill (J) circle[radius=0.1]; +\fill (K) circle[radius=0.1]; +\fill (L) circle[radius=0.1]; +\fill (M) circle[radius=0.1]; + +\end{scope} + +\end{tikzpicture} +\end{document} + diff --git a/buch/chapters/95-homologie/komplex.tex b/buch/chapters/95-homologie/komplex.tex index 6dd8efb..fa2d8e1 100644 --- a/buch/chapters/95-homologie/komplex.tex +++ b/buch/chapters/95-homologie/komplex.tex @@ -6,9 +6,105 @@ \section{Kettenkomplexe \label{buch:section:komplex}} \rhead{Kettenkomplexe} +Die algebraische Struktur, die in Abschnitt~\ref{buch:subsection:triangulation} +konstruiert wurde, kann noch etwas abstrakter konstruiert werden. +Es ergibt sich das Konzept eines Kettenkomplexes. +Die Triangulation gibt also Anlass zu einem Kettenkomplex. +So lässt sich zu einem geometrischen Objekt ein algebraisches +Vergleichsobjekt konstruieren. +Im Idealfall lassens ich anschliessend geometrische Eigenschaften mit +algebraischen Rechnungen zum Beispiel in Vektorräumen mit Matrizen +beantworten. -\subsection{Randoperator von Simplexen -\label{buch:subsection:randoperator-von-simplexen}} +\subsection{Definition +\label{buch:subsection:kettenkomplex-definition}} +Die Operation $\partial$, die für Simplizes konstruiert worden ist, +war linear und hat die Eigenschaft $\partial^2$ gehabt. +Diese Eigenschaften reichen bereits für Definition eines Kettenkomplexes. + +\begin{definition} +Eine Folge $C_0,C_1,C_2,\dots$ von Vektorräumen über dem Körper $\Bbbk$ +mit einer Folge von linearen Abbildungen +$\partial_k\colon C_k \to C_{k-1}$, dem {\em Randoperator}, +heisst ein Kettenkomplex, wenn $\partial_{k-1}\partial_k=0$ gilt +für alle $k>0$. +\end{definition} + +Die aus den Triangulationen konstruieren Vektorräme von +Abschnitt~\ref{buch:subsection:triangulation} bilden einen +Kettenkomplex. + +XXX nachrechnen: $\partial^2 = 0$ ? + +\subsection{Abbildungen +\label{buch:subsection:abbildungen}} +Wenn man verschiedene geometrische Objekte mit Hilfe von Triangulationen +vergleichen will, dann muss man auch das Konzept der Abbildungen zwischen +den geometrischen Objekten in die Kettenkomplexe transportieren. + +Eine Abbildung zwischen Kettenkomplexen muss einerseits eine lineare +Abbildung der Vektorräume $C_k$ sein, andererseits muss sich eine +solche Abbildung mit dem Randoperator vertragen. +Wir definieren daher + +\begin{definition} +Eine Abbildung $f_*$ zwischen zwei Kettenkomplexe $(C_*,\partial^C_*)$ und +$(D_*,\partial^D_*)$ heisst eine Abbildung von Kettenkomplexen, wenn +für jedes $k$ +\begin{equation} +\partial^D_k +\circ +f_{k} += +f_{k+1} +\circ +\partial^C_k +\label{buch:komplex:abbildung} +\end{equation} +gilt. +\end{definition} + +Die Beziehung~\eqref{buch:komplex:abbildung} kann übersichtlich als +kommutatives Diagramm dargestellt werden. +\begin{equation} +\begin{tikzcd} +0 + & C_0 \arrow[l, "\partial_0^C"] + \arrow[d, "f_0"] + & C_1 \arrow[l,"\partial_1^C"] + \arrow[d, "f_1"] + & C_2 \arrow[l,"\partial_2^C"] + \arrow[d, "f_2"] + & \dots \arrow[l] + \arrow[l, "\partial_{k-1}^C"] + & C_k + \arrow[l, "\partial_k^C"] + \arrow[d, "f_k"] + & C_{k+1}\arrow[l, "\partial_{k+1}^C"] + \arrow[d, "f_{k+1}"] + & \dots +\\ +0 + & D_0 \arrow[l, "\partial_0^D"] + & D_1 \arrow[l,"\partial_1^D"] + & D_2 \arrow[l,"\partial_2^D"] + & \dots \arrow[l] + \arrow[l, "\partial_{k-1}^D"] + & D_k + \arrow[l, "\partial_k^D"] + & D_{k+1}\arrow[l, "\partial_{k+1}^D"] + & \dots +\end{tikzcd} +\label{buch:komplex:abbcd} +\end{equation} +Die Relation~\eqref{buch:komplex:abbildung} drückt aus, dass man jeden +den Pfeilen im Diagram~\eqref{buch:komplex:abbcd} folgen kann und +dabei zwischen zwei Vektorräumen unabhängig vom Weg die gleiche Abbildung +resultiert. + +Die Verfeinerung einer Triangulation erzeugt eine solche Abbildung von +Komplexen. + + +% XXX simpliziale Approximation -\subsection{Kettenkomplexe und Morphismen -\label{buch:subsection:kettenkomplex}} diff --git a/buch/chapters/95-homologie/mayervietoris.tex b/buch/chapters/95-homologie/mayervietoris.tex deleted file mode 100644 index 57105f8..0000000 --- a/buch/chapters/95-homologie/mayervietoris.tex +++ /dev/null @@ -1,28 +0,0 @@ -% -% mayervietoris.tex -% -% (c) 2021 Prof Dr Andreas Müller, OST Ostschweizer Fachhochschule -% -\section{Exaktheit und die Mayer-Vietoris-Folge -\label{buch:section:mayervietoris}} -\rhead{Exaktheit und die Mayer-Vietoris-Folge} -Die Berechnung der Homologie-Gruppen ist zwar im Wesentlichen ein -kombinatorisches Problem, trotzdem ist eher aufwändig. -Oft weiss man, wie sich toplogische Räume aus einfacheren Räumen -zusammensetzen lassen. -Eine Mannigkfaltigkeit zum Beispiel wird durch die Karten -definiert, also zusammenziehbare Teilmengen von $\mathbb{R}^n$, -die die Mannigkfaltigkeit überdecken. -Das Ziel dieses Abschnittes ist, Regeln zusammenzustellen, mit denen -man die Homologie eines solchen zusammengesetzten Raumes aus der -Homologie der einzelnen Teile und aus den ``Verklebungsabbildungen'', -die die Teile verbinden, zu berechnen. - -\subsection{Kurze exakte Folgen von Kettenkomplexen -\label{buch:subsection:exaktefolgen}} - -\subsection{Schlangenlemma und lange exakte Folgen -\label{buch:subsection:schlangenlemma}} - -\subsection{Mayer-Vietoris-Folge -\label{buch:subsection:mayervietoris}} diff --git a/buch/chapters/95-homologie/simplex.tex b/buch/chapters/95-homologie/simplex.tex index 5ca2ca8..0cf4aa7 100644 --- a/buch/chapters/95-homologie/simplex.tex +++ b/buch/chapters/95-homologie/simplex.tex @@ -1,17 +1,17 @@ % -% simplex.tex -- simplizes und simpliziale Komplexe +% simplex.tex -- simplizes und Polyeder % % (c) 2021 Prof Dr Andreas Müller, OST Ostschweizer Fachhochschule % -\section{Simplexe und simpliziale Komplexe +\section{Simplices \label{buch:section:simplexe}} -\rhead{Simplexe und simpliziale Komplexe} +\rhead{Simplices} Die Idee, das Dreieck und seinen Rand zu unterscheiden verlangt, dass wir zunächst Dreiecke und deren höherdimensionale Verallgemeinerungen, die sogenannten Simplizes entwickeln müssen. -\subsection{Simplexe und Rand -\label{buch:subsection:simplexe}} +\subsection{Simplices und Rand +\label{buch:subsection:simplices}} \subsubsection{Rand eines Dreiecks} Die Inzidenz-Matrix eines Graphen hat einer Kante die beiden Endpunkte @@ -231,8 +231,127 @@ Vorzeichen zu, die Matrix ist \] \end{definition} +\subsection{Polyeder} +\begin{figure} +\centering +\includegraphics{chapters/95-homologie/images/polyeder.pdf} +\caption{Aufbau eines zweidimensionalen Polyeders aus +verschiedenen Simplizes. +Die Schnittmenge zweier Simplizes muss ein Untersimplex beider Simplizes +sein. +Die roten Kreise im linken Bild weisen auf verschiedene Situationen +hin, wo das diese Bedingung nicht erfüllt ist. +In rechten Bild sind zusätzliche Simlizes hinzugefügt worden, um +die Bedingungen eines Polyeders zu erfüllen. +\label{buch:homologie:figure:polyeder}} +\end{figure} +Aus einzelnen Simplizes können jetzt kompliziertere geometrische +Objekte gebaut werden. +Ein Graph ist ein Beispiel für ein geometrisches Objekt, welches +als Vereinigung von 1-Simplizes entsteht. +Die Vereinigung ist aber nicht beliebig, vielmehr ist die Schnittmenge +zweier beliebiger 1-Simplizes immer entweder leer, eine Menge +mit nur einem Vertex oder ein ganzes 1-Simplex. + +Dies reicht aber nicht, wie Abbildung~\ref{buch:homologie:polyeder} +zeigt. +In einem Graphen dürfen sich Kanten nicht in einem inneren Punkt treffen, +sondern nur in Endpunkten. +Verallgemeinert auf höherdimensionale Simplizes kann man dies als die +Bedingung formulieren, dass die Schnittmenge zweier beliebiger +Simplizes immer Untersimplizes beider Simplizes sein müssen. +Wir fassen dies zusammen in der folgenden Definition. + +\begin{definition} +\index{Polyeder}% +\index{Dimension eines Polyeders}% +\index{Polyeder, Dimension eines}% +Ein {\em Polyeder} ist eine Vereingung von endlich vielen Simplizes derart, +dass die Schnittmenge zweier beliebiger Simplizes immer ein Untersimplex +beider Simplizes ist. +Die {\em Dimension} des Polyeders ist die grösste Dimension der darin +enthaltenen Simplizes. +\end{definition} + +Ein Graph ist nach dieser Definition ein eindimensionales Polyeder. +Die Mengen in der Abbildung~\ref{buch:homologie:figure:polyeder} +ist kein Polyeder, kann aber leicht zu einem Polyeder gemacht werden, +indem man einzelne Kanten mit zusätzlichen Punkten unterteilt. +Auch müssen die zweidimensionalen Simplizes aufgeteilt werden. + +Die Abbildung~\ref{buch:homologie:figure:polyeder} zeigt auch, dass +die Darstellung einer Punktmenge als Polyeder nicht eindeutig ist. +Man kann die Kanten und Flächen jederzeit weiter unterteilen, ohne +dass sich die Gestalt der gesamten Menge dadurch ändert. \subsection{Triangulation -\label{buch:subsection:}} +\label{buch:subsection:triangulation}} +Unser Ziel ist, geometrische Objekte besser verstehen zu können. +Dabei sind uns Deformationen ja sogar Knicke egal, es interessiert uns +nur die ``Gestalt'' des Objekts. +Entfernungen zwischen Punkten sind ebenfalls von untergeordneter +Bedeutung, da sie bei Deformation nicht erhalten bleiben. +Der Begriff des ``topologischen Raumes'' fasst diese Ideen mathematisch +präzise ein, eine genaue Definition würde aber an dieser Stelle zu weit +führen. +Stattdessen beschränken wir uns auf eine Klasse von Punktmengen, die man +mit Simplizes beschreiben kann. + +Ein topologischer Raum zeichnet sich durch einen Nachbarschaftsbegriff +von Punkte aus, der erlaubt zu definieren, was eine stetige Abbildung ist. +Ein stetige Abbildungen bildet nahe beeinander liegende Punkte wieder +auf nahe beeinander liegende Punkte ab. +Dass nahe liegende Punkte nicht plötzlich auf weit auseinander liegende +Punkte abgebildet werden gibt die Intuition wieder, dass Deformationen +möglich sein sollen, dass der Raum dabei aber nicht ``reissen'' darf. +Zwei topologische Räume $X$ und $Y$ können daher als ``gleichgestaltig'' +betrachtet werden, wenn es zwei stetige Abbildungen $f\colon X\to Y$ +und $g\colon Y\to X$ gibt, die zu einander invers sein. +Oder wenn sich $X$ stetig auf $Y$ abbilden lässt, so dass auch die +Umkehrabbildung stetig ist. +Eine solche Abbildung heisst ein {\em Homöomorphismus}, die beiden Räume +$X$ und $Y$ heissen {\em homomorph}. + +Eine Kugel ist natürlich kein Polyeder, aber sie kann leicht homöomorph +auf ein dreidimensionales Simplex abgebildet werden. + +\begin{beispiel} +Sei $T$ ein reguläres Tetraeder mit den Ecken auf der dreidimensionalen +Einheitskugel $B^3$. +Für jeden Richtungsvektor $x\ne 0$ sei $l(x)$ Entfernung vom Mittelpunkt des +Tetraeders bis zum Durchstosspunkt einer Geraden durch den Mittelpunkt +mit Richtungsvektor $x$ durch die Oberfläche des Tetraeders. +Dann sind die Abbildungen +\[ +f\colon +T\to B^3 +: +x \mapsto\begin{cases} +\displaystyle +\frac{x}{l(x)}&\quad\text{für $x\ne 0$}\\ +0&\quad\text{für $x=0$} +\end{cases} +\qquad\text{und}\qquad +g\colon +B^3\to T +: +x \mapsto\begin{cases} +l(x) x&\quad\text{für $x\ne 0$}\\ +0&\quad\text{für $x=0$} +\end{cases} +\] +zueinander inverse stetige Abbildungen oder Homöomorphismen. +\end{beispiel} + +Im Folgenden sollen daher nur solche topologischen Räume untersucht werden, +die homöomorph sind zu einem Polyeder. +Man nennt die homöomorphe Abbildung eines Polyeders auf so einen Raum +auch eine Triangulation. +Durch Unterteilung der Simplizes in kleiner Simplizes kann eine solche +Triangulation beliebig verfeinert werden. + + + + diff --git a/buch/chapters/references.bib b/buch/chapters/references.bib index 59a8376..977bf81 100644 --- a/buch/chapters/references.bib +++ b/buch/chapters/references.bib @@ -39,6 +39,13 @@ abstract = "In this paper, we present Google, a prototype of a large-scale searc year = {2016}, } +@online{buch:rfc2409, + title = {The Internet Key Exchange (IKE)}, + author = { D. Harkins and D. Carrel}, + url = {https://datatracker.ietf.org/doc/html/rfc2409}, + year = {1998} +} + @online{buch:fftw, title = {Fastest Fourier Transform in the West}, url = {http://www.fftw.org/}, diff --git a/buch/papers/clifford/10_Quaternionen.tex b/buch/papers/clifford/10_Quaternionen.tex index 375c6e7..3fe2876 100644 --- a/buch/papers/clifford/10_Quaternionen.tex +++ b/buch/papers/clifford/10_Quaternionen.tex @@ -7,32 +7,39 @@ \rhead{Quaternionen} Wie die komplexen Zahlen eine Erweiterung der reellen Zahlen sind, sind die Quaternionen eine Erweiterung der komplexen Zahlen für den dreidimensionalen Raum. Sie haben, wie die komplexen Zahlen, eine dreh-streckende Eigenschaft. -Sie finden beispielsweise in der Computergraphik und in der Robotik Anwendung. +Sie finden beispielsweise in der Computergrafik und Robotik Anwendung. Die Quaternionen \begin{align} q = w + xi + yj + zk \quad w,x,y,z \in \mathbb{R}\enspace q \in \mathbb{H} \end{align} -können dabei eine Drehstreckung mit dieser Formel erreichen +können dabei eine Drehstreckung mit \begin{align} \label{QuatRot} \begin{split} - &v'' = qvq^{-1};\quad q,v,q^{-1} \in \mathbb{H}\\ - &\operatorname{Re}(q) = \operatorname{Re}(q^{-1})\quad \operatorname{Im}(q) = -\operatorname{Im}(q^{-1}) + v \mapsto v'' = qvq^{-1} \end{split} \end{align} -Auffallend ist hier schon die Ähnlichkeit zu dem Kapitel Rotation. Man könnte sich nun fragen wieso es drei imaginäre Einheiten $i,j,k$ gibt und nicht zwei, was doch näherliegender wäre. Der Grund liegt darin, weil es in der dritten Dimension drei Drehachsen gibt, anstatt nur eine. Wie im Kapitel Rotation beschrieben können wir auch hier die drei Drehungen durch Linearkombinationen von drei Bivektoren beschreiben. In der geometrischen Algebra ist es leicht herauszufinden wie viele Imaginärteile für jede weitere Dimension existieren. Dabei muss man nur die Anzahl der unabhängigen Bivektoren ermitteln. In der vierten Dimension würden es beispielsweise durch alle Vektorkombinationen von $\mathbf{e}_1, \mathbf{e}_2,\mathbf{e}_3, \mathbf{e}_4$ insgesamt 8 Bivektoren existieren (Nicht 16, da $\mathbf{e}_{ij} = -\mathbf{e}_{ji}$ nicht unabhängig voneinander sind). +erreichen, falls $q,v,q^{-1} \in \mathbb{H}$ und die Zusammenhänge +\begin{align} + \operatorname{Re}(q) = \operatorname{Re}(q^{-1})\quad \operatorname{Im}(q) = -\operatorname{Im}(q^{-1}) +\end{align} +gelten. Auffallend ist bei der abbildenden Funktion \eqref{QuatRot} schon die Ähnlichkeit zu \eqref{rotGA} im Kapitel Rotation. Man könnte sich nun fragen wieso es drei imaginäre Einheiten $i,j,k$ gibt und nicht zwei, was doch näherliegender wäre. Der Grund liegt darin, weil es in der dritten Dimension drei Drehachsen gibt, anstatt nur eine. Wie im Kapitel Rotation beschrieben können wir auch hier die drei Drehungen durch Linearkombinationen von drei Bivektoren beschreiben. In der geometrischen Algebra ist es leicht herauszufinden wie viele Imaginärteile für jede weitere Dimension existieren. Dabei muss man nur die Anzahl der unabhängigen Bivektoren ermitteln. In der vierten Dimension würden es beispielsweise durch alle Vektorkombinationen von $\mathbf{e}_1, \mathbf{e}_2,\mathbf{e}_3, \mathbf{e}_4$ insgesamt 8 Bivektoren existieren (Nicht 16, da $\mathbf{e}_{ij} = -\mathbf{e}_{ji}$ nicht unabhängig voneinander sind). -Ohne die geometrische Algebra, haben wir jetzt aber leider ein kleines Problem. Für die Darstellung der Quaternionen bräuchten wir insgesamt vier Achsen. Drei für die imaginären Einheiten und eine für die reelle Einheit. Ein weiterer Nachteil in visueller Hinsicht entsteht beim Anwenden eines Quaternion auf einen Vektor. Sie befinden sich nicht im gleichen Raum und müssen zuerst ineinander umgewandelt werden, um damit zu rechnen, wie man bei $v \in \mathbb{H}$ in der Formel (\ref{QuatRot}) sieht. +Ohne die geometrische Algebra, haben wir jetzt aber leider ein kleines Problem. Für die Darstellung der Quaternionen bräuchten wir insgesamt vier Achsen. Drei für die imaginären Einheiten und eine für die reelle Einheit. Ein weiterer Nachteil in visueller Hinsicht entsteht beim Anwenden eines Quaternion auf einen Vektor. Sie befinden sich nicht im gleichen Raum und müssen zuerst durch +\begin{align} + \mathbf{v} = x\mathbf{\hat{x}} + y\mathbf{\hat{y}} + z \mathbf{\hat{z}} \in \mathbb{R}^3 \enspace\mapsto\enspace v = 0 + xi + yj + zk \in \mathbb{H} +\end{align} +ineinander umgewandelt werden, um damit zu rechnen. \subsection{Geometrische Algebra} -Die geometrische Algebra besitzt die Fähigkeit beide Probleme zu lösen. Die Quaternionen können, wie schon im 2 dimensionalen Fall durch die gerade Grade $G_3^+(\mathbb{R}) \cong \mathbb{H}$ dargestellt werden. Da wir uns jetzt aber in $G_3(\mathbb{R})$ befinden haben wir drei Basisvektoren $\mathbf{e}_1, \mathbf{e}_2, \mathbf{e}_3$ und können somit drei Bivektoren bilden $\mathbf{e}_{12}, \mathbf{e}_{23}, \mathbf{e}_{31}$. +Die geometrische Algebra kann beide Probleme beheben. Die Quaternionen können, wie schon im zweidimensionalen Fall durch die gerade Grade $G_3^+(\mathbb{R}) \cong \mathbb{H}$ dargestellt werden. Da wir uns jetzt aber in $G_3(\mathbb{R})$ befinden haben wir drei Basisvektoren $\mathbf{e}_1, \mathbf{e}_2, \mathbf{e}_3$ und können somit drei Bivektoren $\mathbf{e}_{12}, \mathbf{e}_{23}, \mathbf{e}_{31}$ bilden. \begin{definition} - Multivektoren mit Drehstreckenden Eigenschaften in $G_3(\mathbb{R})$ (gleichbedeutend zu Quaternionen) + Die Multivektoren mit Drehstreckenden Eigenschaften in $G_3(\mathbb{R})$ sind \begin{align} - \mathbf{q} = w + x\mathbf{e}_{12} + y\mathbf{e_{23}} + z\mathbf{e_{31}}; \quad w,x,y,z \in \mathbb{R};\enspace \mathbf{q} \in \mathbb{G}_3^+ + \mathbf{q} = w + x\mathbf{e}_{12} + y\mathbf{e}_{23} + z\mathbf{e}_{31} \quad w,x,y,z \in \mathbb{R}\enspace \mathbf{q} \in \mathbb{G}_3^+. \end{align} \end{definition} -Die Probleme werden dadurch gelöst, da wir die Bivektoren im Raum nicht durch einzelne Achsen darstellen müssen, sondern sie als eine orientiere Fläche darstellen können. Anstatt die Vektoren in Quaternionen umzurechnen, können wir jetzt die Vektoren separat im gleichen Raum darstellen. +Die Probleme werden dadurch gelöst, da wir die Bivektoren im Raum nicht durch einzelne Achsen darstellen müssen, sondern sie als eine orientiere Fläche darstellen können. Anstatt die Vektoren in Quaternionen umzurechnen, können wir jetzt die Vektoren separat im gleichen Raum, wie in Abbildung \ref{BildQuaternionen} gezeigt, darstellen. \begin{figure} \centering \begin{tikzpicture} @@ -66,95 +73,76 @@ Die Probleme werden dadurch gelöst, da wir die Bivektoren im Raum nicht durch e \caption{Darstellung eines Quaternion $\mathbf{q}$ und eines Vektors $\mathbf{v}$ im selben Raum} \label{BildQuaternionen} \end{figure} -Wie schon im 2 dimensionalen Fall beschreibt ein Bivektor, um wie viel der um 90 grad gedrehte orginale Vektor gestreckt wird. Dabei dreht jeder Bivektor den Vektor um eine andere Achse. -\\BILD?\\ -In der Computergraphik und Robotik macht eine Drehstreckung aber nicht viel Sinn. Wieso sollte ein Objekt bei einer Drehung zusätzlich noch grösser werden? Darum verwendet man sogenannte Einheitsquaternionen, welche den Betrag $|q|=1$ haben. Sie rotieren die Objekte bzw. Vektoren lediglich. +Wie schon im zweidimensionalen Fall \eqref{GAdrehstreck} beschreibt im dreidimensionalen Fall mit drei Bivektoren +\begin{align} + \mathbf{qv} &= (w + x\mathbf{e}_{12} + y\mathbf{e}_{23} + z\mathbf{e}_{31})(a\mathbf{e}_1+b\mathbf{e}_2+c\mathbf{e}_3)\\ + &= \underbrace{w(a\mathbf{e}_1+b\mathbf{e}_2+c\mathbf{e}_3)}_{w\mathbf{v}} + \underbrace{x(-a\mathbf{e}_2+b\mathbf{e}_1}_{x\mathbf{v}_{\angle 90^\circ, \parallel \mathbf{e}_{12}}}+c\mathbf{e}_{123}) + \underbrace{y(-b\mathbf{e}_3+c\mathbf{e}_2}_{y\mathbf{v}_{\angle 90^\circ, \parallel \mathbf{e}_{23}}}+a\mathbf{e}_{123}) + \underbrace{z(a\mathbf{e}_3-c\mathbf{e}_1}_{z\mathbf{v}_{\angle 90^\circ, \parallel \mathbf{e}_{31}}}-b\mathbf{e}_{123}) +\end{align} +jeder Bivektoranteil, um wie viel der um 90° gedrehte zu der Ebene parallele Teil des Vektors gestreckt wird. Dabei dreht jeder Bivektor den Vektor um eine andere Achse und man sieht die dreh-streckende Eigenschaft ähnlich zu den komplexen Zahlen. Der störende Trivektoranteil $(xc+ya+zb)\mathbf{e}_{123}$ bekommt man aber nur weg, indem man noch wie in der Rotationsformel \eqref{QuatRot} den Inversen Quaternion $\mathbf{q}^{-1}$ anschliessend multipliziert, wobei die dreh-gestreckten parallelen Anteile nochmals um den gleichen Faktor dreh-gestreckt werden. Da nur so der Trivektoranteil wegfällt, sieht man, dass die Rotationsformel, der einzige Vernünftige weg ist, mit Quaternionen zu arbeiten. + +In der Computergraphik und Robotik macht eine Drehstreckung aber nicht viel Sinn. Wieso sollte ein Objekt bei einer Drehung zusätzlich noch grösser werden? Darum verwendet man sogenannte Einheitsquaternionen, welche den Betrag $|\mathbf{q}|=1$ haben und somit rotieren sie die Objekte bzw. Vektoren lediglich. \begin{definition} - Einheitsquaternionen + Die Einheitsquaternionen sind definiert als \begin{align} \mathbf{q} = \cos(\alpha) + sin(\alpha)(\tilde{x}\mathbf{e}_{12} + \tilde{y}\mathbf{e}_{23} + \tilde{z}\mathbf{e}_{31}) \end{align} \end{definition} -Dabei ist definiert, dass $\tilde{x}^2+\tilde{y}^2+\tilde{z}^2=1$. Somit beträgt der Betrag von $\mathbf{q}$ immer 1. +Zudem setzten wir $\tilde{x}^2+\tilde{y}^2+\tilde{z}^2=1$, damit \begin{align} - |\mathbf{q}| = \sqrt{cos(\alpha)^2 + sin(\alpha)^2(\tilde{x}^2+\tilde{y}^2+\tilde{z}^2) } = \sqrt{cos(\alpha)^2 + sin(\alpha)^2} = 1 + |\mathbf{q}| = \sqrt{cos(\alpha)^2 + sin(\alpha)^2(\tilde{x}^2+\tilde{y}^2+\tilde{z}^2) } = \sqrt{cos(\alpha)^2 + sin(\alpha)^2} = 1. \end{align} -Der Winkel $\alpha$ beschreibt dabei, wie im Bild (...) gezeigt den halben Winkel, um welchen der parallelen Anteil $\mathbf{v_{\perp}}$ des Vektors $\mathbf{v}$ zur kombinierten Bivektorebene $sin(\alpha)^2(\tilde{x}^2+\tilde{y}^2+\tilde{z}^2)$ gedreht wird. +Der Winkel $\alpha$ beschreibt dabei, wie im Bild \ref{BildQuaternionBeispiel2} gezeigt den halben Winkel, um welchen der parallelen Anteil $\mathbf{v_{\parallel}}$ des Vektors $\mathbf{v}$ zur kombinierten Bivektorebene $sin(\alpha)^2(\tilde{x}\mathbf{e}_{12} + \tilde{y}\mathbf{e}_{23} + \tilde{z}\mathbf{e}_{31})$ gedreht wird. -Um einen Vektor zu drehen, verwendet man wieder die gleiche Formel, wie auch schon im zweidimensionalen Fall. +Um einen Vektor zu drehen, verwendet man die in Kapitel Rotation hergeleitete Formel \begin{align} \label{QuatRotGA} \begin{split} - &\mathbf{v}'' = \mathbf{qvq}^{-1}\\ - &\operatorname{Re}(\mathbf{q}) = \operatorname{Re}(\mathbf{q}^{-1});\enspace \operatorname{Im}(\mathbf{q}) = -\operatorname{Im}(\mathbf{q}^-1) + \mathbf{v}'' = \mathbf{qvq}^{-1}, \end{split} \end{align} -Es ist wichtig bei Quaternionen für eine reine Drehstreckung mit $q$ und $q^{-1}$ beidseitig zu multiplizieren, sonst werden die senkrechten Anteile zu den Bivektorebenen ebenfalls beeinflusst, wie man im Kapitel Rotation bei der Formel (\ref{RotAufPerpPar}) sehen kann. +wobei wie auch schon bei den Quaternionen gelten muss, dass +\begin{align} \label{GAReIm} + \operatorname{Re}(\mathbf{q}) = \operatorname{Re}(\mathbf{q}^{-1}) \enspace\text{und}\enspace \operatorname{Im}(\mathbf{q}) = -\operatorname{Im}(\mathbf{q}^-1). +\end{align} +Der Grund für die Zusammenhänge \eqref{GAReIm} kann man durch die hergeleitete vereinfachte Rotationsformel \eqref{GAvereinfRot} sehen, weil durch den negierten Winkel $\theta$ der Reelle bzw. Grad 0 Anteil +\begin{align} + \operatorname{Re}(e^{-\theta \mathbf{e}_{12}}) = \operatorname{Re}(e^{\theta \mathbf{e}_{12}}) +\end{align} +und der Imaginäre bzw. Grad 2 Anteil +\begin{align} + \operatorname{Im}(e^{-\theta \mathbf{e}_{12}}) = -\operatorname{Im}(e^{\theta \mathbf{e}_{12}}) +\end{align} +ist. Durch die geometrische Algebra sieht man nun wieso es wichtig ist bei Quaternionen für eine reine Drehstreckung mit $\mathbf{q}$ und $\mathbf{q}^{-1}$ beidseitig zu multiplizieren, sonst werden die senkrechten Anteile zu den Bivektorebenen ebenfalls beeinflusst, wie man im Kapitel Rotation bei der Formel (\ref{RotAufPerpPar}) sehen kann. \begin{beispiel} - Eine Drehung eines Vektors $\mathbf{v}= 1\mathbf{e}_2$ um 90 Grad um die $\mathbf{e}_1$-Achse und danach 90 Grad um die $\mathbf{e}_2$-Achse. Dafür nehmen wir zuerst einen Einheitsquaternion welcher um die Orientierte Ebene $\mathbf{e}_{23}$ um 90 Grad dreht + Eine Drehung eines Vektors $\mathbf{v}= 1\mathbf{e}_2$ um 90 Grad um die $\mathbf{e}_1$-Achse und danach 90 Grad um die $\mathbf{e}_2$-Achse. Dafür nehmen wir zuerst einen Einheitsquaternion \begin{align} - \mathbf{q}_{23} &= \cos(\pi/4) + sin(\pi/4)(1\mathbf{e}_{23}) = e^{(\pi/4)\mathbf{e}_{23}} &= 0.71 + 0.71\mathbf{e}_{23}\\ - \mathbf{q}_{23}^{-1} &&= 0.71 - 0.71\mathbf{e}_{23} + \mathbf{q}_{23} &= \cos(\pi/4) + sin(\pi/4)(1\mathbf{e}_{23}) = e^{(\pi/4)\mathbf{e}_{23}} &= \textstyle{\frac{\sqrt{2}}{2}}(1 + \mathbf{e}_{23})\\ + \mathbf{q}_{23}^{-1} &&= \textstyle{\frac{\sqrt{2}}{2}} (1- \mathbf{e}_{23}) \end{align} - und danach Einheitsquaternion welcher um die Orientierte Ebene $\mathbf{e}_{31}$ um 90 Grad dreht + welcher um die $\mathbf{e}_{2}$-$\mathbf{e}_{3}$-Ebene um 90 Grad dreht und danach Einheitsquaternion \begin{align} - \mathbf{q}_{31} &= \cos(\pi/4) + sin(\pi/4)(1\mathbf{e}_{31}) = e^{(\pi/4)\mathbf{e}_{31}} &= 0.71 + 0.71\mathbf{e}_{31}\\ - \mathbf{q}_{31}^{-1} &&= 0.71 - 0.71\mathbf{e}_{31} + \mathbf{q}_{31} &= \cos(\pi/4) + sin(\pi/4)(1\mathbf{e}_{31}) = e^{(\pi/4)\mathbf{e}_{31}} &= \textstyle{\frac{\sqrt{2}}{2}}(1 + \mathbf{e}_{31})\\ + \mathbf{q}_{31}^{-1} &&= \textstyle{\frac{\sqrt{2}}{2}}(1 - \mathbf{e}_{31}) \end{align} - Um die vollständige Rotation zu beschreiben können die Einheitsquaternion multipliziert werden, wobei die Reihenfolge der Ausführung beachtet werden muss + welcher um die $\mathbf{e}_{3}$-$\mathbf{e}_{1}$-Ebene um 90 Grad dreht. Um die vollständige Rotation zu beschreiben können die Einheitsquaternion multipliziert werden, wobei die Reihenfolge der Ausführung beachtet werden muss. Somit ist \begin{align} \label{FormelBeispielQuaternion} - \mathbf{q} &= \mathbf{q}_{31}\mathbf{q}_{23} = (0.71 + 0.71\mathbf{e}_{31})(0.71 + 0.71\mathbf{e}_{23}) &= 0.5 + 0.5\mathbf{e}_{31} + 0.5 \mathbf{e}_{23} + 0.5 \mathbf{e}_{12}\\ - \mathbf{q}^{-1} &= \mathbf{q}_{23}^{-1}\mathbf{q}_{31}^{-1} = (0.71 - 0.71\mathbf{e}_{23})(0.71 - 0.71\mathbf{e}_{31}) &= 0.5 - 0.5\mathbf{e}_{31} - 0.5 \mathbf{e}_{23} - 0.5 \mathbf{e}_{12} + \mathbf{q} &= \mathbf{q}_{31}\mathbf{q}_{23} = \textstyle{\frac{\sqrt{2}}{2}}(1 + \mathbf{e}_{31})\textstyle{\frac{\sqrt{2}}{2}}(1 + \mathbf{e}_{23}) &= \textstyle{\frac{1}{2}}(1 + \mathbf{e}_{31} + \mathbf{e}_{23} + \mathbf{e}_{12})\\ + \mathbf{q}^{-1} &= \mathbf{q}_{23}^{-1}\mathbf{q}_{31}^{-1} = \textstyle{\frac{\sqrt{2}}{2}} (1- \mathbf{e}_{23})\textstyle{\frac{\sqrt{2}}{2}}(1 -\mathbf{e}_{31}) &= \textstyle{\frac{1}{2}}(1 - \mathbf{e}_{31} - \mathbf{e}_{23} - \mathbf{e}_{12}). \end{align} Wenn wir nun den Quaternion $\mathbf{q}$ auf den Vektor $\mathbf{v}$ anwenden \begin{align} - \mathbf{v}'' = \mathbf{qvq}^{-1} &= (0.5 + 0.5\mathbf{e}_{31} + 0.5 \mathbf{e}_{23} + 0.5 \mathbf{e}_{12})(1\mathbf{e}_2)(0.5 - 0.5\mathbf{e}_{31} - 0.5 \mathbf{e}_{23} - 0.5 \mathbf{e}_{12})\\ - &= (0.5\mathbf{e}_2 + 0.5 \mathbf{e}_{123} - 0.5 \mathbf{e}_3 + 0.5 \mathbf{e}_1)(0.5 - 0.5\mathbf{e}_{31} - 0.5 \mathbf{e}_{23} - 0.5 \mathbf{e}_{12})\\ - &= (0.25 + 0.25 + 0.25 + 0.25)\mathbf{e}_1 + (0.25 + 0.25 - 0.25 - 0.25)\mathbf{e}_2 +\\ &(-0.25 + 0.25 - 0.25 + 0.25)\mathbf{e}_3 + (0.25 - 0.25 - 0.25 + 0.25)\mathbf{e}_{123}\\ + \mathbf{v}'' = \mathbf{qvq}^{-1} &= \textstyle{\frac{1}{2}}(1 + \mathbf{e}_{31} + \mathbf{e}_{23} + \mathbf{e}_{12})(1\mathbf{e}_2)\textstyle{\frac{1}{2}}(1 - \mathbf{e}_{31} - \mathbf{e}_{23} - \mathbf{e}_{12})\\ + &= \textstyle{\frac{1}{4}}(\mathbf{e}_2 + \mathbf{e}_{123} - \mathbf{e}_3 + \mathbf{e}_1)(1 - \mathbf{e}_{31} - \mathbf{e}_{23} - \mathbf{e}_{12})\\ + &= (\textstyle{\frac{1}{4}} + \textstyle{\frac{1}{4}} + \textstyle{\frac{1}{4}} + \textstyle{\frac{1}{4}})\mathbf{e}_1 + (\textstyle{\frac{1}{4}} + \textstyle{\frac{1}{4}} - \textstyle{\frac{1}{4}} - \textstyle{\frac{1}{4}})\mathbf{e}_2 +\\ &(-\textstyle{\frac{1}{4}} + \textstyle{\frac{1}{4}} - \textstyle{\frac{1}{4}} + \textstyle{\frac{1}{4}})\mathbf{e}_3 + (\textstyle{\frac{1}{4}} - \textstyle{\frac{1}{4}} - \textstyle{\frac{1}{4}} + \textstyle{\frac{1}{4}})\mathbf{e}_{123}\\ &= 1e_1 \end{align} Anders betrachtet könnte man von der Formel \eqref{FormelBeispielQuaternion} sehen, dass der Drehwinkel \begin{align} - \alpha = \arccos(w) = \arccos(0.5) = 60° + \alpha = \arccos(w) = \arccos(\textstyle{\frac{1}{2}}) = 60° \end{align} und die Ebene der kombinierten Bivektoren wie in Abbildung \ref{BildQuaternionBeispiel2} aussieht. Somit kann man sich ebenfalls Vorstellen, wie der parallele Anteil zur Ebene insgesamt um 120° rotiert wird während der senkrechte Anteil unverändert bleibt \end{beispiel} -\begin{figure} - \centering - \begin{tikzpicture} - % Koordinatensystem - \draw[thin,gray!40] (-3,-2) grid (3,3); - \draw[<->] (-3,0)--(3,0) node[right]{$a_1$}; - \draw[<->] (0,-2)--(0,3) node[above]{$a_2$}; - \draw[<->] (3,3)--(-2,-2) node[left]{$a_3$}; - - % q Quaternion - \draw[line width=0,fill=blue!40] (0,0)--(1.41,0)--(1.41,1.41)--(0,1.41) - node[xshift=0.375cm, yshift=-0.5cm, blue]{$x\boldsymbol{e_{12}}$}; - \draw[->] (1.35, 1.2) arc (0:310:0.15); - - \draw[line width=0,fill=blue!40] (0,0)--(-1,-1)--(-1,0.41)--(0,1.41) - node[xshift=-0.5cm, yshift=-1.5cm, blue]{$y\boldsymbol{e_{23}}$}; - \draw[->] (-0.65,-0.5) arc (0:310:0.15); - - \draw[line width=0,fill=blue!40] (0,0)--(-1,-1)--(0.41,-1)--(1.41,0) - node[xshift=-0.7cm, yshift=-0.2cm, blue]{$z\boldsymbol{e_{31}}$}; - \draw[->] (0.4,-0.8) arc (0:310:0.15); - - % Basisvektoren - \draw[line width=1.5pt,gray,-stealth](0,0)--(2,0) node[anchor=south west]{$\boldsymbol{e_1}$}; - \draw[line width=1.5pt,gray,-stealth](0,0)--(0,2) node[anchor=north west, yshift=0.2cm]{$\boldsymbol{e_2}$}; - \draw[line width=1.5pt,gray,-stealth](0,0)--(-1.41,-1.41) node[anchor=south, yshift=0.2cm]{$\boldsymbol{e_3}$}; - - % v Vektor - \draw[line width=2pt,black,-stealth](-0.05,0)--(-0.05,2) node[anchor=east]{$\boldsymbol{v}$}; - % v'' Vektor - \draw[line width=2pt,black,-stealth](0,0.05)--(2,0.05) node[anchor=north]{$\boldsymbol{v}''$}; - \end{tikzpicture} - \caption{Beispiel für Drehung um 90 Grad je um die $\mathbf{e}_1$- und $\mathbf{e}_2$-Achse.} - \label{BildQuaternionBeispiel} -\end{figure} \begin{figure} \centering @@ -201,7 +189,7 @@ Es ist wichtig bei Quaternionen für eine reine Drehstreckung mit $q$ und $q^{-1 \subsection{Interpolation} In der Computergrafik wird Interpolation verwendet, um eine flüssige Drehbewegung zu erreichen. Dabei wird die gewünschte Drehbewegungen des Objektes in kleinere aufgeteilt. Man kann dabei mit zwei verschiedenen Systemen arbeiten. \begin{itemize} - \item Mit den Eulerschen Winkeln, welche für die Meisten zwar intuitiver sind, aber dafür Nachteile haben, worauf ich in diesem Abschnitt eingehen werde. Dabei kann eine ganze Drehbewegung $\mathbf{v}'' = R\mathbf{v}$ durch die Drehmatrix $R$ dargestellt werden. + \item Mit den Eulerschen Winkeln, welche für die Meisten zwar intuitiver sind, aber dafür Nachteile haben, worauf ich in diesem Abschnitt eingehen werde. Dabei kann eine ganze Drehbewegung $\mathbf{v}'' = R\mathbf{v}$ durch die Drehmatrix $R$ \begin{align} \begin{split} &R = R_z(\gamma) R_y(\beta) R_x(\alpha)\\ @@ -217,11 +205,9 @@ In der Computergrafik wird Interpolation verwendet, um eine flüssige Drehbewegu \end{pmatrix} \end{split} \end{align} - Wichtig dabei zu sehen ist, dass die Drehbewegungen durch die einzelnen Matrizen nacheinander ausgeführt werden. Das bedeutet, wenn man die Reihenfolge vertauscht, bekommt man eine völlig andere Drehung. Man kann die Auswirkungen der Reihenfolge gut bei einem Gimbal (REF zu BILD) sehen. Die Matrix ganz links ist die, welche als letztes Angewendet wird. Somit bildet sie die Drehung des äusseren Rings, welche auch die zwei inneren Ringe und das Objekt mitdreht. Die Matrix ganz rechts hingegen bildet nur die Drehung des inneren Rings, welche nur das Objekt selber dreht. Man kann dabei erkennen, dass vorgehen dabei sehr intuitiv ist, aber es kompliziert sein kann eine gewünschte Drehbewegung auszuführen, da sich beim Drehen der äusseren Achse, sich auch die Inneren drehen. Das bedeutet, wenn man sich eine Drehbewegung um die anfängliche x Achse mit $R_x(\alpha_2)$ wünscht, und vorher eine beliebige Drehung $R = R_z(\gamma_1) R_y(\beta_1) R_x(\alpha_1)$ ausgeführt hat, bekommt man nicht das richtige Ergebnis, da die anfängliche x-Achse durch die Drehmatrizen $R_z(\gamma_1)$ und $R_y(\beta_1)$ zu einer neuen, lokalen x-Achse wurde. + dargestellt werden. Wichtig dabei zu sehen ist, dass die Drehbewegungen durch die einzelnen Matrizen nacheinander ausgeführt werden. Das bedeutet, wenn man die Reihenfolge vertauscht, bekommt man eine völlig andere Drehung. Man kann die Auswirkungen der Reihenfolge gut bei einem Gimbal (REF zu BILD) sehen. Die Matrix ganz links ist die, welche als letztes Angewendet wird. Somit bildet sie die Drehung des äusseren Rings, welche auch die zwei inneren Ringe und das Objekt mitdreht. Die Matrix ganz rechts hingegen bildet nur die Drehung des inneren Rings, welche nur das Objekt selber dreht. Man kann dabei erkennen, dass vorgehen dabei sehr intuitiv ist, aber es kompliziert sein kann eine gewünschte Drehbewegung auszuführen, da sich beim Drehen der äusseren Achse, sich auch die Inneren drehen. Das bedeutet, wenn man sich eine Drehbewegung um die anfängliche x Achse mit $R_x(\alpha_2)$ wünscht, und vorher eine beliebige Drehung $R = R_z(\gamma_1) R_y(\beta_1) R_x(\alpha_1)$ ausgeführt hat, bekommt man nicht das richtige Ergebnis, da die anfängliche x-Achse durch die Drehmatrizen $R_z(\gamma_1)$ und $R_y(\beta_1)$ zu einer neuen, lokalen x-Achse wurde. \item Andererseits mit den Quaternionen, welche die besondere Eigenschaft haben, dass eine Drehung immer um die globale Achsen ausgeführt wird, egal in welcher Rotationsposition sich das Objekt befindet. \end{itemize} Für Spielentwickler ist es darum meist sinnvoller Quaternionen für Drehbewegungen anzuwenden, als sich mit komplizierten Berechnungen mit Eulerschen Winkeln herumzuschlagen. \subsection{Gimbal-Lock} -Ein weiterer Nachteil der Eulerschen Winkel ist das Gimbal-Lock. Es entsteht dann, wenn der äussere Ring Deckungsgleich über denn Inneren gedreht wird. Dabei verliert das Gimbal eine Drehrichtung, da der äussere und Innere Ring nun die gleiche Drehrichtung besitzen. Dies kann beispielsweise Probleme bei Spielen bei der Berechnung der Interpolation führen. Man hat das bei älteren Spielen dann gesehen, wenn plötzlich Gliedmassen bei den Spielermodellen in unnatürlichen Richtungen gesprungen sind. -\subsection{Fazit} -andere Darstellungsweise. Besser für Verständnis => komplexe Zahlen erscheinen ähnlicher zu Quaternionen? Eine Sprache für alle Geometrische Probleme
\ No newline at end of file +Ein weiterer Nachteil der Eulerschen Winkel ist das Gimbal-Lock. Es entsteht dann, wenn der äussere Ring Deckungsgleich über denn Inneren gedreht wird. Dabei verliert das Gimbal eine Drehrichtung, da der äussere und Innere Ring nun die gleiche Drehrichtung besitzen. Dies kann beispielsweise Probleme bei Spielen bei der Berechnung der Interpolation führen. Man hat das bei älteren Spielen dann gesehen, wenn plötzlich Gliedmassen bei den Spielermodellen in unnatürlichen Richtungen gesprungen sind.
\ No newline at end of file diff --git a/buch/papers/clifford/11_Fazit.tex b/buch/papers/clifford/11_Fazit.tex new file mode 100644 index 0000000..7352399 --- /dev/null +++ b/buch/papers/clifford/11_Fazit.tex @@ -0,0 +1,9 @@ +% +% teil3.tex -- Beispiel-File für Teil 3 +% +% (c) 2020 Prof Dr Andreas Müller, Hochschule Rapperswil +% +\section{Fazit} +\rhead{Fazit} + +Die geometrische Algebra ist dafür ausgelegt geometrische Operationen, wie die Spiegelung oder Rotation, einfach zu beschreiben. Dadurch kann sie als gute alternative zu der linearen Algebra angewendet werden, um graphische Probleme zu lösen. Sie kann zudem zum Verständnis hinter der Rotierenden Eigenschaften der komplexen Zahlen und Quaternionen beitragen und die Zusammenhänge zwischen den komplexen Zahlen und den Quaternionen besser zeigen.
\ No newline at end of file diff --git a/buch/papers/clifford/6_PauliMatrizen.tex b/buch/papers/clifford/6_PauliMatrizen.tex index e41275a..4438aeb 100644 --- a/buch/papers/clifford/6_PauliMatrizen.tex +++ b/buch/papers/clifford/6_PauliMatrizen.tex @@ -91,7 +91,7 @@ bestätigt. Man kann bei den Definitionen \ref{def:defPauli} und \ref{def:defPau \begin{hilfssatz} Ein beliebiger Multivektor \begin{align} \label{MultiVektorAllg} - M = a_0\mathbf{e}_0 + a_1\mathbf{e}_1 + a_2\mathbf{e}_3 + a_{12}\mathbf{e}_{12} + a_{23}\mathbf{e}_{23} + a_{31}\mathbf{e}_{31} + a_{123}\mathbf{e}_{123}\\ + M = a_0\mathbf{e}_0 + a_1\mathbf{e}_1 + a_2\mathbf{e}_3 + a_{12}\mathbf{e}_{12} + a_{23}\mathbf{e}_{23} + a_{31}\mathbf{e}_{31} + a_{123}\mathbf{e}_{123} \end{align} erhält durch das einsetzten der Formel Matrizen \eqref{Pauli} und \eqref{Pauli2} die Form \begin{align} diff --git a/buch/papers/clifford/7_Reflektion.tex b/buch/papers/clifford/7_Reflektion.tex index bdfb4e8..549848c 100644 --- a/buch/papers/clifford/7_Reflektion.tex +++ b/buch/papers/clifford/7_Reflektion.tex @@ -6,15 +6,15 @@ \section{Spiegelung} \rhead{Spiegelung} -Die Spiegelung ist eine grundlegende, geometrische Operation, aus welcher man weitere, wie beispielsweise die später beschriebene Rotation, ableiten kann. Da die geometrische Algebra für geometrische Anwendungen ausgelegt ist, sollte die Spiegelung auch eine einfache, praktische Formulierung besitzen. +Die Spiegelung ist eine grundlegende, geometrische Operation, aus welcher man weitere Operationen, wie beispielsweise die später beschriebene Rotation, ableiten kann. Da die geometrische Algebra für geometrische Anwendungen ausgelegt ist, sollte die Spiegelung auch eine einfache, praktische Formulierung besitzen. \begin{figure} \centering \begin{tikzpicture} \draw[thin,gray!40] (-3,-1) grid (3,3); \draw[<->] (-3,0)--(3,0) node[right]{$a_1$}; \draw[<->] (0,-1)--(0,3) node[above]{$a_2$}; + \draw[blue, line width=1.0pt] (0,3)--(0,-1) node[anchor=south east]{$\sigma_u$}; \draw[line width=2pt,black,-stealth](0,0)--(2,2) node[anchor=south east]{$\boldsymbol{v}$}; - \draw[line width=1.5pt,blue,-stealth](0,0)--(0,2.5) node[anchor=south east]{$\boldsymbol{u}$}; \draw[line width=2pt,black,-stealth](0,0)--(-2,2) node[anchor=south east]{$\boldsymbol{v'}$}; \draw[line width=1.5pt,gray,-stealth](0,0)--(1,0) node[anchor=north]{$\boldsymbol{e_1}$}; \draw[line width=1.5pt,gray,-stealth](0,0)--(0,1) node[anchor=north east]{$\boldsymbol{e_2}$}; @@ -22,62 +22,74 @@ Die Spiegelung ist eine grundlegende, geometrische Operation, aus welcher man we 0.25cm]{$\boldsymbol{v_{\perp u}}$}; \draw[line width=1.5pt,red,-stealth](-2,2)--(0,2) node[xshift=-1cm, yshift= 0.25cm]{$\boldsymbol{v_{\perp u}}$}; - \draw[line width=1.5pt,purple,-stealth](0,1.5)--(1,1.5) node[xshift=-0.5cm, yshift=-0.25cm]{$\boldsymbol{\hat{n}}$}; + \draw[line width=1.5pt,blue,-stealth](0,0.05)--(1,0.05) node[xshift=-0.5cm, yshift=-0.25cm]{$\boldsymbol{\hat{u}}$}; \end{tikzpicture} - \caption{Spiegelung des Vektors \textbf{v} an Spiegelachse bzw. Vektor \textbf{u}} + \caption{Spiegelung des Vektors $\mathbf{v}$ an der Spiegelebene $\sigma_u$ mit dem Normalenvektor $\mathbf{\hat{u}}$} \label{BildSpiegelung} \end{figure} \subsection{Linearen Algebra} Aus der linearen Algebra ist bekannt, dass man eine Spiegelung an einer Ebene wie folgt beschreiben kann. \begin{definition} - Die Spiegelungsgleichung in der linearen Algebra mit dem Normalenvektor $\mathbf{\hat{n}}$ zur Spiegelebene ist + Die Abbildung der Spiegelung in der linearen Algebra mit dem Normalenvektor $\mathbf{\hat{u}}$ zur Spiegelebene ist \begin{equation} \label{RefLinAlg} - \mathbf{v^{'}} = \mathbf{v} - 2 \cdot \mathbf{v_{\parallel \hat{n}}} = \mathbf{v} - 2 \cdot \mathbf{v_{\perp u}}. + \mathbf{v} = \mathbf{v_{\perp u}} + \mathbf{v_{\parallel u}} \enspace\mapsto\enspace \mathbf{v'} = \mathbf{v_{\perp u}} - \mathbf{v_{\parallel u}} = \mathbf{v} - 2 \cdot \mathbf{v_{\parallel u}}. \end{equation} - Per Definition sind $\mathbf{v_{\parallel \hat{n}}} = \mathbf{v_{\perp u}}$. In der geometrischen Algebra verwenden wir aber in den Formeln Vektoren, welche Spiegelachsen, nicht Spiegelebenen, repräsentieren. \end{definition} -Es scheint für diese Formel aber umständlich zu sein, weitere Spiegelungen mit weiteren Spiegelebenen anzufügen. Man kann diese Abbildung aber auch als Matrix schreiben. Sei $\mathbf{\hat{n}}$ ein Normalenvektor auf die Spiegelungs-Achse bzw. -Ebene, also $\mathbf{\hat{n}}\perp \mathbf{u}$, und sei ausserdem normiert $|\mathbf{\hat{n}}| = 1$, dann kann man die Spiegelung durch die Matrix +Es scheint für diese Formel \eqref{RefLinAlg} aber umständlich zu sein, weitere Spiegelungen mit weiteren Spiegelebenen anzufügen. Weil man $\mathbf{v_{\parallel u}}$ auch als Skalarprodukt $\mathbf{v_{\parallel u}} = \mathbf{\hat{u}} \cdot \mathbf{v}$ schreiben kann, ist es leicht diese Abbildung auch als Matrix darzustellen. Sei $\mathbf{\hat{u}}$ ein Normalenvektor auf die Spiegelungsebene, also $\mathbf{\hat{u}}\perp \sigma_u$, und sei ausserdem normiert $|\mathbf{\hat{u}}| = 1$, dann kann man die Spiegelung durch die Matrix \begin{align} - S = E - 2\dfrac{1}{|\mathbf{n}|^2}\mathbf{nn}^t + S = E - 2\mathbf{\hat{u}\hat{u}}^t \end{align} beschrieben werden. In der zweiten und dritten Dimension ergibt die Berechnung \begin{align} \label{Spiegelmatrizen} S_2 = \begin{pmatrix} - 1-2n_1^2 & -2n_1n_2 \\ - -2n_1n_2 & 1-2n_2^2 - \end{pmatrix} \quad + 1-2u_1^2 & -2u_1u_2 \\ + -2u_1u_2 & 1-2u_2^2 + \end{pmatrix}\enspace\text{und}\enspace S_3 = \begin{pmatrix} - 1-2n_1^2 & -2n_1n_2 & -2n_1n_3\\ - -2n_1n_2 & 1-2n_2^2 & -2n_2n_3\\ - -2n_1n_3 & -2n_2n_3 & 1-2n_3^2\\ + 1-2u_1^2 & -2u_1u_2 & -2u_1u_3\\ + -2u_1u_2 & 1-2u_2^2 & -2u_2u_3\\ + -2u_1u_3 & -2u_2u_3 & 1-2u_3^2\\ \end{pmatrix}. \end{align} -Diese Spiegelmatrizen gehören der orthogonalen Matrizengruppe $S\in \text{O}(n)$ an. Die Matrizengruppe $\text{O}(n)$ haben die Eigenschaft $S^t S = E$, was bedeutet, dass die Länge und Winkel bei der Abbildung beibehalten bleiben. Zusätzlich sind die Spiegelmatrizen symmetrisch, es gilt $S^t = S$. Somit liefert zweimal dieselbe Spiegelung wieder die identische Abbildung, wie man aus +Diese Spiegelmatrizen gehören der orthogonalen Matrizengruppe $S_n\in \text{O}(n)$ an. Die Matrizengruppe $\text{O}(n)$ haben die Eigenschaft $S_n^t S_n = E$, was bedeutet, dass die Länge und Winkel bei der Abbildung beibehalten bleiben. Zusätzlich sind die Spiegelmatrizen symmetrisch, es gilt $S_n^t = S_n$. Somit liefert zweimal dieselbe Spiegelung wieder die identische Abbildung, wie man aus \begin{align} - S^t S = S^2 = E + S_n^t S_n = S_n^2 = E \end{align} schliessen kann. \subsection{Geometrische Algebra} -Um die folgenden Formeln zu verstehen, definieren wir zuerst die Inverse eines Vektors, welche in dieser Form nicht in der linearen Algebra nicht existiert. +Wir definieren zuerst die Inverse eines Vektors, welche in dieser Form nicht in der linearen Algebra nicht existiert. \begin{definition} Die Inverse eines Vektors wird definiert als - \begin{align} - \mathbf{u}^{-1} = \dfrac{\mathbf{u}}{|\mathbf{u}|^2} \Rightarrow \mathbf{uu}^{-1} = \dfrac{\mathbf{u}^2}{|\mathbf{u}|^2} = 1. + \begin{align} \label{InverseGA} + \mathbf{u}^{-1} = \dfrac{\mathbf{u}}{|\mathbf{u}|^2}. \end{align} - Wie schon aus anderen algebraischen Strukturen bekannt, ergibt ein Element, hier $\mathbf{u}$, multipliziert mit dessen Inversen, hier $\mathbf{u}^{-1}$, das neutrale Element der Struktur, hier 1. \end{definition} +Diese Definition ist sinnvoll, da wegen $\mathbf{u}^2 = |\mathbf{u}|^2$ folgt +\begin{align} + \mathbf{uu}^{-1} = \mathbf{u} \frac{\mathbf{u}}{|\mathbf{u}|^2} = \frac{\mathbf{u}^2}{|\mathbf{u}|^2} = \frac{|\mathbf{u}|^2}{|\mathbf{u}|^2} = 1. +\end{align} +Der Vektor $\mathbf{u}^{-1}$ in \eqref{InverseGA} ist also tatsächlich das inverse Element im Sinne des Produktes in der geometrischen Algebra. Die geometrische Algebra leitet aus der obigen Formel \eqref{RefLinAlg} für eine Spiegelung eine einfache und intuitive Form her, welche auch für weitere Operationen erweitert werden kann. \begin{definition} - Die Spiegelungsgleichung in der geometrischen Algebra mit der Spiegelachse $\mathbf{u}$ ist definiert als + Die Abbildung der Spiegelung in der geometrischen Algebra mit dem senkrechten Vektor $\mathbf{u}$ zur Spiegelungsebene $\sigma_u$ ist \begin{align}\label{RefGA} - \mathbf{v}' = \mathbf{uvu}^{-1} + \mathbf{v} \enspace\mapsto\enspace \mathbf{v}' = -\mathbf{uvu}^{-1} \end{align} \end{definition} +Diese Abbildung muss stimmen, weil man durch die Schlussfolgerungen \eqref{uperpv} und \eqref{uparallelv} die Zusammenhänge +\begin{align} + \mathbf{uv_{\perp u}} = -\mathbf{v_{\perp u}u} \enspace\text{und}\enspace \mathbf{uv_{\parallel u}}=\mathbf{v_{\parallel u}u} +\end{align} +der geometrischen Produkte findet und somit die Abbildung aus der geometrischen Algebra \eqref{RefGA} wegen +\begin{align} + \mathbf{v}' = -\mathbf{uvu}^{-1} = -\mathbf{uv_{\perp u}u}^{-1} - \mathbf{uv_{\parallel u}u}^{-1} = -(-\mathbf{v_{\perp u}}\underbrace{\mathbf{u})\mathbf{u}^{-1}}_{1} -(\mathbf{v_{\parallel u}}\underbrace{\mathbf{u})\mathbf{u}^{-1}}_{1} = \mathbf{v_{\perp u}} - \mathbf{v_{\parallel u}} +\end{align} +gleichbedeutend zu der Definition \eqref{RefLinAlg} der Spiegelung ist. -verwendet man für $\mathbf{u}$ nur einen Einheitsvektor $\mathbf{\hat{u}}$, welcher die Länge 1 besitzt, wird die Gleichung zu +Verwendet man für $\mathbf{u}$ nur einen Einheitsvektor $\mathbf{\hat{u}}$, welcher die Länge 1 besitzt, wird die Gleichung \eqref{RefGA} zu \begin{align} - \mathbf{v'} = \mathbf{\hat{u}v\hat{u}} + \mathbf{v'} = -\mathbf{\hat{u}v\hat{u}} \end{align} vereinfacht. Im Gegensatz zu den Abbildungen in der linearen Algebra, welche in jeder anderen Dimension, durch andere Matrizen \eqref{Spiegelmatrizen} beschrieben werden müssen, ist es in der geometrischen Algebra immer der gleiche Vorgehensweise. Zudem ist diese kompakte Schreibweise in der linearen Algebra nicht möglich, da bis auf das Vektorprodukt in der dritten Dimension keine Multiplikation von Vektoren definiert ist.
\ No newline at end of file diff --git a/buch/papers/clifford/7_Spiegelung.tex b/buch/papers/clifford/7_Spiegelung.tex new file mode 100644 index 0000000..549848c --- /dev/null +++ b/buch/papers/clifford/7_Spiegelung.tex @@ -0,0 +1,95 @@ +% +% teil1.tex -- Beispiel-File für das Paper +% +% (c) 2020 Prof Dr Andreas Müller, Hochschule Rapperswil +% +\section{Spiegelung} +\rhead{Spiegelung} + +Die Spiegelung ist eine grundlegende, geometrische Operation, aus welcher man weitere Operationen, wie beispielsweise die später beschriebene Rotation, ableiten kann. Da die geometrische Algebra für geometrische Anwendungen ausgelegt ist, sollte die Spiegelung auch eine einfache, praktische Formulierung besitzen. +\begin{figure} + \centering + \begin{tikzpicture} + \draw[thin,gray!40] (-3,-1) grid (3,3); + \draw[<->] (-3,0)--(3,0) node[right]{$a_1$}; + \draw[<->] (0,-1)--(0,3) node[above]{$a_2$}; + \draw[blue, line width=1.0pt] (0,3)--(0,-1) node[anchor=south east]{$\sigma_u$}; + \draw[line width=2pt,black,-stealth](0,0)--(2,2) node[anchor=south east]{$\boldsymbol{v}$}; + \draw[line width=2pt,black,-stealth](0,0)--(-2,2) node[anchor=south east]{$\boldsymbol{v'}$}; + \draw[line width=1.5pt,gray,-stealth](0,0)--(1,0) node[anchor=north]{$\boldsymbol{e_1}$}; + \draw[line width=1.5pt,gray,-stealth](0,0)--(0,1) node[anchor=north east]{$\boldsymbol{e_2}$}; + \draw[line width=1.5pt,red,-stealth](0,2)--(2,2) node[xshift=-1cm, yshift= + 0.25cm]{$\boldsymbol{v_{\perp u}}$}; + \draw[line width=1.5pt,red,-stealth](-2,2)--(0,2) node[xshift=-1cm, yshift= + 0.25cm]{$\boldsymbol{v_{\perp u}}$}; + \draw[line width=1.5pt,blue,-stealth](0,0.05)--(1,0.05) node[xshift=-0.5cm, yshift=-0.25cm]{$\boldsymbol{\hat{u}}$}; + \end{tikzpicture} + \caption{Spiegelung des Vektors $\mathbf{v}$ an der Spiegelebene $\sigma_u$ mit dem Normalenvektor $\mathbf{\hat{u}}$} + \label{BildSpiegelung} +\end{figure} + +\subsection{Linearen Algebra} +Aus der linearen Algebra ist bekannt, dass man eine Spiegelung an einer Ebene wie folgt beschreiben kann. +\begin{definition} + Die Abbildung der Spiegelung in der linearen Algebra mit dem Normalenvektor $\mathbf{\hat{u}}$ zur Spiegelebene ist + \begin{equation} \label{RefLinAlg} + \mathbf{v} = \mathbf{v_{\perp u}} + \mathbf{v_{\parallel u}} \enspace\mapsto\enspace \mathbf{v'} = \mathbf{v_{\perp u}} - \mathbf{v_{\parallel u}} = \mathbf{v} - 2 \cdot \mathbf{v_{\parallel u}}. + \end{equation} +\end{definition} +Es scheint für diese Formel \eqref{RefLinAlg} aber umständlich zu sein, weitere Spiegelungen mit weiteren Spiegelebenen anzufügen. Weil man $\mathbf{v_{\parallel u}}$ auch als Skalarprodukt $\mathbf{v_{\parallel u}} = \mathbf{\hat{u}} \cdot \mathbf{v}$ schreiben kann, ist es leicht diese Abbildung auch als Matrix darzustellen. Sei $\mathbf{\hat{u}}$ ein Normalenvektor auf die Spiegelungsebene, also $\mathbf{\hat{u}}\perp \sigma_u$, und sei ausserdem normiert $|\mathbf{\hat{u}}| = 1$, dann kann man die Spiegelung durch die Matrix +\begin{align} + S = E - 2\mathbf{\hat{u}\hat{u}}^t +\end{align} +beschrieben werden. In der zweiten und dritten Dimension ergibt die Berechnung +\begin{align} \label{Spiegelmatrizen} + S_2 = \begin{pmatrix} + 1-2u_1^2 & -2u_1u_2 \\ + -2u_1u_2 & 1-2u_2^2 + \end{pmatrix}\enspace\text{und}\enspace + S_3 = \begin{pmatrix} + 1-2u_1^2 & -2u_1u_2 & -2u_1u_3\\ + -2u_1u_2 & 1-2u_2^2 & -2u_2u_3\\ + -2u_1u_3 & -2u_2u_3 & 1-2u_3^2\\ + \end{pmatrix}. +\end{align} +Diese Spiegelmatrizen gehören der orthogonalen Matrizengruppe $S_n\in \text{O}(n)$ an. Die Matrizengruppe $\text{O}(n)$ haben die Eigenschaft $S_n^t S_n = E$, was bedeutet, dass die Länge und Winkel bei der Abbildung beibehalten bleiben. Zusätzlich sind die Spiegelmatrizen symmetrisch, es gilt $S_n^t = S_n$. Somit liefert zweimal dieselbe Spiegelung wieder die identische Abbildung, wie man aus +\begin{align} + S_n^t S_n = S_n^2 = E +\end{align} +schliessen kann. + +\subsection{Geometrische Algebra} +Wir definieren zuerst die Inverse eines Vektors, welche in dieser Form nicht in der linearen Algebra nicht existiert. +\begin{definition} + Die Inverse eines Vektors wird definiert als + \begin{align} \label{InverseGA} + \mathbf{u}^{-1} = \dfrac{\mathbf{u}}{|\mathbf{u}|^2}. + \end{align} +\end{definition} +Diese Definition ist sinnvoll, da wegen $\mathbf{u}^2 = |\mathbf{u}|^2$ folgt +\begin{align} + \mathbf{uu}^{-1} = \mathbf{u} \frac{\mathbf{u}}{|\mathbf{u}|^2} = \frac{\mathbf{u}^2}{|\mathbf{u}|^2} = \frac{|\mathbf{u}|^2}{|\mathbf{u}|^2} = 1. +\end{align} +Der Vektor $\mathbf{u}^{-1}$ in \eqref{InverseGA} ist also tatsächlich das inverse Element im Sinne des Produktes in der geometrischen Algebra. +Die geometrische Algebra leitet aus der obigen Formel \eqref{RefLinAlg} für eine Spiegelung eine einfache und intuitive Form her, welche auch für weitere Operationen erweitert werden kann. +\begin{definition} + Die Abbildung der Spiegelung in der geometrischen Algebra mit dem senkrechten Vektor $\mathbf{u}$ zur Spiegelungsebene $\sigma_u$ ist + \begin{align}\label{RefGA} + \mathbf{v} \enspace\mapsto\enspace \mathbf{v}' = -\mathbf{uvu}^{-1} + \end{align} +\end{definition} +Diese Abbildung muss stimmen, weil man durch die Schlussfolgerungen \eqref{uperpv} und \eqref{uparallelv} die Zusammenhänge +\begin{align} + \mathbf{uv_{\perp u}} = -\mathbf{v_{\perp u}u} \enspace\text{und}\enspace \mathbf{uv_{\parallel u}}=\mathbf{v_{\parallel u}u} +\end{align} +der geometrischen Produkte findet und somit die Abbildung aus der geometrischen Algebra \eqref{RefGA} wegen +\begin{align} + \mathbf{v}' = -\mathbf{uvu}^{-1} = -\mathbf{uv_{\perp u}u}^{-1} - \mathbf{uv_{\parallel u}u}^{-1} = -(-\mathbf{v_{\perp u}}\underbrace{\mathbf{u})\mathbf{u}^{-1}}_{1} -(\mathbf{v_{\parallel u}}\underbrace{\mathbf{u})\mathbf{u}^{-1}}_{1} = \mathbf{v_{\perp u}} - \mathbf{v_{\parallel u}} +\end{align} +gleichbedeutend zu der Definition \eqref{RefLinAlg} der Spiegelung ist. + +Verwendet man für $\mathbf{u}$ nur einen Einheitsvektor $\mathbf{\hat{u}}$, welcher die Länge 1 besitzt, wird die Gleichung \eqref{RefGA} zu +\begin{align} + \mathbf{v'} = -\mathbf{\hat{u}v\hat{u}} +\end{align} +vereinfacht. Im Gegensatz zu den Abbildungen in der linearen Algebra, welche in jeder anderen Dimension, durch andere Matrizen \eqref{Spiegelmatrizen} beschrieben werden müssen, ist es in der geometrischen Algebra immer der gleiche Vorgehensweise. Zudem ist diese kompakte Schreibweise in der linearen Algebra nicht möglich, da bis auf das Vektorprodukt in der dritten Dimension keine Multiplikation von Vektoren definiert ist.
\ No newline at end of file diff --git a/buch/papers/clifford/8_Rotation.tex b/buch/papers/clifford/8_Rotation.tex index 6a3251a..1d5e889 100644 --- a/buch/papers/clifford/8_Rotation.tex +++ b/buch/papers/clifford/8_Rotation.tex @@ -6,7 +6,7 @@ \section{Rotation} \rhead{Rotation} -Eine Rotation kann man aus zwei aufeinanderfolgenden Spiegelungen bilden. Das wird für einige zuerst eine verwirrende Aussage sein, da man aus den vorherig gezeigten Formeln annehmen könnte, dass die Spiegelung schon für eine Drehung ausreicht. Obwohl sich die Längen, Winkel und Volumen sich bei einer Spiegelung, wie bei einer Rotation, nicht ändert, sind sie doch verschieden, da die Orientierung bei der Spiegelung invertiert wird. Stellt man sich beispielsweise ein Objekt im Dreidimensionalen vor und spiegelt dieses an einer Fläche, dann ist es unmöglich nur durch eine Rotation (egal an welchem Punkt) das ursprüngliche Objekt deckungsgleich auf das Gespiegelte zu drehen. Hingegen ist es wiederum möglich ein zweifach gespiegeltes Objekt durch eine Drehung zu erreichen. Das liegt daran, da die Orientierung zweimal invertiert wurde. +Eine Rotation kann man aus zwei aufeinanderfolgenden Spiegelungen bilden. Das kann vielleicht zuerst eine verwirrende Aussage sein, da man aus den vorherig gezeigten Formeln annehmen könnte, dass die Spiegelung schon für eine Drehung ausreicht. Obwohl sich die Längen, Winkel und Volumen sich bei einer Spiegelung, wie bei einer Rotation, nicht ändert, sind sie doch verschieden, da die Orientierung bei der Spiegelung invertiert wird. Stellt man sich beispielsweise ein Objekt im Dreidimensionalen vor und spiegelt dieses an einer Fläche, dann ist es unmöglich nur durch eine Rotation (egal an welchem Punkt) das ursprüngliche Objekt deckungsgleich auf das Gespiegelte zu drehen. Hingegen ist es wiederum möglich ein zweifach gespiegeltes Objekt durch eine Drehung zu erreichen. Das liegt daran, da die Orientierung zweimal invertiert wurde. \\(Hier wird noch ein Bild für das Verständnis eingefügt) \begin{figure} @@ -15,19 +15,28 @@ Eine Rotation kann man aus zwei aufeinanderfolgenden Spiegelungen bilden. Das wi \draw[thin,gray!40] (-3,-1) grid (3,3); \draw[<->] (-3,0)--(3,0) node[right]{$a_1$}; \draw[<->] (0,-1)--(0,3) node[above]{$a_2$}; + \draw[line width=1.0pt,green,-stealth](2,2)--(-2,2) node[anchor=south west]{$\boldsymbol{-2v_{\parallel u}}$}; + \draw[line width=1.0pt,green,-stealth](-2,2)--(-2.828,0) node[anchor=north west]{$\boldsymbol{-2v'_{\parallel w}}$}; + \draw[blue, line width=1.0pt] (0,3)--(0,-1) node[anchor=south east]{$\sigma_u$}; + \draw[red, line width=1.0pt] (-3,1.24)--(2.21,-1) node[anchor=south]{$\sigma_w$}; \draw[line width=2pt,black,-stealth](0,0)--(2,2) node[anchor=south east]{$\boldsymbol{v}$}; - \draw[line width=1.5pt,blue,-stealth](0,0)--(0,2.5) node[anchor=south east]{$\boldsymbol{u}$}; + \draw[line width=1.5pt,blue,-stealth](0,0)--(2.5, 0) node[anchor=south east]{$\boldsymbol{u}$}; \draw[line width=2pt,black,-stealth](0,0)--(-2,2) node[anchor=south east]{$\boldsymbol{v'}$}; - \draw[line width=1.5pt,red,-stealth](0,0)--(-2.31, 0.957) node[anchor=south east]{$\boldsymbol{w}$}; + \draw[line width=1.5pt,red,-stealth](0,0)--(0.957, 2.31) node[anchor=south east]{$\boldsymbol{w}$}; \draw[line width=2pt,black,-stealth](0,0)--(-2.828,0) node[anchor=south east]{$\boldsymbol{v''}$}; \draw[line width=1.5pt,gray,-stealth](0,0)--(1,0) node[anchor=north]{$\boldsymbol{e_1}$}; - \draw[line width=1.5pt,gray,-stealth](0,0)--(0,1) node[anchor=north west]{$\boldsymbol{e_2}$}; + \draw[line width=1.5pt,gray,-stealth](0,0)--(0,1) node[anchor=north east]{$\boldsymbol{e_2}$}; \coordinate (A) at (0,0); - \coordinate (B) at (0,2.5); - \coordinate (C) at (-2.31, 0.957); - \tikzset{anglestyle/.style={angle eccentricity=1.25, draw, thick, angle radius=1.25cm}} + \coordinate (B) at (2.5,0); + \coordinate (C) at (0.957, 2.31); + \tikzset{anglestyle/.style={angle eccentricity=1.25, purple, draw, thick, angle radius=1cm}} \draw pic ["$\theta$", anglestyle] {angle = B--A--C}; + \coordinate (D) at (0,0); + \coordinate (E) at (1,1); + \coordinate (F) at (-1, 0); + \tikzset{anglestyle/.style={angle eccentricity=1.25, purple, draw, thick, angle radius=1.25cm}} + \draw pic ["$2\theta$", anglestyle] {angle = E--D--F}; \end{tikzpicture} \caption{Rotation des Vektors $\textbf{v}$ um $2\theta$} \label{BildRotation} @@ -49,76 +58,84 @@ Diese Drehmatrizen gehören der speziellen orthogonalen Matrizengruppe $D\in \te \subsection{Geometrische Algebra} Da wir jetzt aus der Geometrie wissen, dass eine Rotation durch zwei Spiegelungen gebildet werden kann, können wir die Rotation mit der Formel \eqref{RefGA} einfach herleiten. \begin{satz} - Eine Rotation + Durch zwei nacheinander auf einen Vektor $\mathbf{v}$ angewendete Spiegelungen lässt sich eine Rotation \begin{align} \label{rotGA} - \mathbf{v}'' = \mathbf{wv}'\mathbf{w}^{-1} = \mathbf{w}(\mathbf{uvu}^{-1})\mathbf{w}^{-1} = (\mathbf{wu})\mathbf{v}(\mathbf{u}^{-1}\mathbf{w}^{-1}) + \mathbf{v}'' = -\mathbf{wv}'\mathbf{w}^{-1} = -\mathbf{w}(-\mathbf{uvu}^{-1})\mathbf{w}^{-1} = (\mathbf{wu})\mathbf{v}(\mathbf{u}^{-1}\mathbf{w}^{-1}) \end{align} - lässt sich durch zwei nacheinander auf einen Vektor $\mathbf{v}$ angewendete Spiegelungen beschreiben. + beschreiben. \end{satz} Die Vektoren $\mathbf{w}$ und $\mathbf{u}$ bilden hier wiederum die Spiegelachsen. Diese Formel versuchen wir jetzt noch durch Umstrukturierung zu verbessern. \subsubsection{Exponentialform} -Dazu leiten wir zuerst die Exponentialform eines Vektors her. Es wird dabei zur Vereinfachung davon ausgegangen, dass alle Vektoren $\mathbf{w}, \mathbf{u}, \mathbf{v}$ in der $\mathbf{e}_{12}$ Ebene liegen. Weitere Drehungen können in höheren Dimensionen durch Linearkombinationen von Drehungen in den $\mathbf{e}_{ij}, i\not=j$ Ebenen erreicht werden. Für die Herleitung erweitern wir nun als erstes die Polarform +Dazu leiten wir zuerst die Exponentialform eines Vektors her. Es wird dabei zur Vereinfachung davon ausgegangen, dass alle Vektoren $\mathbf{w}, \mathbf{u}, \mathbf{v}$ in der $\mathbf{e}_{1}$-$\mathbf{e}_{2}$-Ebene liegen. Weitere Drehungen können in höheren Dimensionen durch Linearkombinationen von Drehungen in den $\mathbf{e}_{i}$-$\mathbf{e}_{j}$-Ebenen $(i\not=j)$ erreicht werden. Für die Herleitung ersetzen wir als erstes in der Polarform \begin{align} \mathbf{w} = |\mathbf{w}| \left(\cos(\theta_w) \mathbf{e}_1 + \sin(\theta_w) \mathbf{e}_2\right) \end{align} -eines Vektors mit $\mathbf{e}_1^2 = 1$ beim Sinus +eines Vektors einen Faktor 1 durch $1=\mathbf{e}_1^2$ und erhalten beim Sinus \begin{align}\label{e1ausklammern} - \mathbf{w} &= |\mathbf{w}| \left(\cos(\theta_w) \mathbf{e}_1 + \sin(\theta_w) \mathbf{e}_1\mathbf{e}_1\mathbf{e}_2\right), + \mathbf{w} &= |\mathbf{w}| \left(\cos(\theta_w) \mathbf{e}_1 + \sin(\theta_w) \mathbf{e}_1\mathbf{e}_1\mathbf{e}_2\right). \end{align} -um dann $\mathbf{e}_1$ +In einem zweiten Schritt klammern wir $\mathbf{e}_1$ aus, dies ergibt \begin{align} - \mathbf{w} = |\mathbf{w}|\mathbf{e}_1\left(\cos(\theta_w)+ \sin(\theta_w) \mathbf{e}_{12}\right) \label{ExponentialGA} + \mathbf{w} = |\mathbf{w}|\mathbf{e}_1\left(\cos(\theta_w)+ \sin(\theta_w) \mathbf{e}_{12}\right). \label{ExponentialGA} \end{align} -ausklammern zu können. Die Ähnlichkeit des Klammerausdrucks zu der Eulerschen Formel bei den Komplexen Zahlen ist nun schon gut erkennbar. Versuchen wir nun mithilfe der Reihenentwicklungen +Die Ähnlichkeit des Klammerausdrucks in der Formel \eqref{ExponentialGA} zu der Eulerschen Formel bei den komplexen Zahlen ist nun schon gut erkennbar. Versuchen wir nun mithilfe der Reihenentwicklungen \begin{align} \sin(\theta_w)\mathbf{e}_{12}&=\sum _{n=0}^{\infty }(-1)^{n}{\frac {\theta_w^{2n+1}}{(2n+1)!}}\mathbf{e}_{12} =\theta_w\mathbf{e}_{12}-{\frac {\theta_w^{3}}{3!}}\mathbf{e}_{12}+{\frac {\theta_w^{5}}{5!}}\mathbf{e}_{12}-\cdots \\ \cos(\theta_w)&=\sum _{n=0}^{\infty }(-1)^{n}{\frac {\theta_w^{2n}}{(2n)!}} =1-{\frac {\theta_w^{2}}{2!}}+{\frac {\theta_w^{4}}{4!}}-\cdots \end{align} -den Zusammenhang auch hier herzustellen. Verwenden wir jetzt noch die Eigenschaft, dass $\mathbf{e}_{12}^2=-1, \enspace\mathbf{e}_{12}^3=-\mathbf{e}_{12}, \dots$, bei dem Klammerausdruck in Formel \eqref{ExponentialGA} +diesen Zusammenhang auch hier herzustellen. Setzt man diese beiden Reihenentwicklungen in \eqref{ExponentialGA} ein, erhält man \begin{align} - \cos(\theta_w)+ \sin(\theta_w) \mathbf{e}_{12} &= 1+\theta_w\mathbf{e}_{12}-{\frac {\theta_w^{2}}{2!}}-{\frac {\theta_w^{3}}{3!}}\mathbf{e}_{12}+{\frac {\theta_w^{4}}{4!}}+{\frac {\theta_w^{5}}{5!}}\mathbf{e}_{12}-\cdots\\ - &= 1 \mathbf{e}_{12}^0+\theta_w\mathbf{e}_{12}^1+{\frac {\theta_w^{2}}{2!}}\mathbf{e}_{12}^2+{\frac {\theta_w^{3}}{3!}}\mathbf{e}_{12}^3+{\frac {\theta_w^{4}}{4!}}\mathbf{e}_{12}^4+{\frac {\theta_w^{5}}{5!}}\mathbf{e}_{12}^5+\cdots + \cos(\theta_w)+ \sin(\theta_w) \mathbf{e}_{12} &= 1+\theta_w\mathbf{e}_{12}-{\frac {\theta_w^{2}}{2!}}-{\frac {\theta_w^{3}}{3!}}\mathbf{e}_{12}+{\frac {\theta_w^{4}}{4!}}+{\frac {\theta_w^{5}}{5!}}\mathbf{e}_{12}-\cdots +\end{align} +Dies sieht noch nicht wie eine Exponentialreihe aus, da $\mathbf{e}_{12}$ nur in jedem zweiten Term auftritt. Da aber $\mathbf{e}_{12}=-1$ gibt, erhält man für +\begin{align} + e^{\theta_w\mathbf{e}_{12}} = 1 \mathbf{e}_{12}^0+\theta_w\mathbf{e}_{12}^1+{\frac {\theta_w^{2}}{2!}}\mathbf{e}_{12}^2+{\frac {\theta_w^{3}}{3!}}\mathbf{e}_{12}^3+{\frac {\theta_w^{4}}{4!}}\mathbf{e}_{12}^4+{\frac {\theta_w^{5}}{5!}}\mathbf{e}_{12}^5+\cdots \label{ExponentialGA2} \end{align} -dann sieht man die Übereinstimmung mit der Reihenentwicklung der Exponentialfunktion +Man sieht, dass die beiden Reihen übereinstimmen. Es folgt somit +\begin{align}\label{EulerGA} + e^{\theta_w \mathbf{e}_{12}} = \cos(\theta_w)+ \sin(\theta_w) \mathbf{e}_{12}, +\end{align} +es gibt eine Euler-Formel mit $\mathbf{e}_{12}$ anstelle der imaginären Einheit $j$. + +Wenn man jetzt den Vektor \eqref{ExponentialGA} durch die eulersche Schreibweise \begin{align} - &e^{\theta_w\mathbf{e}_{12}}=\sum _{n=0}^{\infty }{\frac {(\theta_w\mathbf{e}_{12})^{n}}{n!}}={\frac {(\theta_w\mathbf{e}_{12})^{0}}{0!}}+{\frac {(\theta_w\mathbf{e}_{12})^{1}}{1!}}+{\frac {(\theta_w\mathbf{e}_{12})^{2}}{2!}}+{\frac {(\theta_w\mathbf{e}_{12})^{3}}{3!}}+\cdots\\ - &\Rightarrow \mathbf{w} = |w|\mathbf{e}_1 e^{\theta_w \mathbf{e}_{12}} = |w|\mathbf{e}_1\left(\cos(\theta_w)+ \sin(\theta_w) \mathbf{e}_{12}\right). + \mathbf{w} = |\mathbf{w}|\mathbf{e}_1e^{\theta_w\mathbf{e}_{12}} \end{align} -Man kann die Exponentialform des Vektors ähnlich wie die der komplexen Zahlen interpretieren. Der Einheitsvektor $\mathbf{e}_1$ wird um die Länge $|\mathbf{w}|$ gestreckt und um $\theta_w$ gedreht. -Bei den komplexen Zahlen würden man vom Punkt 1 anstatt $\mathbf{e}_1$ ausgehen. +ersetzt, kann die Exponentialform des Vektors ähnlich wie die der komplexen Zahlen interpretieren. Der Einheitsvektor $\mathbf{e}_1$ wird um die Länge $|\mathbf{w}|$ gestreckt und um $\theta_w$ gedreht. \subsubsection{Vektormultiplikation} -Nun werden wir das Produkt von zwei Vektoren $\mathbf{wu}$ -\begin{align} +Nun werden wir das Vektorprodukt +\begin{align} \label{VektorproduktformelGA} \mathbf{wu} = |\mathbf{w}|\mathbf{e}_1 e^{\theta_w \mathbf{e}_{12}}|\mathbf{u}|\mathbf{e}_1 e^{\theta_u \mathbf{e}_{12}} \end{align} -so umformen, dass wir eine bessere Darstellung erhalten. Wir tauschen dafür zuerst beim Vektor $\mathbf{w}$ die Reihenfolge von -$\mathbf{e}_1$ mit dem Exponentialterm $e^{\theta_w \mathbf{e}_{12}}$, indem wir bei der Gleichung \eqref{e1ausklammern}, anstatt mit $\mathbf{e}_1\mathbf{e}_1\mathbf{e}_2$ mit $\mathbf{e}_2\mathbf{e}_1\mathbf{e}_1$ erweitern +so umformen, dass wir die Drehung nur durch Exponentialterme beschreiben können. Wir tauschen dafür zuerst beim Vektor $\mathbf{w}$ die Reihenfolge von +$\mathbf{e}_1$ mit dem Exponentialterm $e^{\theta_w \mathbf{e}_{12}}$, indem wir bei der Gleichung \eqref{e1ausklammern} $1=\mathbf{e}_1^2$ an einer anderen Position \begin{align} - \mathbf{w} &= |\mathbf{w}|\left(\cos(\theta_w)+ \sin(\theta_w) \mathbf{e}_2\mathbf{e}_1\right)\mathbf{e}_1\\ - &= |\mathbf{w}|e^{\theta_w \mathbf{e}_{21}}\mathbf{e}_1\\ - &= |\mathbf{w}|e^{-\theta_w \mathbf{e}_{12}}\mathbf{e}_1 + \mathbf{w} &= |\mathbf{w}|\left(\cos(\theta_w)+ \sin(\theta_w) \mathbf{e}_2\mathbf{e}_1\right)\mathbf{e}_1 +\end{align} +einsetzten. Mithilfe der Formel \eqref{EulerGA} und dem Wissen, dass $\mathbf{e}_{21}= -\mathbf{e}_{12}$ können wir die Umformung +\begin{align} + |\mathbf{w}|e^{-\theta_w \mathbf{e}_{12}}\mathbf{e}_1 \end{align} -und umstrukturiert wieder in die Vektorproduktformel einsetzen +ausführen. Diese wichtige Umstrukturierung können wir wieder in die Vektorproduktformel \eqref{VektorproduktformelGA} einsetzen un erhalten \begin{align} - \mathbf{wu} = |\mathbf{w}||\mathbf{u}|e^{-\theta_w \mathbf{e}_{12}}\mathbf{e}_1\mathbf{e}_1 e^{\theta_u \mathbf{e}_{12}}\\ - \mathbf{wu} = |\mathbf{w}||\mathbf{u}|e^{(\theta_u-\theta_w) \mathbf{e}_{12}}. + \mathbf{wu} &= |\mathbf{w}||\mathbf{u}|e^{-\theta_w \mathbf{e}_{12}}\mathbf{e}_1\mathbf{e}_1 e^{\theta_u \mathbf{e}_{12}}\\ + &= |\mathbf{w}||\mathbf{u}|e^{(\theta_u-\theta_w) \mathbf{e}_{12}}. \end{align} -Der Term $\mathbf{u}^{-1}\mathbf{w}^{-1}$ +Das inverse Vektorprodukt \begin{align} \mathbf{u}^{-1}\mathbf{w}^{-1} = \dfrac{1}{|\mathbf{w}||\mathbf{u}|}e^{(\theta_w-\theta_u) \mathbf{e}_{12}} \end{align} -kann durch die selbe Methode zusammengefasst werden. -Wenn wir den Winkel zwischen den Vektoren $\mathbf{w}$ und $\mathbf{u}$ als $\theta = \theta_w - \theta_u$ definieren erhalten wir +kann durch die selbe Methode vereinfacht werden. +Wenn wir den Winkel zwischen den Vektoren $\mathbf{w}$ und $\mathbf{u}$ als $\theta = \theta_w - \theta_u$ definieren erhalten wir als endgültige Form der Vektorprodukte \begin{align}\label{wuExpo} - \mathbf{wu} = |\mathbf{w}||\mathbf{u}|e^{-\theta \mathbf{e}_{12}}\\ - \mathbf{u}^{-1}\mathbf{w}^{-1} = \dfrac{1}{|\mathbf{w}||\mathbf{u}|}e^{\theta \mathbf{e}_{12}} \label{wuExpoInv} + \mathbf{wu} &= |\mathbf{w}||\mathbf{u}|e^{-\theta \mathbf{e}_{12}}\enspace\text{und}\\ + \mathbf{u}^{-1}\mathbf{w}^{-1} &= \dfrac{1}{|\mathbf{w}||\mathbf{u}|}e^{\theta \mathbf{e}_{12}} \label{wuExpoInv}. \end{align} -die finale Form der Vektorprodukte. \subsubsection{Umstrukturierte Drehungsgleichung} Setzten wir nun unsere neuen Erkenntnisse in die Gleichung \eqref{rotGA} ein \begin{align} - \mathbf{v''} = (|\mathbf{w}||\mathbf{u}|e^{-\theta \mathbf{e}_{12}}) \mathbf{v}( \dfrac{1}{|\mathbf{w}||\mathbf{u}|}e^{\theta \mathbf{e}_{12}}), + \mathbf{v''} = (|\mathbf{w}||\mathbf{u}|e^{-\theta \mathbf{e}_{12}})\mathbf{v}\biggl(\dfrac{1}{|\mathbf{w}||\mathbf{u}|}e^{\theta \mathbf{e}_{12}}\biggr), \end{align} erhalten wir durch die Kürzungen der Längen die vereinfachte Drehungsgleichung \begin{align} @@ -145,12 +162,12 @@ kann man sehen, dass nur der parallele Anteil $\mathbf{v_\parallel}$ des Vektors \end{align} und das Produkt der Inversen $\mathbf{u}^{-1}\mathbf{w}^{-1}$ \begin{align} - \mathbf{u}^{-1}\mathbf{w}^{-1} = (\dfrac{\mathbf{e}_1}{1^2})(\dfrac{2\mathbf{e}_2}{2^2}) = \dfrac{1}{2}\mathbf{e}_{12}. + \mathbf{u}^{-1}\mathbf{w}^{-1} = \biggl(\dfrac{\mathbf{e}_1}{1^2}\biggr) \left(\dfrac{2\mathbf{e}_2}{2^2}\right) = \dfrac{1}{2}\mathbf{e}_{12}. \end{align} Der rotierte Vektor $\mathbf{v}''$ können wir nun durch das einsetzten und auflösen der Produkte in die Gleichung \eqref{rotGA} \begin{align} - \mathbf{v}'' = (\mathbf{wu})\mathbf{v}(\mathbf{u}^{-1}\mathbf{w}^{-1}) &= (-2e_{12})(1\mathbf{e}_1 + \mathbf{e}_2 + 1\mathbf{e}_3)(\dfrac{1}{2}\mathbf{e}_{12})\\ - &= (2\mathbf{e}_2-2\mathbf{e}_1-2\mathbf{e}_{123})(\dfrac{1}{2}\mathbf{e}_{12})\\ + \mathbf{v}'' = (\mathbf{wu})\mathbf{v}(\mathbf{u}^{-1}\mathbf{w}^{-1}) &= (-2e_{12})(1\mathbf{e}_1 + \mathbf{e}_2 + 1\mathbf{e}_3)(\textstyle{\frac{1}{2}}\mathbf{e}_{12})\\ + &= (2\mathbf{e}_2-2\mathbf{e}_1-2\mathbf{e}_{123})(\textstyle{\frac{1}{2}}\mathbf{e}_{12})\\ &= -1\mathbf{e}_1 - 1\mathbf{e}_2 + 1\mathbf{e}_3 \end{align} finden. Aus dem Resultat $\mathbf{v}''= -1\mathbf{e}_1 + 1\mathbf{e}_2 + 1\mathbf{e}_3$ können wir bestätigen, dass @@ -159,11 +176,11 @@ kann man sehen, dass nur der parallele Anteil $\mathbf{v_\parallel}$ des Vektors \item sich der parallele Anteil $\mathbf{v_\parallel}'' = -1\mathbf{e}_1 - 1\mathbf{e}_2$ gedreht hat und der senkrechte Anteil $\mathbf{v_\perp}'' = 1\mathbf{e}_3$ unverändert blieb. \item der parallele Teil sich genau um $2\theta=180$° gedreht hat. $\theta$ kann übrigens durch die Umformung des Produkt $\mathbf{wu}$ in die Exponentialschreibweise \begin{align} - &\mathbf{wu} = -2\mathbf{e}_{12} = 2(0-1\mathbf{e}_{12})=2(\cos(\dfrac{-\pi}{2} + \sin(\dfrac{-\pi}{2})\mathbf{e}_{12})) = 2e^{(-\pi/2)\mathbf{e}_{12}} + &\mathbf{wu} = -2\mathbf{e}_{12} = 2(0-1\mathbf{e}_{12})=2(\cos\biggl(\dfrac{-\pi}{2}\biggr) + \sin\biggl(\dfrac{-\pi}{2}\biggr)\mathbf{e}_{12}) = 2e^{(-\pi/2)\mathbf{e}_{12}} \end{align} durch einen Vergleich mir der Formel \eqref{wuExpo} \begin{align} - \theta = -(\dfrac{-\pi}{2}) = \dfrac{\pi}{2} + \theta = -\biggl(\dfrac{-\pi}{2}\biggr) = \dfrac{\pi}{2} \end{align} ausgelesen werden. \end{itemize} diff --git a/buch/papers/clifford/9_KomplexeZahlen.tex b/buch/papers/clifford/9_KomplexeZahlen.tex index 70107da..aaccd3d 100644 --- a/buch/papers/clifford/9_KomplexeZahlen.tex +++ b/buch/papers/clifford/9_KomplexeZahlen.tex @@ -6,23 +6,34 @@ \section{Komplexe Zahlen} \rhead{Komplexe Zahlen} -Die komplexen Zahlen finden eine Vielzahl von Anwendungsgebiete in den Ingenieurwissenschaften. Das liegt daran, weil die komplexen Zahlen Rotationen und Schwingungen gut beschreiben können. Nach dem vorherigen Kapitel überrascht es wahrscheinlich nicht viele, dass es möglich ist komplexe Zahlen in der geometrischen Algebra darzustellen. Sie können durch die geraden Grade der 2 Dimensionalen geometrischen Algebra vollständig beschrieben werden: $\mathbf{g}_n \in G_2^+(\mathbb{R}) \cong \mathbb{C}$. Das bedeutet eine komplexe Zahl kann durch ein Skalar (Grad 0) und einem Bivektor (Grad 2) dargestellt werden +Die komplexen Zahlen finden eine Vielzahl von Anwendungsgebiete in den Ingenieurwissenschaften. Das liegt daran, weil die komplexen Zahlen Rotationen und Schwingungen gut beschreiben können. Nach dem vorherigen Kapitel überrascht es wahrscheinlich nicht viele, dass es möglich ist komplexe Zahlen in der geometrischen Algebra darzustellen. Sie können durch die geraden Grade der zweidimensionalen geometrischen Algebra vollständig beschrieben werden: $\mathbf{g}_n \in G_2^+(\mathbb{R}) \cong \mathbb{C}$. Das bedeutet eine komplexe Zahl \begin{align} a_0 + a_1 j \cong a_0 + a_1 \mathbf{e}_{12} = \mathbf{g}_n\quad a_0, a_1 \in \mathbb{R}\\ |r|e^{\theta j} \cong |r|e^{\theta \mathbf{e}_{12}} = \mathbf{g}_n; \quad r, \theta \in \mathbb{R} \end{align} -weil $j$ und $\mathbf{e}_{12}$ beide die Eigenschaft besitzen quadriert $-1$ zu ergeben +kann durch ein Skalar (Grad 0) und einem Bivektor (Grad 2) dargestellt werden, weil $j$ und $\mathbf{e}_{12}$ beide die Eigenschaft \begin{align} j^2 = -1\quad \mathbf{e}_{12}^2 = -1 \end{align} -Man beachte, dass wenn wir, wie bei den komplexen Zahlen, Elemente von $G_2^+(\mathbb{R})$ miteinander Multiplizieren, ist es nicht, wie im Kapitel Rotation bei der Formel (\ref{rotGA})beschrieben, eine Multiplikation von zwei $g_n$ mit einem Vektor. Im zweidimensionalen bewirken beide Multiplikationen grundsätzlich das Gleiche (eine Drehstreckung), aber die Multiplikation von mehreren $g_n$ ist kommutativ, wie wir es von den komplexen Zahlen kennen. +besitzen. Die Kommutativität \begin{align} - \mathbf{g}_1\mathbf{g}_2 = \mathbf{g}_2\mathbf{g}_1 \quad&\Leftrightarrow\quad (a + b \mathbf{e}_{12})(f + g \mathbf{e}_{12}) = (f + g \mathbf{e}_{12})(a + b \mathbf{e}_{12})\\ - \mathbf{g}_1\mathbf{v}\not= \mathbf{v}\mathbf{g}_1 \quad&\Leftrightarrow\quad(a + b \mathbf{e}_{12})(x\mathbf{e}_1+y\mathbf{e}_2)\not= (x\mathbf{e}_1+y\mathbf{e}_2)(a + b \mathbf{e}_{12}) + \begin{split} + \mathbf{g}_1\mathbf{g}_2 = \mathbf{g}_2\mathbf{g}_1 \enspace&\Leftrightarrow\enspace (a + b \mathbf{e}_{12})(f + g \mathbf{e}_{12}) = (f + g \mathbf{e}_{12})(a + b \mathbf{e}_{12})\\ &\Leftrightarrow\enspace |\mathbf{g}_1||\mathbf{g}_2|e^{(\theta_{g_1} + \theta_{g_2})\mathbf{e}_{12}} = |\mathbf{g}_2||\mathbf{g}_1|e^{(\theta_{g_2} + \theta_{g_1})\mathbf{e}_{12}}, + \end{split} \end{align} -Um später die Auswirkung der Quaternionen besser zu verstehen, möchte ich kurz darauf eingehen, was ein $g_n$ für eine Auswirkung auf einen Vektor hat. -Wir kennen diesen Effekt schon von den komplexen Zahlen. Wenn eine komplexe Zahl $c_1=a+bj$ mit einer zweiten $c_2=f+gj$ multipliziert wird, dann kann man diese so aufteilen. +welche wir schon von den komplexen Zahlen her kennen, ist dabei eine in der geometrischen Algebra nur selten anzutreffende Eigenschaft. Beispielsweise ist das geometrische Produkt von \begin{align} - c = c_1\cdot c_2 = (a + bj)(d + ej) = f\cdot(a+bj) + gj\cdot(a+bj) + \mathbf{g}_1\mathbf{v}\not= \mathbf{v}\mathbf{g}_1 \quad\Leftrightarrow\quad(a + b \mathbf{e}_{12})(x\mathbf{e}_1+y\mathbf{e}_2)\not= (x\mathbf{e}_1+y\mathbf{e}_2)(a + b \mathbf{e}_{12}) \end{align} -Dabei ist $f\cdot(a+bj)$ die jetzige komplexe Zahl $c_1$ um den Faktor $f$ steckt und $gj\cdot(a+bj)$ die um 90° im Gegenuhrzeigersinn gedrehte Zahl $c_2$ um den Faktor $g$ streckt. Diese Anteile addiert ergeben, dann den um $c_2$ dreh-gestreckten Vektor $c_1$. Die wirklichen Vorteile der geometrischen Algebra werden sich aber erst bei den Quaternionen zeigen.
\ No newline at end of file +und auch die im folgenden Kapitel behandelten Quaternionen nicht kommutativ. + +Um später die Auswirkung der Quaternionen auf Vektoren besser zu verstehen, möchte ich kurz darauf eingehen, was ein $\mathbf{g}_n$ für eine Auswirkung auf einen Vektor hat. +Wir kennen diesen Effekt schon von den komplexen Zahlen. Wenn eine komplexe Zahl $c_1=a+bj$ mit einer zweiten $c_2=f+gj$ multipliziert wird, dann kann man +\begin{align} + c = c_1\cdot c_2 = (a + bj)(d + ej) = a\cdot(d+ej) + bj\cdot(d+ej) +\end{align} +so aufteilen. Dabei ist $a\cdot(d+ej)$ die jetzige komplexe Zahl $c_2$ um den Faktor $a$ steckt und $bj\cdot(d+ej)$ die um 90° im Gegenuhrzeigersinn gedrehte Zahl $c_2$ um den Faktor $b$ streckt. Diese Anteile addiert ergeben, dann den um $c_1$ dreh-gestreckten Vektor $c_2$. Der gleiche Effekt hat +\begin{align}\label{GAdrehstreck} + \mathbf{v}' = \mathbf{g}\mathbf{v} = (a + b\mathbf{e}_{12})(d\mathbf{e}_{1} + e\mathbf{e}_{2}) = a(d\mathbf{e}_{1} + e\mathbf{e}_{2}) + b\mathbf{e}_{12}(d\mathbf{e}_{1} + e\mathbf{e}_{2}) +\end{align} +in der zweidimensionalen geometrischen Algebra. Im Falle der komplexen Zahlen macht es jetzt noch nicht wirklich Sinn in die geometrische Algebra zu wechseln. Die potenziellen Vorteile der geometrischen Algebra werden sich aber erst bei den Quaternionen zeigen.
\ No newline at end of file diff --git a/buch/papers/clifford/Makefile.inc b/buch/papers/clifford/Makefile.inc index e168ae8..fe32eba 100644 --- a/buch/papers/clifford/Makefile.inc +++ b/buch/papers/clifford/Makefile.inc @@ -14,7 +14,9 @@ dependencies-clifford = \ papers/clifford/4_GeometrischesProdukt.tex \ papers/clifford/5_PolareDarstellung.tex \ papers/clifford/6_PauliMatrizen.tex \ - papers/clifford/7_Reflektion.tex \ + papers/clifford/7_Spiegelung.tex \ papers/clifford/8_Rotation.tex \ papers/clifford/9_KomplexeZahlen.tex \ - papers/clifford/10_Quaternionen.tex + papers/clifford/10_Quaternionen.tex \ + papers/clifford/11_Fazit.tex + diff --git a/buch/papers/clifford/main.tex b/buch/papers/clifford/main.tex index ec44963..3649b20 100644 --- a/buch/papers/clifford/main.tex +++ b/buch/papers/clifford/main.tex @@ -16,10 +16,11 @@ \input{papers/clifford/4_GeometrischesProdukt.tex} \input{papers/clifford/5_PolareDarstellung.tex} \input{papers/clifford/6_PauliMatrizen.tex} -\input{papers/clifford/7_Reflektion.tex} +\input{papers/clifford/7_Spiegelung.tex} \input{papers/clifford/8_Rotation.tex} \input{papers/clifford/9_KomplexeZahlen.tex} \input{papers/clifford/10_Quaternionen.tex} +\input{papers/clifford/11_Fazit.tex} \printbibliography[heading=subbibliography] \end{refsection} diff --git a/buch/papers/erdbeben/Gausskurve2.pdf b/buch/papers/erdbeben/Gausskurve2.pdf Binary files differindex bee3bc0..5e4afdf 100644 --- a/buch/papers/erdbeben/Gausskurve2.pdf +++ b/buch/papers/erdbeben/Gausskurve2.pdf diff --git a/buch/papers/erdbeben/Gausskurve2.tex b/buch/papers/erdbeben/Gausskurve2.tex index 44319c3..2441766 100644 --- a/buch/papers/erdbeben/Gausskurve2.tex +++ b/buch/papers/erdbeben/Gausskurve2.tex @@ -1,13 +1,12 @@ \documentclass{standalone} \usepackage{pgfplots} - +\usepackage{txfonts} \pgfplotsset{compat = newest} \begin{document} - -\begin{tikzpicture} +\begin{tikzpicture}[>=latex,thick] \begin{axis}[ diff --git a/buch/papers/erdbeben/Gausskurve3.pdf b/buch/papers/erdbeben/Gausskurve3.pdf Binary files differindex e86a403..b86023f 100644 --- a/buch/papers/erdbeben/Gausskurve3.pdf +++ b/buch/papers/erdbeben/Gausskurve3.pdf diff --git a/buch/papers/erdbeben/Gausskurve3.tex b/buch/papers/erdbeben/Gausskurve3.tex index 85455ef..032d6de 100644 --- a/buch/papers/erdbeben/Gausskurve3.tex +++ b/buch/papers/erdbeben/Gausskurve3.tex @@ -1,13 +1,12 @@ \documentclass{standalone} \usepackage{pgfplots} - +\usepackage{txfonts} \pgfplotsset{compat = newest} \begin{document} - -\begin{tikzpicture} +\begin{tikzpicture}[>=latex,thick] \begin{axis}[ diff --git a/buch/papers/erdbeben/main.tex b/buch/papers/erdbeben/main.tex index 95f1f4b..4167475 100644 --- a/buch/papers/erdbeben/main.tex +++ b/buch/papers/erdbeben/main.tex @@ -4,7 +4,7 @@ % (c) 2020 Hochschule Rapperswil % \chapter{Erdbebenmessung\label{chapter:erdbeben}} -\lhead{Thema} +\lhead{Erdbeben} \begin{refsection} \chapterauthor{Lukas Zogg und Fabio Veicelli} diff --git a/buch/papers/erdbeben/references.bib b/buch/papers/erdbeben/references.bib index 56ca24b..444c82d 100644 --- a/buch/papers/erdbeben/references.bib +++ b/buch/papers/erdbeben/references.bib @@ -1,22 +1,22 @@ %% This BibTeX bibliography file was created using BibDesk. %% https://bibdesk.sourceforge.io/ -%% Created for lukas zogg at 2021-07-17 16:48:19 +0200 +%% Created for lukas zogg at 2021-07-27 17:56:45 +0200 %% Saved with string encoding Unicode (UTF-8) -@article{aragher_understanding_2012, +@article{erdbeben:aragher_understanding_2012, author = {Faragher, Ramsey}, date-added = {2021-07-17 16:44:00 +0200}, date-modified = {2021-07-17 16:45:54 +0200}, - journal = { Signal Processing Magazine}, + journal = {Signal Processing Magazine}, month = {09}, number = {5}, pages = {128--132}, - title = {Understanding the Basis of the Kalman Filter Via a Simple and Intuitive Derivation }, + title = {Understanding the Basis of the Kalman Filter Via a Simple and Intuitive Derivation}, volume = {29}, year = {2012}, Bdsk-File-1 = {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}} diff --git a/buch/papers/erdbeben/teil0.tex b/buch/papers/erdbeben/teil0.tex index 8ce8ff2..c985713 100644 --- a/buch/papers/erdbeben/teil0.tex +++ b/buch/papers/erdbeben/teil0.tex @@ -23,6 +23,7 @@ Die Masse schwing jedoch in seiner Eigendynamik weiter. Relativbewegung des Bodens kann damit als Auslenkung im Zeitverlauf gemessen werden. In modernen Seismographen wird die Bodenbewegung in alle Richtungen gemessen, sowohl Horizontal als auch Vertikal. Wir konstruieren uns eine einfachere Version eines Seismographen mit eine Gehäuse, an dem zwei Federn und eine Masse befestigt sind. +Der Seismograph ist in Abbildung ~\ref{erdbeben:Seismograph} ersichtlich. Ein Sensor unter der Masse misst die Position, bzw. die Auslenkung der Feder und der Masse. Dies bedeutet, unser Seismograph kann nur in eine Dimension Messwerte aufnehmen. @@ -30,52 +31,52 @@ Dies bedeutet, unser Seismograph kann nur in eine Dimension Messwerte aufnehmen. \begin{center} \includegraphics[width=5cm]{papers/erdbeben/Apperatur} \caption{Aufbau des Seismographen mit Gehäuse, Masse, Federn und Sensor} + \label{erdbeben:Seismograph} \end{center} \end{figure} \subsection{Ziel} Unser Seismograph misst nur die Position der Masse über die Zeit. -Wir wollen jedoch die Beschleunigung $a(t)$ des Boden bzw. die Kraft $f(t)$ welche auf das Gehäuse wirkt bestimmten. -Anhand dieser Beschleunigung bzw. der Krafteinwirkung durch die Bodenbewegung wird später das Bauwerk bemessen. +Wir wollen jedoch die Beschleunigung $a(t)$ des Boden, bzw. die Kraft $f(t)$, welche auf das Gehäuse wirkt, bestimmten. +Anhand dieser Beschleunigung, bzw. der Krafteinwirkung durch die Bodenbewegung, wird später das Bauwerk bemessen. Dies bedeutet, die für uns interessante Grösse $f(t)$ wird nicht durch einen Sensor erfasst. Jedoch können wir durch zweifaches ableiten der Positionsmessung $s(t)$ die Beschleunigung der Masse berechnen. Das heisst: Die Messung ist zweifach Integriert die Kraft $f(t)$ inklusive der Eigendynamik der Masse. -Um die Bewegung der Masse zu berechnen, müssen wir Gleichungen für unser System finden. +Um die Krafteinwirkung der Masse zu berechnen, müssen wir Gleichungen für unser System finden. \subsection{Systemgleichung} -Im Fall unseres Seismographen, kann die Differentialgleichung zweiter Ordnung einer gedämpften Schwingung am harmonischen Oszillator verwendet werden. -Diese lautet: +Im Paper~\cite{erdbeben:mendezmueller} wurde das System gleich definiert und vorgegangen. +Im Fall unseres Seismographen, handelt es sich um ein Feder-Masse-Pendel. +Dieser kann durch die Differentialgleichung zweiter Ordnung einer gedämpften Schwingung am harmonischen Oszillator beschrieben werden. +Die Gleichung lautet: \begin{equation} -m\ddot s + 2k \dot s + Ds = f +m\ddot s + 2k \dot s + Ds = f. \end{equation} -mit den Konstanten $m$ = Masse, $k$ = Dämpfungskonstante und $D$ = Federkonstante. -Da die DGL linear ist, kann sie in die kompaktere und einfachere Matrix-Form umgewandelt werden. Dazu wird die Differentialgleichung zweiter Ordnung substituiert: -\[ {s_1}=s \qquad -{s_2}=\dot s, \qquad\] -Somit entstehen die Gleichungen für die Position $s(t)$ der Masse : +wobei $m$ die Masse, $k$ die Dämpfungskonstante und $D$ die Federkonstante bezeichnet. +Da die Differentialgleichung linear ist, kann sie in die kompaktere und einfachere Matrix-Form umgewandelt werden. +Dazu verwenden wir die Subsitution: +\[
s_1 = s
\qquad \text{und} \qquad
s_2 = \dot s
.
\] +Somit entstehen die Gleichungen für die Position $ \dot s_1(t)$ der Masse : \[ \dot {s_1} = {s_2}\] und -\[ \dot s_2 = -\frac{D}{m} {s_1} -\frac{2k}{m} {s_2} + \frac{f} {m} \] für die Beschleunigung $a(t)$ der Masse. - +\[ \dot s_2 = -\frac{D}{m} {s_1} -\frac{2k}{m} {s_2} + \frac{f} {m} \] +für die Beschleunigung $\dot s_2(t)$ der Masse. Diese können wir nun in der Form -\[ {s_3}=-\frac{D}{m} {s_1} -\frac{2k}{m} {s_2} + \frac{f} {m} \] +\[ f =-\frac{D}{m} {s_1} -\frac{2k}{m} {s_2} + \frac{f} {m} \] auch als Matrix-Vektor-Gleichung darstellen. Dafür wird die Gleichung in die Zustände aufgeteilt. -Die für uns relevanten Zustände sind die Position der Masse, die Geschwindigkeit der Masse und die äussere Beschleunigung des ganzen System. -Dabei muss unterschieden werden, um welche Beschleunigung es sich handelt. -Das System beinhaltet sowohl eine Beschleunigung der Masse, innere Beschleunigung, als auch eine Beschleunigung der ganzen Apparatur, äussere Beschleunigung. -In unserem Fall wird die äusseren Beschleunigung gesucht, da diese der Erdbebenanregung gleich kommt. -\begin{equation} -\frac{d}{dt} \left(\begin{array}{c} {s_1} \\ {s_2} \end{array}\right) = \left( - \begin{array}{ccc} -0 & 1& 0 \\ -- \frac{D}{m} &-\frac{2k}{m} & \frac{1} {m}\\ -\end{array}\right) \left(\begin{array}{c} {s_1} \\ {s_2} \\ {s_3} \end{array}\right). -\end{equation} - -Durch Rücksubstituion ergibt sich: +Die für uns relevanten Zustände sind die Position der Masse, die Geschwindigkeit der Masse und die äussere Beschleunigung des ganzen Systems. + +Dabei muss unterschieden werden, um welche Beschleunigung es sich handelt. +Das System beinhaltet sowohl eine Beschleunigung der Masse (innere Beschleunigung) als auch eine Beschleunigung der ganzen Apparatur (äussere Beschleunigung). +In unserem Fall wird die äusseren Beschleunigung gesucht, da diese der Erdbebenanregung gleich kommt. +Dazu wird ein Zustandsvektor definiert: +\[ + \left(\begin{array}{c} {s_1} \\ {s_2} \\ {f} \end{array}\right). + \] +Durch Rücksubstituion ergibt sich uns folgende Systemgleichung in Matrix schreibweise, , wobei $\dot {s_1}= v$ ist: \begin{equation} -\frac{d}{dt} \left(\begin{array}{c} s(t) \\ v(t) \end{array}\right) = \left( +\frac{d}{dt} \left(\begin{array}{c} s(t) \\ v(t) \\ f(t) \end{array}\right) = \left( \begin{array}{ccc} 0 & 1& 0 \\ - \frac{D}{m} &-\frac{2k}{m} & \frac{1} {m}\\ diff --git a/buch/papers/erdbeben/teil1.tex b/buch/papers/erdbeben/teil1.tex index e07800f..6c334bf 100644 --- a/buch/papers/erdbeben/teil1.tex +++ b/buch/papers/erdbeben/teil1.tex @@ -14,6 +14,8 @@ \rhead{Kalman-Filter} \section{Kalman-Filter} +Interessante Grösse ist also Integral von Überlagerung zweier Kräfte. +Wir brauchen also dir zweite Ableitung von der Messung , ohne deren Eigendynamik. Da wir die äussere Kraft nicht direkt messen können, benötigen wir ein Werkzeug, welches aus der gemessenen Position, die Krafteinwirkung auf unsere System schätzt. Dies ist eine typische Anwendung für das Kalman-Filter. Unser Ziel ist es, anhand der Messung die eigentlich interessante Grösse $f$ zu bestimmen. @@ -23,8 +25,8 @@ Die Idee dahinter ist, dass das Kalman-Filter die nicht-deterministische Grösse Für mehrere Dimensionen (x,y,z) würde der Pythagoras für das System benötigt werden. Da sich der Pythagoras bekanntlich nicht linear verhält, kann kein lineares Kalman-Filter implementiert werden. Da das Kalman-Filter besonders effektiv und einfach für lineare Abläufe geeignet ist, würde eine zweidimensionale Betrachtung den Rahmen dieser Arbeit sprengen. -Für ein nicht-lineares System werden Extended Kalman-Filter benötigt, bei denen die System-Matrix (A) durch die Jacobi-Matrix des System ersetzt wird. Einfachheitshalber beschränken wir uns auf den linearen Fall, da dadurch die wesentlichen Punkte bereits aufgezeigt werden. +Für ein nicht-lineares System werden Extended Kalman-Filter benötigt, bei denen die System-Matrix (A) durch die Jacobi-Matrix des System ersetzt wird. \subsection{Geschichte} Das Kalman-Filter wurde 1960 von Rudolf Emil Kalman entdeckt und direkt von der NASA für die Appollo Mission benutzt. @@ -35,57 +37,60 @@ Das Filter schätzt den Zustand eines Systems anhand von Messungen und kann den Das Kalman-Filter schätzt den wahrscheinlichsten Wert zwischen Normalverteilungen. Dies bedeutet, das Filter schätzt nicht nur den Mittelwert, sondern auch die Standartabweichung. Da Normalverteilungen dadurch vollständig definiert sind, schätzt ein Kalman-Filter die gesamte Verteilungsfunktion des Zustandes. +In der Abbildung~\ref{erdbeben: Zwei Normalverteilungen} sind zwei Funktionen dargestellt. Die eine Funktion zeigt die errechnete Vorhersage des Zustands, bzw. deren Normalverteilung. Die andere Funktion zeigt die verrauschte Messung des nächsten Zustand, bzw. deren Normalverteilung. -Wie man am Beispiel der Gauss-Verteilungen unten sehen kann, ist sowohl der geschätzte Zustand als auch der gemessene Zustand normalverteilt und haben dementsprechend unterschiedliche Standardabweichungen $\sigma$ und Erwartungswerte $\mu$. - +Wie man am Beispiel der Gauss-Verteilungen in Abblidung~\ref{erdbeben: Zwei Normalverteilungen} sehen kann, ist sowohl der geschätzte Zustand als auch der gemessene Zustand normalverteilt und haben dementsprechend unterschiedliche Standardabweichungen $\sigma$ und Erwartungswerte $\mu$. Dies wird in~\cite{erdbeben:aragher_understanding_2012}beschrieben. \begin{figure} \begin{center} \includegraphics[width=5cm]{papers/erdbeben/Gausskurve2.pdf} \caption{Zwei Normalerteilungen; Die eine Funktion zeigt die Vorhersage, die andere die Messung} + \label{erdbeben: Zwei Normalverteilungen} \end{center} \end{figure} - - +Wir haben eine Vorhersage aus der Systemdynamik und eine Messung des Zustandes. +Diese widersprechen sich im Allgemeinen. +Jedoch wissen wir die Wahrscheinlichkeiten der beiden Aussagen. Um eine genauere Schätzung des Zustandes zu machen, wird nun ein Wert zwischen den beiden Verteilungen berechnet. Nun wird eine Eigenschaft der Normalverteilung ausgenutzt. Durch das Multiplizieren zweier Normalverteilungen entsteht eine neue Normalverteilung. Wir haben eine Normalverteilung der Vorhersage: - -\[ {y_1}(x;{\mu_1},{\sigma_1})=\frac{1}{\sqrt{2\pi\sigma_1^2}}\quad e^{-\frac{(x-{\mu_1})^2}{2{\sigma_1}^2}} \] +\[ +{y_1}(x;{\mu_1},{\sigma_1})=\frac{1}{\sqrt{2\pi\sigma_1^2}}\quad e^{-\frac{(x-{\mu_1})^2}{2{\sigma_1}^2}} +\] und der Messung: -\[ {y_2}(x;{\mu_2},{\sigma_2})=\frac{1}{\sqrt{2\pi\sigma_2^2}}\quad e^{-\frac{(x-{\mu_2})^2}{2{\sigma_2}^2}}. \] - - - -Diesen werden nun Multipliziert und durch deren Fläche geteilt um sie wieder zu Normieren: -\[ -{y_f}(x;{\mu_f},{\sigma_f})=\frac{ \frac{1}{\sqrt{2\pi\sigma_1^2}}e^{-\frac{(x-{\mu_1})^2}{2{\sigma_1}^2}} \cdot \frac{1}{\sqrt{2\pi\sigma_2^2}}e^{-\frac{(x-{\mu_2})^2}{2{\sigma_2}^2}}}{\int {y_1}\cdot{y_2} dx\,} - \] - +\[ +{y_2}(x;{\mu_2},{\sigma_2})=\frac{1}{\sqrt{2\pi\sigma_2^2}}\quad e^{-\frac{(x-{\mu_2})^2}{2{\sigma_2}^2}}. +\] +Diesen werden nun multipliziert und durch deren Fläche geteilt um sie wieder zu normieren, $\odot$ beschreibt dabei die Multiplikation und die Normierung auf den Flächeninhalt eins : +\begin{align*}
{y_f}(x; {\mu_f}, {\sigma_f}) = {y_1}(x;{ \mu_1},{ \sigma_1}) \odot {y_2}(x; {\mu_2}, {\sigma_2}) + &= + \frac{1}{\sqrt{2\pi\sigma_1^2}}\quad e^{-\frac{(x-{\mu_1})^2}{2{\sigma_1}^2}} \odot \frac{1}{\sqrt{2\pi\sigma_2^2}}\quad e^{-\frac{(x-{\mu_2})^2}{2{\sigma_2}^2}} + \\ + &=
\frac{ \frac{1}{\sqrt{2\pi\sigma_1^2}}e^{-\frac{(x-{\mu_1})^2}{2{\sigma_1}^2}} \cdot \frac{1}{\sqrt{2\pi\sigma_2^2}}e^{-\frac{(x-{\mu_2})^2}{2{\sigma_2}^2}}}{\int {y_1} {y_2} dx}.
\end{align*} Diese Kombination der beiden Verteilungen resultiert wiederum in einer Normalverteilung -\[ {y_f}(x; {\mu_f}, {\sigma_f}) = {y_1}(x;{ \mu_1},{ \sigma_1}) {\cdot y_2}(x; {\mu_2}, {\sigma_2}), \] mit Erwartungswert \[ \mu_f = \frac{\mu_1\sigma_2^2 + \mu_2 \sigma_1^2}{\sigma_1^2 + \sigma_2^2} \] und Varianz -\[ \sigma_f^2 = \frac{\sigma_1^2 \sigma_2^2}{\sigma_1^2 + \sigma_2^2}. \] - +\[ +\sigma_f^2 = \frac{\sigma_1^2 \sigma_2^2}{\sigma_1^2 + \sigma_2^2}. +\] Dadurch gleicht sich die neue Kurve den anderen an. Interessant daran ist, dass die fusionierte Kurve sich der genauere Normal-Verteilung anpasst. Ist ${\sigma_2}$ klein und ${\sigma_1}$ gross, so wird sich die fusionierte Kurve näher an ${y_2}(x;{\mu_2},{\sigma_2})$ begeben. -Sie ist also gewichtet und die best mögliche Schätzung. - - +Somit ist $\mu_f$ ist das gewichtete Mittel der beiden $\mu_{1,2}$, und die Varianzen sind die Gewichte! +Die neue Funktion ist die best mögliche Schätzung für zwei Verteilungen, welche den selben Zustand beschreiben. +Dies ist in der Abbildung~\ref{erdbeben:Gauss3} anhand der rote Funktion ersichtlich. \begin{figure} \begin{center} \includegraphics[width=5cm]{papers/erdbeben/Gausskurve3.pdf} \caption{Durch das Multiplizieren der blauen und der orangen Verteilung entsteht die die rote, optimale Funktion} + \label{erdbeben:Gauss3} \end{center} \end{figure} - - Was in zwei Dimensionen erklärt wurde, funktioniert auch in mehreren Dimensionen. Dieses Prinzip mach sich das Kalman Filter zu nutze, und wird von uns für die Erdbeben Berechnung genutzt. \section{Filter-Matrizen} +Da wir nun ein Werkzeug besitzen, dass die Beschleunigung, welche auf das Gehäuse wirkt, ermitteln kann, wird dieses nun Schritt für Schritt erklärt. Um den Kalman Filter zu starten, müssen gewisse Bedingungen definiert werden. In diesem Abschnitt werden die einzelnen Parameter und Matrizen erklärt und erläutert, wofür sie nützlich sind. @@ -94,8 +99,6 @@ In diesem Abschnitt werden die einzelnen Parameter und Matrizen erklärt und erl Das Filter benötigt eine Anfangsbedingung. In unserem Fall ist es die Ruhelage, die Masse bewegt sich nicht. Zudem erfährt die Apparatur keine äussere Kraft. - - \[ {x_0 }= \left( \begin{array}{c} {s_0}\\ {v_0}\\{f_0}\end{array}\right) = \left( \begin{array}{c} 0\\ 0\\ 0\end{array}\right) \] \subsubsection*{Anfangsfehler / Kovarianzmatrix $P$} @@ -108,7 +111,6 @@ Kovarianz: Cov(x, y) und Varianz: Var(x) = Cov(x, x) In unserem Fall ist der Anfangszustand gut bekannt. Wir gehen davon aus, dass das System in Ruhe und in Abwesenheit eines Erdbeben startet, somit kann die Matrix mit Nullen bestückt werden. Als Initialwert für die Kovarianzmatrix ergibt sich - \[ {P_0 }= \left( @@ -145,9 +147,9 @@ Die Matrix $\Phi$ beschreibt die Übergänge zwischen zeitlich aufeinanderfolgen \subsubsection*{Prozessrauschkovarianzmatrix $Q$} Die Prozessrauschmatrix teilt dem Filter mit, wie sich der Prozess verändert. -Kalman-Filter berücksichtigen sowohl Unsicherheiten wie Messfehler und -rauschen. -In der Matrix $Q$ geht es jedoch im die Unsicherheit die der Prozess mit sich bringt. -Bei unserem Modell könnte das beispielsweise ein Windstoss an die Masse sein. +Kalman-Filter berücksichtigen Unsicherheiten wie Messfehler und -rauschen. +In der Matrix $Q$ geht es jedoch um die Unsicherheit, die der Prozess mit sich bringt. +Bei unserem Modell könnte das beispielsweise ein Windstoss an die Masse sein oder auch die Ungenauigkeiten im Modell, wie die Annahme das dich die Kraft nicht ändert. Für uns wäre dies: \[ Q = \left( @@ -157,7 +159,6 @@ Q = \left( 0 & 0& {\sigma_f }^2\\ \end{array}\right) \] - Die Standabweichungen müssten statistisch ermittelt werden, da der Fehler nicht vom Sensor kommt und somit nicht vom Hersteller gegeben ist. Das Bedeutet wiederum dass $Q$ die Unsicherheit des Prozesses beschreibt und nicht die der Messung. @@ -165,13 +166,15 @@ Das Bedeutet wiederum dass $Q$ die Unsicherheit des Prozesses beschreibt und nic Die Messmatrix gibt an, welche Parameter gemessen werden. $H$ ist die Gleichung die für die Vorhersage der Messung. In unserem Falle ist es die Position der Massen. - -\[ H = (1, 0, 0) \] +\[ +H = (1, 0, 0) +\] \subsubsection*{Messrauschkovarianz $R$} Die Messrauschkovarianzmatrix beinhaltet, wie der Name schon sagt, das Rauschen der Messung. In unserem Fall wird nur die Position der Masse gemessen. Da wir keine anderen Sensoren haben ist $R$ lediglich: -\[ R= ({\sigma_{sensor}}^2). +\[ +R= ({\sigma_\mathrm{sensor}}^2). \] Diese Messrauchen wird meistens vom Sensorhersteller angegeben. Für unsere theoretische Apparatur wird hier ein kleiner Fehler eingesetzt da heutige Sensoren sehr genau messen können. @@ -182,19 +185,25 @@ Zuerst wird der nächste Zustand der Masse vorhergesagt, danach wird die Messung Das Filter berechnet aufgrund der aktuellen Schätzung eine Vorhersage. Diese wird, sobald verfügbar, mit der Messung verglichen. Aus dieser Differenz und den Unsicherheiten des Prozesses ($Q$) und der Messung ($R$) wird der wahrscheinlichste, neue Zustand geschätzt. +Dabei muss genau auf den Index geachtet werden. Nach dem Artikel~\cite{erdbeben:wikipedia} ist die Indexierung so genormt: +Der Zeitschritt wird mit $k$ definiert, $k-1$ ist somit ein Zeitschritt vor $k$. +Auf der linken Seite von | wird der aktuelle Zustand verlangt, bzw. ausgegeben, auf der rechten Seiten den bisherigen Zustand. +Dies bedeutet, dass die Notation $x_{n|m}$ die Schätzung von $x$ zum Zeitpunkt $n$ bis und mit zur Zeitpunkt $m \leq \ n$ präsentiert. \subsubsection*{Vorhersage} Im Filterschritt Vorhersage wird der nächste Zustand anhand des Anfangszustand und der Systemmatrix berechnet. Dies funktioniert mit dem Rechenschritt: -\[ -{x_{k-1}}=\Phi \cdot {x_{k-1}}= \exp(A\Delta t)\cdot{x_{k-1}}. - \] - -Die Kovarianz $P_{pred}$ wird ebenfalls neu berechnet. Da wir ein mehrdimensionales System haben, kommt noch die Prozessunsicherheit $Q$ dazu, so dass die Unsicherheit des Anfangsfehlers $P$ laufend verändert. +\[ +{x_{k|k-1}}=\Phi{x_{k-1|k-1}}= \exp(A\Delta t){x_{k-1|k-1}}. +\] +Die Kovarianz $P_{k|k-1}$ wird ebenfalls neu berechnet. Zudem kommt noch die Prozessunsicherheit $Q$ dazu, so dass die Unsicherheit des Anfangsfehlers $P$ laufend verändert. Dies funktioniert durch multiplizieren der Systemmatrix mit dem aktualisierten Anfangsfehler. Dazu wird noch die Prozessunsicherheit addiert, somit entsteht die Gleichung -\[ {P_{k-1}} = {\Phi_k} {P_{k-1}} {\Phi_k} ^T + {Q_{k-1}} .\] -Es vergeht genau $t$ Zeit, und dieser Vorgang wird wiederholt. +\[ +{P_{k|k-1}}=\Phi {P_{k-1|k-1}} {\Phi _{k}}^T + {Q_{k-1}}. +\] +Es vergeht genau $\Delta t$ Zeit, und dieser Vorgang wird wiederholt. +Das hochgestellte T bezeichnet die transponierte Matrix. Dabei wird in den späteren Schritten überprüft, wie genau die letzte Anpassung von $P$ zur Messung stimmt. Ist der Unterschied klein, wird die Kovarianz $P$ kleiner, ist der Unterschied gross, wird auch die Kovarianz grösser. Das Filter passt sich selber an und korrigiert sich bei grosser Abweichung. @@ -202,74 +211,83 @@ Das Filter passt sich selber an und korrigiert sich bei grosser Abweichung. \subsubsection*{Messen} Der Sensor wurde noch nicht benutz, doch genau der liefert Werte für das Filter. Die aktuellen Messwerte $z$ werden die Innovation $w$ mit dem Zustandsvektor $x$ und der Messmatrix $H$ zusammengerechnet. -Hier bei wird lediglich die Messung mit dem Fehler behaftet, und die Messmatrix $H$ mit der Vorhersage multipliziert - -\[{w_{k}}={z_{k}}-{H}\cdot{x_{k-1}}.\] - +Hier bei wird lediglich die Messung mit dem Fehler behaftet, und die Messmatrix $H$ mit der Vorhersage multipliziert. +\[ +{w_{k}}={z_{k}}-{H}{x_{k|k-1}}. +\] Die Innovation ist der Teil der Messung, die nicht durch die Systemdynamik erklärt werden kann. Die Hilfsgröße Innovation beschreibt, wie genau die Vorhersage den aktuellen Messwert mittels der Systemmatrix $\Phi$ beschreiben kann. Für eine schlechte Vorhersage wird die dazugehörige Innovation gross, für eine genaue Vorhersage dagegen klein sein. Entsprechende Korrekturen müssen dann gross bzw. nur gering ausfallen. -Innovation = Messung - Vorhersage. Dies ist intuitiv logisch, eine Innovation von 0 bedeutet, dass die Messung nichts Neues hervorbrachte. +Innovation = Messung - Vorhersage. Dies leuchtet ein, eine Innovation von 0 bedeutet, dass die Messung nichts Neues hervorbrachte. Im nächsten Schritt wir analysiert, mit welcher Kovarianz weiter gerechnet wird. Hierbei wird die Unsicherheit $P$, die Messmatrix $H$ und die Messunsicherheit $R$ miteinander verrechnet. \[ -{S_{k}}={H}{P_{k-1}}{H}^T+{R_{k}} - \] +{S_{k}}={H}{P_{k|k-1}}{H}^T+{R_{k}} +\] \subsubsection*{Aktualisieren} Im nächsten Schritt kommt nun die Wahrscheinlichkeit dazu. -\[ -{K_{k}}= {{P_{k-1}} \cdot {H_{k}^T}}\cdot {S_{k}}^{-1} - \] +\[{K_{k}}= {P_{k|k-1}} {H^T}{S_{k}^{-1}}\] Dieser Vorgang wird Kalman-Gain genannt. -Er sagt aus, welcher Kurve mehr Vertraut werden soll, dem Messwert oder der Systemdynamik. -Das Kalman-Gain wird geringer, wenn der Messwert dem vorhergesagten Systemzustand entspricht. -Sind die Messwerte komplett anders als die Vorhersage, werden die Elemente in der Matrix $K$ grösser. -Anhand der Informationen aus dem Kalman-Gain $K$ wird das System aktualisiert. +Das Kalman-Gain gibt dem Zustand die Gewichtung, bzw. wie die Vorhersage auf den Zustand passt. +Vereinfacht gesagt: Es wird das das Verhältnis zwischen der Unsicherheit der Vorhersage $P_k$ zu der zugehörigen Messunsicherheit $R_k$ gebildet. +In unserem Fall wird werden die Elemente der Kalman-Matrix vorweg berechnet, da das Kalman-Gain ohne Messungen auskommt. -\[ -{x_{k|k}}={x_{k-1}}+({K_{k}}\cdot {w_{k}}) - \] +Anhand der Informationen aus dem Kalman-Gain $K$ wird das System aktualisiert. +\[ +{x_{k|k}}={x_{k|k-1}}+{K_{k}}{w_{k}} +\] +Dabei wird der Unterschied zwischen dem erwarteten, errechneten, Zustand und dem gemessenen Zustand berechnet. Dazu kommt eine neue Kovarianz für den nächste Vorhersageschritt: - -\[ -{P_{k}}=(I-({K_{k}} \cdot {H})) \cdot {P_{k-1}} - \] - +\[ +{P_{k|k}}=(I-{K_{k}}{H}){P_{k|k-1}} +\] Der ganze Algorithmus und beginnt wieder mit der Vorhersage - -\[ -{x_{k-1}}=\Phi \cdot {x_{k-1}}= \exp(A\Delta t)\cdot{x_{k-1}}. - \] - +\[ +{x_{k|k-1}}=\Phi{x_{k-1|k-1}}= \exp(A\Delta t){x_{k|k-1}}. +\] \subsection{Zusammenfassung } Zusammenfassend kann das Kalman-Filter in offizieller Typus dargestellt werden. Dabei beginnt das Filter mit dem Anfangszustand für $k=0$ 1. Nächster Zustand vorhersagen -\[{x_{k-1}}={\Phi} \cdot {x_{k-1}}= \exp(A\Delta t)\cdot{x_{k-1}}.\] +\[ +{x_{k|k-1}}=\Phi{x_{k-1|k-1}}= \exp(A\Delta t){x_{k-1|k-1}}. +\] 2. Nächste Fehlerkovarianz vorhersagen -\[{P_{k-1}}={\Phi} {P_{k-1}} {\Phi _{k}}^T + {Q_{k-1}}.\] +\[ +{P_{k|k-1}}=\Phi {P_{k-1|k-1}} {\Phi _{k}}^T + {Q_{k-1}}. +\] 3. Zustand wird gemessen -\[{w_{k}}={z_{k}}-{H}\cdot{x_{k-1}}.\] +\[ +{w_{k}}={z_{k}}-{H}{x_{k|k-1}}. +\] 4. Innovation (= Messung - Vorhersage) -\[ {S_{k}}={H}{P_{k-1}}{H}^T+{R_{k}}\] +\[ +{S_{k}}={H}{P_{k|k-1}}{H}^T+{R_{k}} +\] 5. Das Kalman Filter anwenden -\[{K_{k}}= {P_{k-1}} \cdot {H^T}\cdot {S_{k}^{-1}}\] +\[ +{K_{k}}= {P_{k|k-1}} {H^T}{S_{k}^{-1}} +\] 6. Schätzung aktualisieren -\[{x_{k}}={x_{k-1}}+({K_{k}}\cdot {w_{k}}) \] +\[ +{x_{k|k}}={x_{k|k-1}}+{K_{k}}{w_{k}} +\] 7. Fehlerkovarianz aktualisieren -\[{P_{k}}=(I-({K_{k}}\cdot {H})) \cdot {P_{k-1}} \] +\[ +{P_{k|k}}=(I-{K_{k}}{H}){P_{k|k-1}} +\] 8. Die Outputs von $k$ werden die Inputs für ${k-1}$ und werden wieder im Schritt 1 verwendet diff --git a/buch/papers/ifs/images/FIC.pdf b/buch/papers/ifs/images/FIC.pdf index 1c76dfe..525a857 100644 --- a/buch/papers/ifs/images/FIC.pdf +++ b/buch/papers/ifs/images/FIC.pdf @@ -1,7 +1,7 @@ %PDF-1.6
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diff --git a/buch/papers/ifs/teil0.tex b/buch/papers/ifs/teil0.tex index 833748c..af2105e 100644 --- a/buch/papers/ifs/teil0.tex +++ b/buch/papers/ifs/teil0.tex @@ -5,7 +5,7 @@ % \section{Einleitung \label{ifs:section:teil0}} \rhead{Was ist ein Iteriertes Funktionsschema} -Mit der Hilfe von Iterierten Funktionsschemata (IFS) kann mit nur wenigen affinen Funktionen, komplexe Bilder beschreiben werden. +Mit der Hilfe von Iterierten Funktionsschemata (IFS) können mit nur wenigen affinen Funktionen komplexe Bilder beschrieben werden. In der Regel sind diese Bilder Fraktale. Wie es dazu kommt, und wie man mit IFS auch Bilder komprimieren kann, wollen wir in diesem Kapitel untersuchen. diff --git a/buch/papers/ifs/teil1.tex b/buch/papers/ifs/teil1.tex index a75b529..7ce546a 100644 --- a/buch/papers/ifs/teil1.tex +++ b/buch/papers/ifs/teil1.tex @@ -7,29 +7,27 @@ \label{ifs:section:teil1}} \rhead{Problemstellung} Bevor wir die IFS ansehen, schauen wir uns Fraktale genauer an. - - Über die genaue Definition von Fraktalen sind sich die Mathematiker nicht einig. -In diesem Kapitel orientieren wir uns an den Eigenschaften welche Kenneth Falconer in seinem Buch Fractal Geometry \cite{ifs:fractal-geometry} beschreibt. +In diesem Kapitel orientieren wir uns an den Eigenschaften, welche Kenneth Falconer in seinem Buch {\em Fractal Geometry} \cite{ifs:fractal-geometry} beschreibt. Von einem Fraktal $F$ können wir folgende Eigenschaften erwarten: \begin{enumerate} \item $F$ hat eine unendlich feine Struktur \item $F$ kann nicht mit der klassischen Geometrie beschrieben werden. \item Oftmals hat $F$ eine Form von Selbstähnlichkeit. - \item Die 'fraktale Dimension' ist grösser als die topologische Dimension + Man spricht von einer selbstähnlichen Menge, wenn sich diese Menge überdecken lässt mit echten Teilmengen, die zur ganzen Menge ähnlich sind. + \item Die `fraktale Dimension´ ist grösser als die topologische Dimension \item Viele Fraktale lassen sich auf eine simple Art definieren. Es genügen zum Beispiel nur wenige Funktionen, welche rekursiv ausgeführt werden, um ein Fraktal zu definieren. \end{enumerate} \subsection{Koch Kurve \label{ifs:subsection:lilkoch}} Diese Eigenschaften möchten wir nun am Beispiel der Koch Kurve näher anschauen. -In Abbildung \ref{ifs:kochkurve8} sehen wir die Koch Kurve. Sie besteht aus lauter kleineren Kopien von sich selber. -Den Konstruktionsvorgang ist in Abbildung \ref{ifs:kochconst} dargestellt. +In Abbildung \ref{ifs:kochkurve8} sehen wir die Koch Kurve. Sie besteht aus lauter kleineren Kopien von sich selbst. +Der Konstruktionsvorgang ist in Abbildung \ref{ifs:kochconst} dargestellt. Gestartet wird mit einer einzelnen Strecke der Länge $a$. Diese wird in ersten Schritt durch vier gleich langen Streckenabschnitte der Länge $\frac{a}{3}$ ersetzt. In \ref{ifs:kochconstb} ist die Anordnung dieser vier Streckenabschnitte ersichtlich. Dieser Schritt wird nun für jeden der resultierten Streckenabschnitten wiederholt. Die Kurve besteht also aus vier kleineren Kopien der ganzen Kurve, was auch unter Selbstähnlichkeit bekannt ist. -Man spricht von einer selbstähnlichen Menge, wenn sich diese Menge überdecken lässt mit echten Teilmengen, die zur ganzen Menge ähnlich sind. \begin{figure} @@ -66,7 +64,7 @@ berechnen. In jedem Schritt wird die Länge um den Faktor $\frac{4}{3}$ verlängert. Daraus resultiert, dass die Länge gegen $\infty$ divergiert. -Die Fläche unter der Kurve lässt sich folgendermassen berechnen +Die Fläche der Kurve lässt sich folgendermassen berechnen \begin{align*} A_0 &= 0 \\ A_1 &= \left( \frac{a}{3}\right)^2 \frac{\sqrt{3}}{4} = a^2 \frac{\sqrt{3}}{36}\\ @@ -88,14 +86,14 @@ Wie wir sehen ist die Koch-Kurve ein Objekt mit endlicher Fläche, aber unendlic Zu guter Letzt bestimmen wir die Dimension der Kurve. Es gibt viele verschiedene Methoden die Dimension zu definieren. Diese können dann auch unterschiedliche Resultate liefern. Vor allem im Zusammenhang mit Fraktalen findet man in der Literatur unterschiedliche Arten. -In diesem Beispiel werden wir die Ähnlichkeits-Dimension \cite{ifs:fractal-geometry}. +Da die Kochsche Kurve selbstähnlich ist, ist die Ähnlichkeits-Dimension \cite{ifs:fractal-geometry} die angemessene Messzahl für die Dimension. Die Ähnlichkeits-Dimension $D$ ist das Verhältnis der Logarithmen der Anzahl Kopien $N$ des Originales und deren Skalierungsfaktor $\epsilon$ \begin{align*} D = - \frac{\log N}{\log \epsilon }. \end{align*} -Mit ihr kann man einfach die Dimension selbstähnlicher Mengen bestimmen. -Als Beispiel nehmen wir ein gleichseitiges Dreieck. Dieses besteht aus $N = 4$ Kopien mit halber ($\epsilon = 1/2$) Kantenlänge $l$, Abbildung \ref{ifs:trinagle}. +Die Ähnlichkeits-Dimension stimmt für viele gewöhnliche Geometrische Objekte mit der intuitiven Vorstellung von Dimension überein. +Zum Beispiel besteht ein Dreieck aus $N = 4$ Kopien mit halber ($\epsilon = 1/2$) Kantenlänge $l$, Abbildung \ref{ifs:trinagle}. Somit hat das Dreieck die Dimension $D = 2$. Die Koch Kurve besteht aus $N = 4$ Kopien mit Kantenlänge $\epsilon =l \cdot 1/3$. Ihre Ähnlichkeits-Dimension ist somit @@ -103,7 +101,7 @@ Ihre Ähnlichkeits-Dimension ist somit D = - \frac{\log N }{\log \epsilon } = - \frac{\log 4 }{\log 1/3 } \approx 1.2619. \end{align*} Wie wir nun sehen besitzt die Koch-Kurve alle oben beschriebenen Eigenschaften von Fraktalen. -Dies muss jedoch nicht bei allen Fraktalen der Fall. Sonst wäre die Frage nach einer 'richtigen' Definition einfach zu beantworten. +Dies muss jedoch nicht bei allen Fraktalen der Fall sein. Sonst wäre die Frage nach einer 'richtigen' Definition einfach zu beantworten. \begin{figure} \centering \begin{tikzpicture} diff --git a/buch/papers/ifs/teil2.tex b/buch/papers/ifs/teil2.tex index fd10634..49c1cf3 100644 --- a/buch/papers/ifs/teil2.tex +++ b/buch/papers/ifs/teil2.tex @@ -6,8 +6,9 @@ \section{Fraktale mit IFS \label{ifs:section:teil2}} \rhead{Teil 2} -Wollen wir nun eine bestimmte Art anschauen, wie man Fraktale machen kann. -Zur Veranschaulichung dieser Methode nehmen wir das Sierpinski Dreieck. +Wollen wir nun eine bestimmte Art anschauen, wie man Fraktale erzeugen kann. +Im Beispiel auf Seite \pageref{ifs:trinagle} haben wir ein Dreieck aus 4 skalierten Kopien zusammengefügt. +Lässt man die Kopie im Zentrum des Dreiecks weg, entsteht die Grundlage des sogenannten Sierpinski-Dreieck, Abbildung \ref{ifs:sierpinski10}. \begin{figure} \centering \includegraphics[width=0.5\textwidth]{papers/ifs/images/sierpinski} @@ -92,20 +93,21 @@ Man kann sogar noch einen Schritt weiter gehen, und sagen: Wenn wir die Funktion \label{ifs:sierpconst} \end{figure} Im Beispiel der Abbildung \ref{ifs:sierpconst} sehen wir, wie das Bild nach jeder Iteration dem Sierpinski-Dreieck ähnlicher wird. -Der Abstand zum Original wird immer kleiner, und konvergiert gegen null. +Der `Abstand´ zum Original wird immer kleiner, und konvergiert gegen null. \subsection{Iterierte Funktionensysteme \label{ifs:subsection:IteratedFunktionensysteme}} In diesem Abschnitt wollen wir die Erkenntnis, wie wir aus einer beliebigen Menge ein Sierpinski-Dreieck generieren können, verallgemeinern. -$S_1,\dots,S_n$ sind Kontraktionen auf die Menge $D \subset \mathbb{R}^n$. Es gilt +$S_1,\dots,S_n$ sind Kontraktionen auf der Menge $D \subset \mathbb{R}^n$. Es gilt \begin{align} |S_i(x) - S_i(y)| \leq c_i|x - y| \end{align} für jedes i mit einem $c_i < 1$. -Der Banachsche Fixpunktsatz besagt, dass für solche Kontraktionen ein Eindeutiges $A$ existiert, für das $S(A) = A$ gilt. +Der Banachsche Fixpunktsatz besagt, dass für solche Kontraktionen ein Eindeutiges $A$ existiert, für das $S_i(A) = A$ gilt. Den Beweis kann man in \cite{ifs:Rousseau2012} nachlesen. + Hat man nicht nur eine sondern mehrere Kontraktionen, dann existiert eine eindeutige kompakte Menge $F$ für die gilt \begin{equation} F = \bigcup\limits_{i = 1}^{m} S_i(F). @@ -125,7 +127,7 @@ Diese Menge ist auch als Attraktor eines IFS bekannt. Der Beweis für die Existenz eines eindeutigen Attraktors ist in \cite{ifs:fractal-geometry} beschrieben. \subsection{Beispiel: Barnsley-Farn} -Der Barnsley-Farn, Abbildung \ref{ifs:farn}, ist ein Beispiel eines Fraktal, welches mit einem IFS generiert werden kann. +Der Barnsley-Farn, Abbildung \ref{ifs:farn}, ist ein Beispiel eines Fraktals, welches mit einem IFS generiert werden kann. Wie man schnell erkennen kann, besteht der Farn aus Blättern, welche eine grosse Ähnlichkeit zum ganzen Farn haben. Die vier affinen Transformationen \begin{align} @@ -183,22 +185,22 @@ Die vier affinen Transformationen \begin{pmatrix} 0 \\ 0.44 - \end{pmatrix}\\ + \end{pmatrix},\\ \label{ifs:farnFormel} \end{align} -, welche für die konstruktion des Farns benötigt werden sind in der Abbildung \ref{ifs:farncolor} farblich dargestellt. +, welche für die Konstruktion des Farns benötigt werden sind in der Abbildung \ref{ifs:farncolor} farblich dargestellt. Das gesamte Farnblatt ist in der schwarzen Box. -Auf diese werden die Transformationen angewendet +Auf diese werden die Transformationen angewendet. $S_1$ erstellt den Stiel des Farnblattes (rot). -Die Transformation bildet das Gesamte Blatt auf die Y-Achse ab. +Die Transformation bildet das gesamte Blatt auf die $y$-Achse ab. $S_2$ (grün) erstellt den Hauptteil des Farnes. Sie verkleinert und dreht das gesamte Bild und stellt es auf das Ende des Stiels aus $S_1$. -$S_3$ bildet das gesamte Blatt auf das blaue Teilblatt unten Links ab. +$S_3$ bildet das gesamte Blatt auf das blaue Teilblatt unten links ab. $S_4$ spiegelt das Blatt und bildet es auf das magentafarbene Teilblatt ab. \subsection{Erzeugung eines Bildes zu einem IFS} Es gibt zwei verschiedene Methoden um das Bild zu einem IFS zu erzeugen. Die erste Methode ist wahrscheinlich die intuitivste. -Wir beginnen mit einm Startbild, zum Beispiel ein Schwarzes Quadrat, und bilden dieses mit den affinen Transformationen des IFS ab. +Wir beginnen mit einem Startbild, zum Beispiel ein Schwarzes Quadrat, und bilden dieses mit den affinen Transformationen des IFS ab. Das neue Bild, dass entsteht, ist die nächste Iterierte. Dieses wird wieder mit den Transformationen abgebildet. Wir wiederholen den letzten schritt, bis wir zufrieden mit der neusten Iterierten sind. @@ -213,10 +215,11 @@ Bis jetzt wurde immer davon gesprochen, die Transformationen auf die gesamte Men Bei komplizierteren IFS welche viele Iterationen brauchen, bis man den Attraktor erkennen kann, ist die erste Methode ziemlich rechenintensiv. Beim Chaosspiel werden die Transformationen nicht auf die Menge angewendet, sondern nur auf einen einzelnen Punkt. Der Startpunkt kann dabei ein beliebiger Punkt in $E$ sein. -Es wird bei jedem Iterationsschritt nur eine Transformation, welche zufällig gewählt wurde, angewendet. +Es wird bei jedem Iterationsschritt nur eine Transformation $S_i$, welche zufällig gewählt wurde, angewendet. + Da, wie wir beim Barnsley-Farn gut sehen, nicht jede Transformation gleich viel des Bildes ausmacht, werden diese beim Chaosspiel gewichtet. -Je mehr eine Transformation kontrahiert, desto weniger Punkte braucht es um die resultierende Teilabbildung darzustellen. -Im Fall des Barnsley-Fern wird $S_1$ in $1\%$, $S_2$ in $85\%$ und $S_3 \& S_4$ in $7\%$ der Iterationen ausgeführt. +Je mehr eine Transformation kontrahiert, desto weniger Punkte braucht es, um die resultierende Teilabbildung darzustellen. +Im Fall des Barnsley-Fern wird $S_1$ in $1\%$, $S_2$ in $85\%$ und $S_3$ und $S_4$ in $7\%$ der Iterationen ausgeführt. Wir sehen auch in Abbildung \ref{ifs:farncolor} gut, dass der rote Stiel, $S_1$, einiges weniger Punkte braucht als der grüne Hauptteil des Blattes, $S_2$. In Abbildung \ref{ifs:farnNoWeight} wurden die vier gleich stark gewichtet. diff --git a/buch/papers/ifs/teil3.tex b/buch/papers/ifs/teil3.tex index 78fb935..1d39c6f 100644 --- a/buch/papers/ifs/teil3.tex +++ b/buch/papers/ifs/teil3.tex @@ -6,32 +6,30 @@ \section{Fraktale Bildkomprimierung \label{ifs:section:teil3}} \rhead{Fraktale Bildkomprimierung} -Mit dem Prinzip dieser IFS ist es auch möglich Bilder zu Komprimieren. -Diese Idee hatte der Mathematiker Michael Barnsley, welcher mit seinem Buch Fractals Everywhere einen wichtigen Beitrag zum Verständnis von Fraktalen geliefert hat. +Mit dem Prinzip dieser IFS ist es auch möglich, Bilder zu komprimieren. +Diese Idee hatte der Mathematiker Michael Barnsley, welcher mit seinem Buch {\em Fractals Everywhere} einen wichtigen Beitrag zum Verständnis von Fraktalen geliefert hat. Das Ziel ist es ein IFS zu finden, welches das Bild als Attraktor hat. In diesem Unterkapitel wollen wir eine Methode dafür anschauen, wie sie in \cite{ifs:Rousseau2012} beschrieben ist. Es ist wohl nicht falsch zu sagen, dass Ähnlichkeiten zur gesamten Menge, wie wir sie zum Beispiel beim Barnsley Farn gesehen haben, bei Bilder aus dem Alltag eher selten anzutreffen sind. Ein IFS, wie wir es in \ref{ifs:subsection:IteratedFunktionensysteme} definiert haben, wird uns also nicht weiter helfen. -Die Lösung dazu sind Partitionierte IFS (PIFS) \cite{ifs:pifs}. +Anders sieht es mit Partitionierten IFS (PIFS) \cite{ifs:pifs} aus. In \ref{ifs:transformation} wurde definiert, dass die Kontraktionen $S_i$ bei IFS auf die gesamte Menge $E$ angewendet werden. Bei einem PIFS wird der Attraktor in disjunkte Teilmengen aufgeteilt. Für jede dieser Teilmengen $R_i$ braucht es dann eine grössere Teilmenge, welche mit einer affinen Transformation eine zu $R_i$ ähnliche Menge bildet. -Wir müssen nicht mehr Ähnlichkeiten zum ganzen Bild finden, sondern zwischen Teilen des Bildes. +Wir müssen nicht mehr Ähnlichkeiten zum ganzen Bild finden, sondern nur zwischen Teilen des Bildes. Doch wie finden wir das PIFS, welches das Bild als Attraktor hat? -\subsection{das Kompressionsverfahren +\subsection{Das Kompressionsverfahren \label{ifs:subsection:malorum}} Wir beschränken das Verfahren für Graustufenbilder. Wie das Verfahren für Farbbilder verwendet werden kann, wird später erläutert. -Ein Graustufenbild kann man als Pixelraster mit einer x und y Achse verstehen. +Ein Graustufenbild kann man als Pixelraster mit einer $x$ und $y$ Achse verstehen. Jedem dieser Pixel wird ein Grauwert zugeordnet. -Ein Bild ist also eine Funktion, die jedem Pixel einen Grauwert $z$ zuweist -\begin{align*} - z = f(x,y). -\end{align*} +Ein Bild ist also eine Funktion, die jedem Pixel einen Grauwert \(z = f(x,y)\) zuweist. + +Wir suchen ein PIFS, welches das zu komprimierende Bild als Attraktor hat. +In einem ersten Schritt teilen wir das Bild in disjunkte benachbarte $b \times b$ Pixel-Quadrate auf. Diese Blöcke nennen wir Range-Blöcke der Menge $R=\{R_0,R_1,...R_m\}$. Diese sind als Raster im rechten Bild der Abbildung \ref{ifs:FIC} dargestellt. -Wir suchen ein PIFS welches das zu komprimierende Bild als Attraktor hat. -In einem ersten Schritt teilen wir das Bild in disjunkte benachbarte $b \times b$ Pixel-Quadrate auf. Diese Blöcke nennen wir Range-Blöcke der Menge $R=\{R_0,R_1,...R_m\}$ Im nächsten Schritt teilen wir das Bild in alle möglichen $2b \times 2b$ Pixel-Quadrate auf. Diese sind die Domain-Blöcke der Menge $D = \{D_0,D_1,...D_n\}$. Im dritten und letzten Schritt wird für jeden Range-Block $R_i$ ein Domain-Block $D_j$ gesucht, welcher ihm am ähnlichsten ist. Zwei Beispiele wie solche Domain-, und Range-Block Paare aussehen können, sehen wir in Abbildung \ref{ifs:FIC} @@ -57,8 +55,10 @@ Zuerst brauchen wir die Transformation g_i \end{pmatrix} \end{align*} -um ein Element aus $D$ auf ein Element von $R$ Abzubilden. -Wenn wir die Grauwerte ausser acht lassen, haben wir die affine Abbildung +um ein Element aus $D$ auf ein Element von $R$ Abzubilden. +Das bestimmen der besten Transformation kann man in drei Schritte aufteilen. + +\textbf{Schritt 1: }Wenn wir die Grauwerte ausser acht lassen, haben wir die affine Abbildung \begin{align} t_i(x,y) = \begin{pmatrix} @@ -84,38 +84,46 @@ Wir sind auf folgende acht Abbildungen beschränkt: \item Spiegelung an der vertikalen, horizontalen und den Diagonalachsen. \end{itemize} Da wir ein $2b \times 2b$ Feld auf ein $b \times b$ Feld abbilden möchten, müssen wir zuerst $G_j$ um $1/2$ skalieren. -Dies erreichen wir, indem wir alle disjunkten $2 \times 2$ px Blöcke mit einem Pixel des Grautones deren Mittelwertes ersetzen. +Dies erreichen wir, indem wir alle disjunkten $2 \times 2$ Pixel Blöcke mit einem Pixel des Grautones deren Mittelwertes ersetzen. - -Die Parameter $s_i$ und $g_i$ beschreiben die Änderung des Grautones. $s$ verändert den Kontrast und $g$ verschiebt die Grautöne auf die richtige Helligkeit, sie bilden die lineare Funktion +\textbf{Schritt 2: }Es muss nicht nur eine geometrische Abbildung, sondern auch eine Abbildung für die Grautöne gewählt werden. Letztere lässt sich mit den Parametern $s_i$ und $g_i$ beschrieben. +Wir suchen einen linearen Zusammenhang zwischen den Grautönen des Domain-, und Range-Block. $s_i$ verändert den Kontrast und $g_i$ verschiebt die Grautöne auf die richtige Helligkeit, sie bilden die lineare Funktion \begin{align*} z' = s_i z + g_i. \end{align*} Für die Bestimmung dieser Parameter führen wir zuerst die Bildfunktionen $f_{R_i}$ und $\tilde{f_{R_i}}$ ein. -$f_{R_i}$ ist die Bildfunktion des Range-Blockes $R_i$ und $\tilde{f_{R_i}}$ ist die Bildfunktion des zuerst Skalierten und dann mit \ref{ifs:affTrans} transformierten Domain-Blocks $D_j$. +$f_{R_i}$ ist die Bildfunktion des Range-Blockes $R_i$ und $\tilde{f_{R_i}}$ ist die Bildfunktion des zuerst skalierten und dann mit \eqref{ifs:affTrans} transformierten Domain-Blocks $D_j$. -Wir suchen $s_i$ und $g_i$ so das +Wir suchen $s_i$ und $g_i$ so das der quadratische Abstand zwischen \begin{align*} - f_{R_i} = s_i \tilde{f_{R_i}} + g_i = \bar{f_{R_i}}. + \bar{f_{R_i}} = s_i \tilde{f_{R_i}} + g_i \end{align*} -Die Parameter lassen sich mit +und $f_{R_i}$ am kleinsten ist. +Dies ist ein klassisches Problem der linearen Regression. Die Parameter lassen sich mit \begin{align*} - s = \frac{\operatorname{cov}(f_{R_i}), f(\tilde{f_{R_i}}))}{\operatorname{var}(\tilde{f_{R_i}})} \\ - g = E(f_{R_i}) - s E(f(\tilde{f_{R_i}})) + s_i = \frac{\operatorname{cov}(f_{R_i}, \tilde{f_{R_i}})}{\operatorname{var}(\tilde{f_{R_i}})} \\ + g_i = E(f_{R_i}) - s E(\tilde{f_{R_i}}) \end{align*} berechnen. +Die Varianz und Kovarianz erstrecken sich über die Grauwerte der Pixel der Blöcke. Mit diesen Parametern haben wir nun die Transformation vollständig bestimmt. -Um zu beurteilen wie ähnlich der Domain-Block $D_j$ mit der gefundenen Transformation $T$ dem Range-Block ist, berechnet man den quadratischen Abstand + +Um zu beurteilen wie ähnlich der Domain-Block $D_j$ mit der gefundenen Transformation $T$ dem Range-Block ist, berechnet man den quadratischen Fehler \begin{align*} e = d(f_{R_i}, \bar{f_{R_i}}). \end{align*} -Dieser Abstand sollte so klein wie möglich sein. +$e$ sollte so klein wie möglich sein. + +\textbf{Schritt 3: } +Somit haben wir die zwei Schritte um eine Transformation $T_i$ zu finden. +Wir führen den zweiten Schritt für jede der acht möglichen affinen Abbildungen vom ersten Schritt aus, und bestimmen den jeweilig resultierenden Fehler $e$. +Es resultieren acht $T_j$ mit ihren jeweiligen Fehlern. -Wir bestimmen die Parameter $s$ und $g$ für jede der acht möglichen affinen Abbildungen und das mit jedem Domain-Block. -Die Kombination von $D_j$ und $T_i$, welche den kleinsten Abstand $e$ hat, ist die beste. +Um den besten Domain-Block zu finden, führen wir die drei Schritte für jeden Domain-Block aus. +Der Domain-Block $D_j$, welcher die Transformation $T_j$ mit dem kleinsten Fehler $e$ hat, ist der ähnlichste. -Diese Schritte führen wir für jeden Range-Block $R_i$ aus. -Am Ende des Algorithmus haben wir für jeden Range-Block den zugehörigen Domain-Block und Transformation gefunden. +Wir suchen nun für jeden Range-Block $R_i$ den ähnlichsten Domain-Block. +Am Ende des Algorithmus haben wir für jeden Range-Block den zugehörigen Domain-Block und die dazugehörige Transformation gefunden. \begin{figure} \centering @@ -140,19 +148,18 @@ Teilt man ein Bild in die drei Farbkanäle auf, das heisst, es wird nur noch ein Nun wendet man auf jeden dieser Farbkanalbilder den Algorithmus an, und fügt nach der Rekonstruktion die Kanäle wieder zusammen. \subsubsection{Performance des Verfahren} -Dieser Grundalgorithmus der fraktalen Bildkompression ist recht langsam und skaliert auch schlecht für grössere Bilder. -Dies resultiert aus eigenen Experimenten. +Experimentelle Beobachtungen haben gezeigt, dass dieser Grundalgorithmus der fraktalen Bildkompression recht langsam ist und auch schlecht für grössere Bilder skaliert. Man kann die Laufzeit zwar verbessern indem man die Domain-Blöcke auch disjunkt macht, und für weniger detailreiche Bilder ein grösseres $b$ wählt, jedoch wird er auch so nicht so schnell wie zum Beispiel das JPEG-Verfahren. Es wurden bessere Algorithmen der fraktalen Bildkompression entwickelt, doch auch diese können, vor allem in der Laufzeit, noch nicht mit herkömmlichen Komprimierungsverfahren mithalten. \subsection{Beispiel} -Wir Verwenden dafür den oben beschriebenen Algorithmus, welcher uns für jeden Range-Block die benötigten Parameter liefert. +Wir verwenden dafür den oben beschriebenen Algorithmus, welcher uns für jeden Range-Block die benötigten Parameter liefert. Mit diesen lässt sich das Bild im Anschluss wieder Rekonstruieren. -Die Range-Blöcke wurden $4\times4$ gewählt und die Dommain dementsprechend $8\times8$. +Die Range-Blöcke wurden $4\times4$ gewählt und die Domain dementsprechend $8\times8$. Um etwas Zeit bei der Komprimierung zu ersparen, wurden nur disjunkte Domain-Blöcke gebraucht. -Als erstes Beispiel wählen wir das 360x360px Bild von Rapperswil in Abbildung \ref{ifs:original}. -Das Startbild ist ein mittelgraues 360x360px Bild, Abbildung \ref{ifs:bild0}. -Es kann jedoch ein beliebiges Startbild +Als erstes Beispiel wählen wir das 360$\times$360px Bild von Rapperswil in Abbildung \ref{ifs:original}. +Das Startbild ist ein mittelgraues 360$\times$360px Bild, Abbildung \ref{ifs:bild0}. +Es kann jedoch ein beliebiges Startbild sein. Nun lassen wir das PIFS laufen. Wie wir in Abbildung \ref{ifs:rappirecoa} sehen, ist schon nach der ersten Iteration das Bild schon erkennbar. Nach der fünften Iteration , Abbildung \ref{ifs:rappirecoc} gibt es fast keinen Unterschied mehr zur letzten Iteration, wir können die Rekonstruktion beenden. diff --git a/buch/papers/multiplikation/Makefile b/buch/papers/multiplikation/Makefile index 8f04c2c..8f04c2c 100644..100755 --- a/buch/papers/multiplikation/Makefile +++ b/buch/papers/multiplikation/Makefile diff --git a/buch/papers/multiplikation/Makefile.inc b/buch/papers/multiplikation/Makefile.inc index b78d67e..074020f 100644..100755 --- a/buch/papers/multiplikation/Makefile.inc +++ b/buch/papers/multiplikation/Makefile.inc @@ -7,8 +7,7 @@ dependencies-multiplikation = \ papers/multiplikation/packages.tex \ papers/multiplikation/main.tex \ papers/multiplikation/references.bib \ - papers/multiplikation/teil0.tex \ - papers/multiplikation/teil1.tex \ - papers/multiplikation/teil2.tex \ - papers/multiplikation/teil3.tex + papers/multiplikation/einlteung.tex \ + papers/multiplikation/loesungsmethoden.tex \ + papers/multiplikation/problemstellung.tex diff --git a/buch/papers/multiplikation/code/Figure_1.png b/buch/papers/multiplikation/code/Figure_1.png Binary files differnew file mode 100755 index 0000000..9def15a --- /dev/null +++ b/buch/papers/multiplikation/code/Figure_1.png diff --git a/buch/papers/multiplikation/code/MM b/buch/papers/multiplikation/code/MM Binary files differnew file mode 100755 index 0000000..f07985f --- /dev/null +++ b/buch/papers/multiplikation/code/MM diff --git a/buch/papers/multiplikation/code/MM.c b/buch/papers/multiplikation/code/MM.c new file mode 100755 index 0000000..04c4dab --- /dev/null +++ b/buch/papers/multiplikation/code/MM.c @@ -0,0 +1,465 @@ +#include <stdio.h>
+#include <stdint.h>
+#include <stdlib.h>
+#include <time.h>
+#include <omp.h>
+#include "c_matrix.h"
+#include <gsl/gsl_cblas.h>
+#include <string.h>
+
+void MM(int *A, int *B, int *C, int n);
+void openMP_MM(int *A, int *B, int *C, int n);
+void winograd(int *A, int *B, int *C, int n);
+int winograd_inner(int *a, int *b, int n);
+void run_algo(void (*algo)(), char alog_name[], int print);
+void run_algo_cblas(int print);
+void MM_dc(int *A, int *B, int *C, int n);
+void strassen(int *A, int *B, int *C, int n);
+void printMatrix(int *C, int n);
+void printMatrix_double(double *C, int n);
+void split(int *in, int *out, int n, int col, int row);
+void join(int *in, int *out, int n, int col, int row);
+void add(int *A, int *B, int *C, int n);
+void sub(int *A, int *B, int *C, int n);
+void multiply(int *A, int *B, int *C, int n);
+
+int main() {
+ // omp_set_dynamic(0);
+ // omp_set_num_threads(4);
+// run_algo(openMP_MM, "openMP_MM",0);
+ run_algo(MM_dc, "MM_dc",0);
+ run_algo(strassen, "strassen",0);
+
+ run_algo(MM, "MM", 0);
+ // run_algo(winograd, "winograd", 0);
+ run_algo_cblas(0);
+
+ return 0;
+}
+
+void MM(int *A, int *B, int *C, int n) {
+ for (int i = 0; i < n; ++i) {
+ for (int j = 0; j < n; ++j) {
+ int sum = 0;
+ for (int k = 0; k < n; ++k) {
+ sum += (*((A + i * n) + k)) * (*((B + k * n) + j));
+ }
+ *((C + i * n) + j) = sum;
+ }
+ }
+}
+
+int winograd_inner(int *a, int *b, int n){
+ int ab = 0;
+ if(n%2==0)
+ {
+ int xi = 0;
+ int etha = 0;
+ for(int i = 0; i<n/2;++i)
+ {
+ xi += a[2*i]*a[2*i+1];
+ etha += b[2*i]*b[2*i+1];
+ ab += (a[2*i]+b[2*i+1])*(a[2*i+1]+b[2*i]);
+ }
+ ab = ab-etha-xi;
+ }
+ return ab;
+ }
+
+ void winograd(int *A, int *B, int *C, int n) {
+
+ int xi_array[n];
+ int etha_array[n];
+ int xi = 0;
+ int etha = 0;
+ int ab = 0;
+
+ for (int i = 0; i < n; ++i) {
+ xi = 0;
+ etha = 0;
+ for(int k = 0;k<n/2;++k)
+ {
+ xi += (*((A + i * n) + 2*k))*(*((A + i * n) + (2*k+1)));
+ etha += (*((B + 2*k * n) + i))*(*((B + (2*k+1) * n) + i));
+ }
+ xi_array[i] = xi;
+ etha_array[i] = etha;
+ }
+
+ for (int i = 0; i < n; ++i) {
+ for (int j = 0; j < n; ++j) {
+ ab = 0;
+ for(int k = 0;k<n/2;++k)
+ {
+ ab += ((*((A + i * n) + 2*k))+(*((B + (2*k+1) * n) + j)))*((*((A + i * n) + (2*k+1)))+(*((B + 2*k * n) + j)));
+ }
+ *((C + i * n) + j) = ab-etha_array[j]-xi_array[i];
+ }
+ }
+
+
+
+
+ // for (int i = 0; i < n; ++i) {
+ // int *a = (int*) malloc(n * sizeof(int));
+ // for(int k = 0; k<n; ++k)
+ // {
+ // a[k] = (*((A + i * n) + k));
+ // }
+ //
+ // for (int j = 0; j < n; ++j) {
+ // int *b = (int*) malloc(n * sizeof(int));
+ // for(int k = 0; k<n; ++k)
+ // {
+ // b[k] =(*((B + k * n) + j));
+ // }
+ // *((C + i * n) + j) = winograd_inner(a,b,n);
+ // }
+ // }
+ }
+
+
+void openMP_MM(int *A, int *B, int *C, int n) {
+
+ #pragma omp parallel for
+ for (int i = 0; i < n; ++i) {
+ for (int j = 0; j < n; ++j) {
+ int sum = 0;
+ for (int k = 0; k < n; ++k) {
+ sum += (*((A + i * n) + k)) * (*((B + k * n) + j));
+ }
+ *((C + i * n) + j) = sum;
+ }
+ }
+}
+
+void MM_dc(int *A, int *B, int *C, int n) {
+ if (n <= 2) {
+ MM((int*) A, (int*) B, (int*) C, n);
+ } else {
+ int *A11 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *A12 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *A21 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *A22 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *B11 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *B12 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *B21 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *B22 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+
+ split((int*) A, (int*) A11, n / 2, 0, 0);
+ split((int*) A, (int*) A12, n / 2, 0, n / 2);
+ split((int*) A, (int*) A21, n / 2, n / 2, 0);
+ split((int*) A, (int*) A22, n / 2, n / 2, n / 2);
+ split((int*) B, (int*) B11, n / 2, 0, 0);
+ split((int*) B, (int*) B12, n / 2, 0, n / 2);
+ split((int*) B, (int*) B21, n / 2, n / 2, 0);
+ split((int*) B, (int*) B22, n / 2, n / 2, n / 2);
+
+ int *tmp1 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *tmp2 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *tmp3 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *tmp4 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *tmp5 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *tmp6 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *tmp7 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *tmp8 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+
+ MM_dc((int*) A11, (int*) B11, (int*) tmp1, n / 2);
+ MM_dc((int*) A12, (int*) B21, (int*) tmp2, n / 2);
+ MM_dc((int*) A11, (int*) B12, (int*) tmp3, n / 2);
+ MM_dc((int*) A12, (int*) B22, (int*) tmp4, n / 2);
+ MM_dc((int*) A21, (int*) B11, (int*) tmp5, n / 2);
+ MM_dc((int*) A22, (int*) B21, (int*) tmp6, n / 2);
+ MM_dc((int*) A21, (int*) B12, (int*) tmp7, n / 2);
+ MM_dc((int*) A22, (int*) B22, (int*) tmp8, n / 2);
+
+ free(A11);
+ free(A12);
+ free(A21);
+ free(A22);
+ free(B11);
+ free(B12);
+ free(B21);
+ free(B22);
+
+ int *C11 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *C12 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *C21 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *C22 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+
+ add((int*) tmp1, (int*) tmp2, (int*) C11, n / 2);
+ add((int*) tmp3, (int*) tmp4, (int*) C12, n / 2);
+ add((int*) tmp5, (int*) tmp6, (int*) C21, n / 2);
+ add((int*) tmp7, (int*) tmp8, (int*) C22, n / 2);
+
+ free(tmp1);
+ free(tmp2);
+ free(tmp3);
+ free(tmp4);
+ free(tmp5);
+ free(tmp6);
+ free(tmp7);
+ free(tmp8);
+
+ join((int*) C11, (int*) C, n / 2, 0, 0);
+ join((int*) C12, (int*) C, n / 2, 0, n / 2);
+ join((int*) C21, (int*) C, n / 2, n / 2, 0);
+ join((int*) C22, (int*) C, n / 2, n / 2, n / 2);
+
+ free(C11);
+ free(C12);
+ free(C21);
+ free(C22);
+
+ }
+}
+
+void strassen(int *A, int *B, int *C, int n) {
+ if (n <= 2) {
+
+ int P, Q, R, S, T, U, V;
+ P = ((*((A + 0 * n) + 0)) + (*((A + 1 * n) + 1)))
+ * ((*((B + 0 * n) + 0)) + (*((B + 1 * n) + 1)));
+ Q = ((*((A + 1 * n) + 0)) + (*((A + 1 * n) + 1)))
+ * ((*((B + 0 * n) + 0)));
+ R = ((*((A + 0 * n) + 0)))
+ * ((*((B + 0 * n) + 1)) - (*((B + 1 * n) + 1)));
+ S = ((*((A + 1 * n) + 1)))
+ * ((*((B + 1 * n) + 0)) - (*((B + 0 * n) + 0)));
+ T = ((*((A + 0 * n) + 0)) + (*((A + 0 * n) + 1)))
+ * ((*((B + 1 * n) + 1)));
+ U = ((*((A + 1 * n) + 0)) - (*((A + 0 * n) + 0)))
+ * ((*((B + 0 * n) + 0)) + (*((B + 0 * n) + 1)));
+ V = ((*((A + 0 * n) + 1)) - (*((A + 1 * n) + 1)))
+ * ((*((B + 1 * n) + 0)) + (*((B + 1 * n) + 1)));
+ (*((C + 0 * n) + 0)) = P + S - T + V;
+ (*((C + 0 * n) + 1)) = R + T;
+ (*((C + 1 * n) + 0)) = Q + S;
+ (*((C + 1 * n) + 1)) = P + R - Q + U;
+
+ } else {
+ int *A11 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *A12 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *A21 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *A22 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *B11 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *B12 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *B21 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *B22 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+
+ split((int*) A, (int*) A11, n / 2, 0, 0);
+ split((int*) A, (int*) A12, n / 2, 0, n / 2);
+ split((int*) A, (int*) A21, n / 2, n / 2, 0);
+ split((int*) A, (int*) A22, n / 2, n / 2, n / 2);
+ split((int*) B, (int*) B11, n / 2, 0, 0);
+ split((int*) B, (int*) B12, n / 2, 0, n / 2);
+ split((int*) B, (int*) B21, n / 2, n / 2, 0);
+ split((int*) B, (int*) B22, n / 2, n / 2, n / 2);
+
+ int *P = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *Q = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *R = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *S = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *T = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *U = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *V = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+
+ int *addA = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *addB = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+
+ add((int*) A11, (int*) A22, (int*) addA, n / 2);
+ add((int*) B11, (int*) B22, (int*) addB, n / 2);
+ strassen((int*) addA, (int*) addB, (int*) P, n / 2);
+
+ add((int*) A21, (int*) A22, (int*) addA, n / 2);
+ strassen((int*) addA, (int*) B11, (int*) Q, n / 2);
+
+ sub((int*) B12, (int*) B22, (int*) addB, n / 2);
+ strassen((int*) A11, (int*) addB, (int*) R, n / 2);
+
+ sub((int*) B21, (int*) B11, (int*) addB, n / 2);
+ strassen((int*) A22, (int*) addB, (int*) S, n / 2);
+
+ add((int*) A11, (int*) A12, (int*) addA, n / 2);
+ strassen((int*) addA, (int*) B22, (int*) T, n / 2);
+
+ sub((int*) A21, (int*) A11, (int*) addA, n / 2);
+ add((int*) B11, (int*) B12, (int*) addB, n / 2);
+ strassen((int*) addA, (int*) addB, (int*) U, n / 2);
+
+ sub((int*) A12, (int*) A22, (int*) addA, n / 2);
+ add((int*) B21, (int*) B22, (int*) addB, n / 2);
+ strassen((int*) addA, (int*) addB, (int*) V, n / 2);
+
+ free(A11);
+ free(A12);
+ free(A21);
+ free(A22);
+ free(B11);
+ free(B12);
+ free(B21);
+ free(B22);
+ free(addA);
+ free(addB);
+
+ int *C11 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *C12 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *C21 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *C22 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+
+ int *resAdd1 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+ int *resAdd2 = (int*) malloc(n / 2 * n / 2 * sizeof(int));
+
+ add((int*) R, (int*) T, (int*) C12, n / 2);
+ add((int*) Q, (int*) S, (int*) C21, n / 2);
+
+ add((int*) P, (int*) S, (int*) resAdd1, n / 2);
+ add((int*) resAdd1, (int*) V, (int*) resAdd2, n / 2);
+ sub((int*) resAdd2, (int*) T, (int*) C11, n / 2);
+
+ add((int*) P, (int*) R, (int*) resAdd1, n / 2);
+ add((int*) resAdd1, (int*) U, (int*) resAdd2, n / 2);
+ sub((int*) resAdd2, (int*) Q, (int*) C22, n / 2);
+
+ free(P);
+ free(Q);
+ free(R);
+ free(S);
+ free(T);
+ free(U);
+ free(V);
+ free(resAdd1);
+ free(resAdd2);
+
+ join((int*) C11, (int*) C, n / 2, 0, 0);
+ join((int*) C12, (int*) C, n / 2, 0, n / 2);
+ join((int*) C21, (int*) C, n / 2, n / 2, 0);
+ join((int*) C22, (int*) C, n / 2, n / 2, n / 2);
+
+ free(C11);
+ free(C12);
+ free(C21);
+ free(C22);
+ }
+}
+
+void add(int *A, int *B, int *C, int n) {
+ for (int i = 0; i < n; i++) {
+ for (int j = 0; j < n; j++) {
+ *((C + i * n) + j) = *((A + i * n) + j) + *((B + i * n) + j);
+ }
+ }
+}
+
+void sub(int *A, int *B, int *C, int n) {
+ for (int i = 0; i < n; i++) {
+ for (int j = 0; j < n; j++) {
+ *((C + i * n) + j) = *((A + i * n) + j) - *((B + i * n) + j);
+ }
+ }
+}
+
+void multiply(int *A, int *B, int *C, int n) {
+ int mul;
+
+ for (int i = 0; i < n; ++i) {
+ for (int j = 0; j < n; ++j) {
+ mul = (*((A + i * n) + j)) * (*((B + i * n) + j));
+ *((C + i * n) + j) = mul;
+ }
+ }
+}
+
+void split(int *in, int *out, int n, int col, int row) {
+ for (int i1 = 0, i2 = col; i1 < n; i1++, i2++)
+ for (int j1 = 0, j2 = row; j1 < n; j1++, j2++) {
+ *((out + i1 * n) + j1) = *((in + i2 * n * 2) + j2);
+
+ }
+}
+
+void join(int *in, int *out, int n, int col, int row) {
+ for (int i1 = 0, i2 = col; i1 < n; i1++, i2++)
+ for (int j1 = 0, j2 = row; j1 < n; j1++, j2++)
+ *((out + i2 * n * 2) + j2) = *((in + i1 * n) + j1);
+}
+
+void printMatrix(int *C, int n) {
+ for (int i = 0; i < n; ++i) {
+ for (int j = 0; j < n; ++j) {
+ printf("%d ", *((C + i * n) + j));
+ }
+ printf("\n");
+ }
+}
+
+void printMatrix_double(double *C, int n) {
+ for (int i = 0; i < n; ++i) {
+ for (int j = 0; j < n; ++j) {
+ printf("%.0f ", *((C + i * n) + j));
+ }
+ printf("\n");
+ }
+}
+
+void run_algo(void (*algo)(), char alog_name[], int print)
+{
+ FILE *fptr;
+
+ char fileName[40] = "meas/";
+ strcat(fileName, alog_name);
+ strcat(fileName, ".txt");
+ fptr = fopen(fileName, "w");
+
+
+ for(int i=0; i<n_arrays; ++i)
+ {
+ for(int j = 0; j<1; ++j)
+ {
+ int *C = (int*) malloc(n[i] * n[i] * sizeof(int));
+ double dtime = omp_get_wtime();
+ algo(Ap[i], Bp[i], (int*) C, n[i]);
+ dtime = omp_get_wtime() - dtime;
+ // printf("The %s program took %f seconds to execute \n", alog_name, dtime);
+ fprintf(fptr, "%f,%d\n", dtime, n[i]);
+
+ if(print==1)
+ {
+ printMatrix((int*)C, n[i]);
+ }
+ free(C);
+ }
+ }
+ fclose(fptr);
+
+}
+
+void run_algo_cblas(int print)
+{
+
+ FILE *fptr;
+
+ fptr = fopen("meas/blas.txt", "w");
+ for(int i=0; i<n_arrays; ++i)
+ {
+ for(int j = 0; j<1; ++j)
+ {
+ double *dC = (double*) malloc(n[i] * n[i] * sizeof(double));
+ double dtime = omp_get_wtime();
+ cblas_dgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans, n[i], n[i], n[i], 1.0, dAp[i], n[i],
+ dBp[i], n[i], 0.0, dC, n[i]);
+ dtime = omp_get_wtime() - dtime;
+ // printf("The cblas program took %f seconds to execute \n", dtime);
+ fprintf(fptr, "%f,%d\n",dtime, n[i]);
+
+ if(print==1)
+ {
+ printMatrix_double( (double*)dC, n[i]);
+ }
+
+ free(dC);
+ }
+ }
+ fclose(fptr);
+
+}
diff --git a/buch/papers/multiplikation/code/MM.py b/buch/papers/multiplikation/code/MM.py new file mode 100644 index 0000000..626b82d --- /dev/null +++ b/buch/papers/multiplikation/code/MM.py @@ -0,0 +1,311 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Fri Mar 19 07:31:29 2021 + +@author: nunigan +""" +import numpy as np +import time +import matplotlib.pyplot as plt +from scipy.optimize import curve_fit +import tikzplotlib +def MM(A, B): + n = np.shape(A)[0] + C = np.zeros((n, n)) + for i in range(n): + for j in range(n): + C[i, j] = 0 + for k in range(n): + C[i, j] += A[i, k]*B[k, j] + return C + + +def MM_dc(A, B): + n = np.shape(A)[0] + if(n <= 2): + C = np.zeros((n, n)) + C[0, 0] = A[0, 0]*B[0, 0]+A[0, 1]*B[1, 0] + C[0, 1] = A[0, 0]*B[0, 1]+A[0, 1]*B[1, 1] + C[1, 0] = A[1, 0]*B[0, 0]+A[1, 1]*B[1, 0] + C[1, 1] = A[1, 0]*B[0, 1]+A[1, 1]*B[1, 1] + return C + else: + A11, A12, A21, A22 = A[:n//2, :n//2], A[:n//2, n//2:], A[n//2:, :n//2], A[n//2:, n//2:] + B11, B12, B21, B22 = B[:n//2, :n//2], B[:n//2, n//2:], B[n//2:, :n//2], B[n//2:, n//2:] + C11 = MM_dc(A11, B11) + MM_dc(A12, B21) + C12 = MM_dc(A11, B12) + MM_dc(A12, B22) + C21 = MM_dc(A21, B11) + MM_dc(A22, B21) + C22 = MM_dc(A21, B12) + MM_dc(A22, B22) + C = np.vstack((np.hstack((C11, C12)), np.hstack((C21, C22)))) + return C + + +def strassen(A, B): + n = np.shape(A)[0] + if(n <= 2): + C = np.zeros((n, n)) + P = (A[0, 0]+A[1, 1])*(B[0, 0]+B[1, 1]) + Q = (A[1, 0]+A[1, 1])*B[0, 0] + R = A[0, 0]*(B[0, 1]-B[1, 1]) + S = A[1, 1]*(B[1, 0]-B[0, 0]) + T = (A[0, 0]+A[0, 1])*B[1, 1] + U = (A[1, 0]-A[0, 0])*(B[0, 0]+B[0, 1]) + V = (A[0, 1]-A[1, 1])*(B[1, 0]+B[1, 1]) + C[0, 0] = P+S-T+V + C[0, 1] = R+T + C[1, 0] = Q+S + C[1, 1] = P+R-Q+U + return C + else: + m = n//2 + A11, A12, A21, A22 = A[:m, :m], A[:m, m:], A[m:, :m], A[m:, m:] + B11, B12, B21, B22 = B[:m, :m], B[:m, m:], B[m:, :m], B[m:, m:] + P = strassen((A11+A22),(B11+B22)) + Q = strassen((A21+A22),B11) + R = strassen(A11,(B12-B22)) + S = strassen(A22,(B21-B11)) + T = strassen((A11+A12),B22) + U = strassen((A21-A11),(B11+B12)) + V = strassen((A12-A22),(B21+B22)) + + C11 = P+S-T+V + C12 = R+T + C21 = Q+S + C22 = P+R-Q+U + + C = np.vstack((np.hstack((C11, C12)), np.hstack((C21, C22)))) + return C + +def winograd_inner(a, b): + n = np.shape(a)[0] + if n%2 == 0: + xi = np.sum(a[::2]*a[1::2]) + etha = np.sum(b[::2]*b[1::2]) + # print("xi = {}, etha = {}".format(xi, etha)) + ab = np.sum((a[::2]+b[1::2])*(a[1::2]+b[::2]))-xi-etha + else: + xi = np.sum(a[0:-1:2]*a[1::2]) + etha = np.sum(b[0:-1:2]*b[1::2]) + ab = np.sum((a[0:-1:2]+b[1::2])*(a[1::2]+b[0:-1:2]))-xi-etha+a[-1]*b[-1] + return ab + +def winograd(A, B): + m,n = np.shape(A) + n2,p = np.shape(B) + C = np.zeros((m,p)) + for i in range(np.shape(A)[0]): + for j in range(np.shape(B)[1]): + C[i,j] = winograd_inner(A[i,:], B[:,j]) + return C + +def winograd2(A, B): + m,n = np.shape(A) + n2,p = np.shape(B) + C = np.zeros((m,p)) + xi = np.zeros((m)) + eta = np.zeros((p)) + ab = 0 + for i in range(m): + for j in range(n//2): + xi[i] += A[i,2*j]*A[i,2*j+1] + + for i in range(p): + for j in range(n//2): + eta[i] += B[2*j,i]*B[2*j+1,i] + + if n%2==0: + for i in range(m): + for j in range(p): + ab = 0 + for k in range(n//2): + ab += (A[i,2*k]+B[2*k+1,j])*(A[i,2*k+1]+B[2*k,j]) + C[i,j] = ab-eta[j]-xi[i] + else: + for i in range(m): + for j in range(p): + ab = 0 + for k in range(n//2): + ab += (A[i,2*k]+B[2*k+1,j])*(A[i,2*k+1]+B[2*k,j]) + C[i,j] = ab-eta[j]-xi[i]+A[i,-1]*B[-1,j] + + return C + +def test_perfomance(n): + t_mm = [] + t_mm_dc = [] + t_mm_strassen = [] + t_wino = [] + t_np = [] + + for i in n: + A = np.random.randn(i, i) + B = np.random.randn(i, i) + # A = np.random.randint(-100, 100,(i, i)) + # B = np.random.randint(-100, 100,(i, i)) + + start = time.time() + C3 = strassen(A, B) + t_mm_strassen.append(time.time() - start) + + start = time.time() + C1 = MM(A, B) + t_mm.append(time.time() - start) + + start = time.time() + C2 = MM_dc(A, B) + t_mm_dc.append(time.time() - start) + + start = time.time() + C4 = winograd2(A, B) + t_wino.append(time.time() - start) + + start = time.time() + C = A@B + t_np.append(time.time() - start) + + plt.figure(figsize=(13,8)) + plt.rcParams['font.family'] = 'STIXGeneral' + plt.rc('axes', labelsize=23) + plt.rc('xtick', labelsize=23) + plt.rc('ytick', labelsize=23) + plt.plot(n, t_mm, label='Standard', lw=5) + plt.plot(n, t_mm_dc, label='Divide and conquer', lw=5) + plt.plot(n, t_mm_strassen, label='Strassen', lw=5) + plt.plot(n, t_wino, label='Winograd', lw=5) + plt.plot(n, t_np, label='NumPy A@B', lw=5) + plt.legend() + plt.xlabel("n") + plt.ylabel("time (s)") + plt.grid(True) + plt.tight_layout() + # plt.yscale('log') + plt.legend(fontsize=19) + plt.savefig('meas_' + str(max(n))+ '.pdf') + arr = np.array([n, t_mm, t_mm_dc, t_mm_strassen, t_wino, t_np]) + np.savetxt('meas_' + str(max(n))+ '.txt',arr) + return arr + + +def plot(num): + arr = np.loadtxt('meas_{}.txt'.format(num)) + n, t_mm, t_mm_dc, t_mm_strassen, t_wino, t_np = arr + plt.figure(figsize=(13,8)) + plt.rcParams['font.family'] = 'STIXGeneral' + plt.rc('axes', labelsize=23) + plt.rc('xtick', labelsize=23) + plt.rc('ytick', labelsize=23) + plt.plot(n, t_mm, label='3 For Loops', lw=5) + plt.plot(n, t_mm_dc, label='Divide and Conquer', lw=5) + plt.plot(n, t_mm_strassen, label='Strassen', lw=5) + # plt.plot(n, t_wino, label='Winograd', lw=5) + plt.plot(n, t_np, label='NumPy A@B', lw=5) + plt.legend() + plt.xlabel("n") + plt.ylabel("time (s)") + plt.grid(True) + plt.tight_layout() + # plt.yscale('log') + plt.legend(fontsize=19) + plt.savefig('meas_' + str(num)+ '.pdf') + return arr + +def plot_c_res(ave, num): + MM = np.loadtxt("meas/MM.txt", delimiter=',') + # winograd = np.loadtxt("meas/winograd.txt", delimiter=',') + blas = np.loadtxt("meas/blas.txt", delimiter=',') + MM_dc = np.loadtxt("meas/MM_dc.txt", delimiter=',') + strassen = np.loadtxt("meas/strassen.txt", delimiter=',') + + MM_t = MM[:,0] + MM_n = MM[:,1] + MM_t = np.mean(MM_t.reshape(-1,ave),axis=1) + MM_n = np.mean(MM_n.reshape(-1,ave),axis=1) + + MM_dc_t = MM_dc[:,0] + MM_dc_n = MM_dc[:,1] + MM_dc_t = np.mean(MM_dc_t.reshape(-1,ave),axis=1) + MM_dc_n = np.mean(MM_dc_n.reshape(-1,ave),axis=1) + + strassen_t = strassen[:,0] + strassen_n = strassen[:,1] + strassen_t = np.mean(strassen_t.reshape(-1,ave),axis=1) + strassen_n = np.mean(strassen_n.reshape(-1,ave),axis=1) + + # winograd_t = winograd[:,0] + # winograd_n = winograd[:,1] + # winograd_t = np.mean(winograd_t.reshape(-1,ave),axis=1) + # winograd_n = np.mean(winograd_n.reshape(-1,ave),axis=1) + + blas_t = blas[:,0] + blas_n = blas[:,1] + blas_t = np.mean(blas_t.reshape(-1,ave),axis=1) + blas_n = np.mean(blas_n.reshape(-1,ave),axis=1) + + def func(x, a,b): + return b*x**a + + # popt, pcov = curve_fit(func, blas_n, blas_t) + # popt1, pcov2 = curve_fit(func, blas_n, winograd_t) + # popt2, pcov2 = curve_fit(func, blas_n, MM_t) + + plt.figure(figsize=(13,8)) + plt.rcParams['font.family'] = 'STIXGeneral' + plt.rc('axes', labelsize=23) + plt.rc('xtick', labelsize=23) + plt.rc('ytick', labelsize=23) + plt.plot(MM_n, MM_t, label='3 For Loops', lw=5) + # plt.plot(winograd_n, winograd_t, label='Winograd MM', lw=5) + plt.plot(blas_n, blas_t, label='Blas', lw=5) + plt.plot(strassen_n, strassen_t, label='Strassen', lw=5) + plt.plot(MM_dc_n, MM_dc_t, label='Divide and Conquer', lw=5) + plt.xlabel("n") + plt.ylabel("time (s)") + plt.grid(True) + plt.tight_layout() + plt.legend(fontsize=19) + plt.savefig('c_meas_' + str(num)+ '.pdf') + + # plt.plot(blas_n, func(blas_n, *popt), 'r-', label='fit blas: a=%5.5f, b=%5.10f' % tuple(popt)) + # plt.plot(blas_n, func(blas_n, *popt1), 'r-', label='fit winograd: a=%5.5f, b=%5.10f' % tuple(popt1)) + # plt.plot(blas_n, func(blas_n, *popt2), 'r-', label='fit MM: a=%5.5f, b=%5.10f' % tuple(popt2)) + + plt.legend() + + +# test%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +if __name__ == '__main__': + plot_c_res(1, 4096) + + + # plot(8) + # n = np.logspace(1,10,10,base=2,dtype=(np.int)) + # n = np.arange(1,50,2) + A = np.random.randint(-10, 10, (5,3)) + B = np.random.randint(-10, 10, (3,5)) + + C = winograd2(A, B) + C_test = A@B + print(C) + print(C_test) + # print(np.equal(C, C_test)) + + # t_np = test_perfomance(n) + # C = strassen(A, B) + # C_test = A@B + + + # plot_c_res() + # def func(x, a): + # return x**a + + # popt, pcov = curve_fit(func, n, t_np, bounds=(2, 3)) + + + # plt.figure() + # plt.plot(n, t_np, 'b-', label='data') + # plt.plot(n, func(n, *popt), 'r-', label='fit: a=%5.3f' % tuple(popt)) + # plt.xlabel('x') + # plt.ylabel('y') + # plt.legend() +
\ No newline at end of file diff --git a/buch/papers/multiplikation/code/__pycache__/MM.cpython-38.pyc b/buch/papers/multiplikation/code/__pycache__/MM.cpython-38.pyc Binary files differnew file mode 100644 index 0000000..7768772 --- /dev/null +++ b/buch/papers/multiplikation/code/__pycache__/MM.cpython-38.pyc diff --git a/buch/papers/multiplikation/code/c_matrix.h b/buch/papers/multiplikation/code/c_matrix.h new file mode 100644 index 0000000..13df55d --- /dev/null +++ b/buch/papers/multiplikation/code/c_matrix.h @@ -0,0 +1,101 @@ +/* Seminar Matrizen, autogenerated File, Michael Schmid, 30/05/2021, 22:00:57 */ + +#include <stdint.h> +const int A0[][2] = + { + {-15,68}, + {49,86} + }; +const int B0[][2] = + { + {33,73}, + {38,-76} + }; +const double dB0[][2] = + { + {33,73}, + {38,-76} + }; +const double dA0[][2] = + { + {-15,68}, + {49,86} + }; +const int A1[][4] = + { + {75,-38,-32,-65}, + {37,74,-31,29}, + {15,-62,-20,-20}, + {-31,-35,-89,47} + }; +const int B1[][4] = + { + {71,90,78,-98}, + {4,63,12,-47}, + {11,-44,75,-69}, + {95,-15,64,23} + }; +const double dB1[][4] = + { + {71,90,78,-98}, + {4,63,12,-47}, + {11,-44,75,-69}, + {95,-15,64,23} + }; +const double dA1[][4] = + { + {75,-38,-32,-65}, + {37,74,-31,29}, + {15,-62,-20,-20}, + {-31,-35,-89,47} + }; +const int A2[][8] = + { + {80,42,3,-16,6,55,87,16}, + {-99,-14,21,-1,-94,-56,91,10}, + {-47,-55,-59,62,12,-53,87,-65}, + {-60,94,-67,23,-62,33,-63,-72}, + {12,-75,16,21,22,-37,1,16}, + {-100,-99,82,-66,2,64,-13,44}, + {59,-100,-90,8,36,-24,18,88}, + {73,-58,75,-100,-19,-29,85,-19} + }; +const int B2[][8] = + { + {-61,88,69,49,-53,47,73,45}, + {16,14,-88,-11,-67,-73,-20,43}, + {-60,-63,26,32,-29,18,-44,-69}, + {1,21,21,38,7,-100,-61,-76}, + {-90,95,-99,88,49,-80,27,-36}, + {24,-12,-47,-7,29,15,52,37}, + {-98,-76,29,76,-41,-75,97,79}, + {62,-90,-35,-14,-30,-42,-95,52} + }; +const double dB2[][8] = + { + {-61,88,69,49,-53,47,73,45}, + {16,14,-88,-11,-67,-73,-20,43}, + {-60,-63,26,32,-29,18,-44,-69}, + {1,21,21,38,7,-100,-61,-76}, + {-90,95,-99,88,49,-80,27,-36}, + {24,-12,-47,-7,29,15,52,37}, + {-98,-76,29,76,-41,-75,97,79}, + {62,-90,-35,-14,-30,-42,-95,52} + }; +const double dA2[][8] = + { + {80,42,3,-16,6,55,87,16}, + {-99,-14,21,-1,-94,-56,91,10}, + {-47,-55,-59,62,12,-53,87,-65}, + {-60,94,-67,23,-62,33,-63,-72}, + {12,-75,16,21,22,-37,1,16}, + {-100,-99,82,-66,2,64,-13,44}, + {59,-100,-90,8,36,-24,18,88}, + {73,-58,75,-100,-19,-29,85,-19} + }; +const int *Ap[3] = {(int*) A0,(int*) A1,(int*) A2}; +const int *Bp[3] = {(int*) B0,(int*) B1,(int*) B2}; +const double *dAp[3] = {(double*) dA0,(double*) dA1,(double*) dA2}; +const double *dBp[3] = {(double*) dB0,(double*) dB1,(double*) dB2}; +int n[3] = {2,4,8}; +int n_arrays = 3; diff --git a/buch/papers/multiplikation/code/c_meas_1024.pdf b/buch/papers/multiplikation/code/c_meas_1024.pdf Binary files differnew file mode 100644 index 0000000..95b68b5 --- /dev/null +++ b/buch/papers/multiplikation/code/c_meas_1024.pdf diff --git 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b/buch/papers/multiplikation/code/c_meas_8.pdf Binary files differnew file mode 100644 index 0000000..9682aca --- /dev/null +++ b/buch/papers/multiplikation/code/c_meas_8.pdf diff --git a/buch/papers/multiplikation/code/helper_class.py b/buch/papers/multiplikation/code/helper_class.py new file mode 100755 index 0000000..485fa76 --- /dev/null +++ b/buch/papers/multiplikation/code/helper_class.py @@ -0,0 +1,105 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Fri Mar 12 09:02:48 2021 + +@author: nunigan +""" + +from datetime import datetime +import numpy as np + +class Helper(): + def __init__(self): + pass + + def write_c_matrix(self, n_array): + + with open('c_matrix.h', 'w') as file: + file.writelines('/* Seminar Matrizen, autogenerated File, Michael Schmid, {} */ \n \n'.format(datetime.now().strftime("%d/%m/%Y, %H:%M:%S"))) + + file.writelines('#include <stdint.h> \n') + + + + for k, n in enumerate(n_array): + A = np.random.randint(-100,100,(n,n)) + B = np.random.randint(-100,100,(n,n)) + file.writelines('const int A{}[][{}] = \n'.format(k, n)) + file.writelines(' {\n') + for i in range(n): + file.writelines(' {') + for j in range(n): + if j == n-1: + file.writelines('{}'.format(A[i,j])) + else: + file.writelines('{},'.format(A[i,j])) + if i == n-1: + file.writelines('}\n') + else: + file.writelines('},\n') + + file.writelines(' };\n') + + file.writelines('const int B{}[][{}] = \n'.format(k,n)) + file.writelines(' {\n') + for i in range(n): + file.writelines(' {') + for j in range(n): + if j == n-1: + file.writelines('{}'.format(B[i,j])) + else: + file.writelines('{},'.format(B[i,j])) + if i == n-1: + file.writelines('}\n') + else: + file.writelines('},\n') + + file.writelines(' };\n') + + file.writelines('const double dB{}[][{}] = \n'.format(k,n)) + file.writelines(' {\n') + for i in range(n): + file.writelines(' {') + for j in range(n): + if j == n-1: + file.writelines('{}'.format(B[i,j])) + else: + file.writelines('{},'.format(B[i,j])) + if i == n-1: + file.writelines('}\n') + else: + file.writelines('},\n') + + file.writelines(' };\n') + + file.writelines('const double dA{}[][{}] = \n'.format(k,n)) + file.writelines(' {\n') + for i in range(n): + file.writelines(' {') + for j in range(n): + if j == n-1: + file.writelines('{}'.format(A[i,j])) + else: + file.writelines('{},'.format(A[i,j])) + if i == n-1: + file.writelines('}\n') + else: + file.writelines('},\n') + + file.writelines(' };\n') + + file.writelines('const int *Ap[{}] = {{{}}}; \n'.format(len(n_array),",".join(['(int*) A'+str(element) for element in np.arange(len(n_array))]))) + file.writelines('const int *Bp[{}] = {{{}}}; \n'.format(len(n_array),",".join(['(int*) B'+str(element) for element in np.arange(len(n_array))]))) + file.writelines('const double *dAp[{}] = {{{}}}; \n'.format(len(n_array),",".join(['(double*) dA'+str(element) for element in np.arange(len(n_array))]))) + file.writelines('const double *dBp[{}] = {{{}}}; \n'.format(len(n_array),",".join(['(double*) dB'+str(element) for element in np.arange(len(n_array))]))) + file.writelines('int n[{}] = {{{}}}; \n'.format(len(n_array),",".join([str(element) for element in n_array]))) + file.writelines('int n_arrays = {};\n'.format(len(n_array))) + +# test%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +if __name__ == '__main__': + + helper = Helper() + # n = np.arange(2,10) + n = np.logspace(1,3,3,base=2,dtype=(np.int)) + C = helper.write_c_matrix(n) diff --git a/buch/papers/multiplikation/code/meas/MM.txt b/buch/papers/multiplikation/code/meas/MM.txt new file mode 100644 index 0000000..1a0cd5d --- /dev/null +++ b/buch/papers/multiplikation/code/meas/MM.txt @@ -0,0 +1,12 @@ +0.000000,2 +0.000000,4 +0.000002,8 +0.000011,16 +0.000080,32 +0.000653,64 +0.005397,128 +0.045147,256 +0.487710,512 +3.964180,1024 +128.863544,2048 +996.370209,4096 diff --git a/buch/papers/multiplikation/code/meas/MM_dc.txt b/buch/papers/multiplikation/code/meas/MM_dc.txt new file mode 100644 index 0000000..0d5580a --- /dev/null +++ b/buch/papers/multiplikation/code/meas/MM_dc.txt @@ -0,0 +1,12 @@ +0.000006,2 +0.000007,4 +0.000035,8 +0.000228,16 +0.001310,32 +0.007204,64 +0.034338,128 +0.267511,256 +2.131212,512 +17.177403,1024 +146.112874,2048 +1156.777565,4096 diff --git a/buch/papers/multiplikation/code/meas/blas.txt b/buch/papers/multiplikation/code/meas/blas.txt new file mode 100644 index 0000000..6b7cd0b --- /dev/null +++ b/buch/papers/multiplikation/code/meas/blas.txt @@ -0,0 +1,12 @@ +0.000001,2 +0.000000,4 +0.000001,8 +0.000003,16 +0.000021,32 +0.000164,64 +0.001240,128 +0.009657,256 +0.072523,512 +0.735149,1024 +6.895747,2048 +56.812183,4096 diff --git a/buch/papers/multiplikation/code/meas/strassen.txt b/buch/papers/multiplikation/code/meas/strassen.txt new file mode 100644 index 0000000..89cf41a --- /dev/null +++ b/buch/papers/multiplikation/code/meas/strassen.txt @@ -0,0 +1,12 @@ +0.000000,2 +0.000003,4 +0.000010,8 +0.000086,16 +0.000476,32 +0.003366,64 +0.025547,128 +0.184593,256 +1.248713,512 +9.007700,1024 +61.079879,2048 +424.493037,4096 diff --git a/buch/papers/multiplikation/code/meas/test/4096/MM.txt b/buch/papers/multiplikation/code/meas/test/4096/MM.txt new file mode 100644 index 0000000..25e40e1 --- /dev/null +++ b/buch/papers/multiplikation/code/meas/test/4096/MM.txt @@ -0,0 +1,12 @@ +0.000000,2 +0.000000,4 +0.000002,8 +0.000011,16 +0.000100,32 +0.000712,64 +0.005498,128 +0.046711,256 +0.489233,512 +4.006544,1024 +124.427496,2048 +993.405615,4096 diff --git a/buch/papers/multiplikation/code/meas/test/4096/strassen.txt b/buch/papers/multiplikation/code/meas/test/4096/strassen.txt new file mode 100644 index 0000000..eb2a496 --- /dev/null +++ b/buch/papers/multiplikation/code/meas/test/4096/strassen.txt @@ -0,0 +1,12 @@ +0.000007,2 +0.000007,4 +0.000029,8 +0.000199,16 +0.001414,32 +0.007583,64 +0.028096,128 +0.171662,256 +1.198323,512 +8.421896,1024 +58.803644,2048 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at={(0.03,0.97)}, + anchor=north west, + draw=white!80!black, + fill=white!89.8039215686275!black +}, +tick align=outside, +tick pos=left, +x grid style={white}, +xlabel={n}, +xmajorgrids, +xmin=-4.3, xmax=134.3, +xtick style={color=white!33.3333333333333!black}, +y grid style={white}, +ylabel={time (s)}, +ymajorgrids, +ymin=-0.0834965705871582, ymax=1.75356960296631, +ytick style={color=white!33.3333333333333!black} +] +\addplot [line width=2pt, color0] +table {% +2 1.57356262207031e-05 +4 5.96046447753906e-05 +8 0.000428915023803711 +16 0.00276041030883789 +32 0.0217020511627197 +64 0.160412073135376 +128 1.3419406414032 +}; +\addlegendentry{Standard MM} +\addplot [line width=2pt, color1] +table {% +2 6.43730163574219e-06 +4 6.69956207275391e-05 +8 0.00048065185546875 +16 0.00336766242980957 +32 0.0257236957550049 +64 0.231612205505371 +128 1.67006659507751 +}; +\addlegendentry{Divide and conquer MM} +\addplot [line width=2pt, color2] +table {% +2 2.90870666503906e-05 +4 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2020 Prof Dr Andreas Müller, Hochschule Rapperswil +% +\section{Einleitung \label{multiplikation:section:einleitung}} +\rhead{Einleitung} + +Die Multiplikation zweier Matrizen ist eine wichtige Operation die in verschiedensten Teilen der Mathematik Anwendung findet. +Die Beschreibung der Multiplikation aus der Definition 2.10 (\textcolor{blue} {Kein Hyperlink zu einer Definition?)}: + +Eine $m\times n$-Matrix $\mathbf{A}\in M_{m\times n}(\Bbbk)$ und eine +$n\times p$-Matrix $\mathbf{B}\in M_{n\times l}(\Bbbk)$ haben als Produkt +eine $n\times l$-Matrix $\mathbf{C}=\mathbf{AB}\in M_{n\times l}(\Bbbk)$ mit den +Koeffizienten +\begin{equation} +c_{ij} = \sum_{k=1}^n a_{ik} b_{kj}. +\label{multiplikation:eq:MM} +\end{equation} +Grafisch kann die Matrizenmultiplikation $AB=C$ wie in \ref{multiplikation:fig:mm_viz} visualisiert werden. +\begin{figure} + \center + \includegraphics[]{papers/multiplikation/images/mm_visualisation} + \caption{Matrizen Multiplikation} + \label{multiplikation:fig:mm_viz} +\end{figure} +Im Fall einer Matrizengr\"osse von $2\times 2$ +\begin{equation} + \begin{bmatrix} +A_{11} & A_{12}\\ +A_{21} & A_{22} +\end{bmatrix} +\begin{bmatrix} +B_{11} & B_{12}\\ +B_{21} & B_{22} +\end{bmatrix} += +\begin{bmatrix} +C_{11} & C_{12}\\ +C_{21} & C_{22} +\end{bmatrix} +\end{equation} +kann die Gleichung der einzelnen Terme +\begin{equation} \label{multiplikation:eq:MM_exp} +\begin{split} +C_{11} &= A_{11} \cdot B_{11} + A_{12} \cdot B_{21}\\ +C_{12} &= A_{11} \cdot B_{12} + A_{12} \cdot B_{22}\\ +C_{21} &= A_{21} \cdot B_{11} + A_{22} \cdot B_{21}\\ +C_{22} &= A_{21} \cdot B_{12} + A_{22} \cdot B_{22} +\end{split} +\end{equation} +explizit geschrieben werden. diff --git a/buch/papers/multiplikation/images/bigo.pdf b/buch/papers/multiplikation/images/bigo.pdf Binary files differnew file mode 100644 index 0000000..dfa2ba4 --- /dev/null +++ b/buch/papers/multiplikation/images/bigo.pdf diff --git a/buch/papers/multiplikation/images/bigo.tex b/buch/papers/multiplikation/images/bigo.tex new file mode 100644 index 0000000..e3293e4 --- /dev/null +++ b/buch/papers/multiplikation/images/bigo.tex @@ -0,0 +1,107 @@ +\documentclass[border=10pt,varwidth]{standalone} +\usepackage[left=25mm,right=25mm,top=25mm,bottom=25mm]{geometry} +\usepackage[utf8]{inputenc} +\usepackage[T1]{fontenc} +\usepackage{times} +\usepackage{geometry} +\usepackage{amsmath} +\usepackage{amssymb} +\usepackage{mathrsfs} +\usepackage{amsfonts} +\usepackage{amsthm} +\usepackage{lipsum} +\usepackage{amscd} +\usepackage{graphicx} +\usepackage{fancyhdr} +\usepackage{textcomp} +\usepackage{pgfplots} +\usepackage{txfonts} +\usepackage[all]{xy} +\usepackage{paralist} +\usepackage[colorlinks=true]{hyperref} +\usepackage{array} +\usepackage{tikz} +\usepackage{slashed} +\usepackage{pdfpages} +\usepackage{cite} +\usepackage{url} +\usepackage{amsmath,amsfonts,amssymb} +\usepackage{tikz} +\usetikzlibrary{arrows,matrix,positioning} +\usetikzlibrary{overlay-beamer-styles} +\usetikzlibrary{matrix.skeleton} +\usetikzlibrary{automata,positioning} +\usetikzlibrary{decorations.text} +\usepackage{listings} +\usepackage{multirow} +\usepackage{color} + +\begin{document} + +\begin{tikzpicture} +\begin{axis}[ + axis lines = left, + xlabel = $n$ (Data Input), + ylabel = {$t$ (time)}, + legend pos=north east, + very thick, + ymax = 500, + yticklabels=\empty, + xticklabels=\empty, + scale only axis=true, + width=12cm, height=6cm, + ] +\addplot [ + domain= 1:20, + samples=100, + color=red, +] +{1}; +\addlegendentry{$\mathcal{O}(1)$} +\addplot [ + domain= 1:20, + samples=100, + color=green, +] +{x}; +\addlegendentry{$\mathcal{O}(n)$} +\addplot [ + domain= 1:20, + samples=100, + color=blue, +] +{x^2}; +\addlegendentry{$\mathcal{O}(n^2)$} +\addplot [ + domain= 1:10, + samples=100, + color=purple, +] +{x^3}; +\addlegendentry{$\mathcal{O}(n^3)$} +\addplot [ + domain= 1:10, + samples=100, + color=black, +] +{exp(x)}; +\addlegendentry{$\mathcal{O}(e^n)$} +\addplot [ + domain= 1:20, + samples=100, + color=orange, +] +{log2(x)}; +\addlegendentry{$\mathcal{O}(\log n)$} + +\addplot [ + domain= 1:20, + samples=100, + color=gray, +] +{x*log2(x)}; +\addlegendentry{$\mathcal{O}(n \log n)$} +\end{axis} +\end{tikzpicture} + +\end{document} diff --git a/buch/papers/multiplikation/images/mm_visualisation.pdf b/buch/papers/multiplikation/images/mm_visualisation.pdf Binary files differnew file mode 100644 index 0000000..9309df1 --- /dev/null +++ b/buch/papers/multiplikation/images/mm_visualisation.pdf diff --git a/buch/papers/multiplikation/images/mm_visualisation.tex b/buch/papers/multiplikation/images/mm_visualisation.tex new file mode 100644 index 0000000..6e8f789 --- /dev/null +++ b/buch/papers/multiplikation/images/mm_visualisation.tex @@ -0,0 +1,45 @@ + + \begin{tikzpicture}[ampersand replacement=\&] + + \matrix (A)[matrix of math nodes, label skeleton, left delimiter=[,right delimiter={]}] at (0,0) + { + A_{1,1} \& \cdots \& A_{1,k} \& \cdots \& A_{1,n} \\ + \vdots \& \& \vdots \& \& \vdots \\ + A_{i,1} \& \cdots \& A_{i,k} \& \cdots \& A_{i,n} \\ + \vdots \& \& \vdots \& \& \vdots \\ + A_{m,1} \& \cdots \& A_{m,k} \& \cdots \& A_{m,n} \\ + }; + + \node [right=0.1 of A] (mul) {$\cdot$}; + + + \matrix (B)[right=0.1 of mul, matrix of math nodes, label skeleton, left delimiter=[,right delimiter={]}] + { + B_{1,1} \& \cdots \& B_{1,j} \& \cdots \& B_{1,p} \\ + \vdots \& \& \vdots \& \& \vdots \\ + B_{k,1} \& \cdots \& B_{k,j} \& \cdots \& B_{k,p} \\ + \vdots \& \& \vdots \& \& \vdots \\ + B_{n,1} \& \cdots \& B_{n,j} \& \cdots \& B_{n,p} \\ + }; + + \node [right=0.1 of B] (eq) {$=$}; + + \matrix (C)[right=0.1 of eq, matrix of math nodes, label skeleton, left delimiter=[,right delimiter={]}] + { + C_{1,1} \& \cdots \& C_{1,j} \& \cdots \& C_{1,p} \\ + \vdots \& \& \vdots \& \& \vdots \\ + C_{i,1} \& \cdots \& C_{i,j} \& \cdots \& C_{i,p} \\ + \vdots \& \& \vdots \& \& \vdots \\ + C_{m,1} \& \cdots \& C_{m,j} \& \cdots \& C_{m,p} \\ + }; + + + \node[opacity=0.5, rounded corners=2pt, inner sep=-1pt, fill=green, fit=(A-3-1)(A-3-5)] {}; + \node[opacity=0.5, rounded corners=2pt, inner sep=-1pt, fill=blue, fit=(B-1-3)(B-5-3)] {}; + \node[opacity=0.5, rounded corners=2pt, inner sep=-1pt, fill=red, fit=(C-3-3)] {}; + + + \end{tikzpicture} + +\end{document} + diff --git a/buch/papers/multiplikation/images/strassen.pdf b/buch/papers/multiplikation/images/strassen.pdf Binary files differnew file mode 100644 index 0000000..9899dcb --- /dev/null +++ b/buch/papers/multiplikation/images/strassen.pdf diff --git a/buch/papers/multiplikation/images/strassen.tex b/buch/papers/multiplikation/images/strassen.tex new file mode 100644 index 0000000..797772b --- /dev/null +++ b/buch/papers/multiplikation/images/strassen.tex @@ -0,0 +1,140 @@ +\documentclass[border=10pt]{standalone} +\usepackage[left=25mm,right=25mm,top=25mm,bottom=25mm]{geometry} +\usepackage[utf8]{inputenc} +\usepackage[T1]{fontenc} +\usepackage{times} +\usepackage{geometry} +\usepackage{amsmath} +\usepackage{amssymb} +\usepackage{mathrsfs} +\usepackage{amsfonts} +\usepackage{amsthm} +\usepackage{lipsum} +\usepackage{amscd} +\usepackage{graphicx} +\usepackage{fancyhdr} +\usepackage{textcomp} +\usepackage{pgfplots} +\usepackage{txfonts} +\usepackage[all]{xy} +\usepackage{paralist} +\usepackage[colorlinks=true]{hyperref} +\usepackage{array} +\usepackage{tikz} +\usepackage{slashed} +\usepackage{pdfpages} +\usepackage{cite} +\usepackage{url} +\usepackage{amsmath,amsfonts,amssymb} +\usepackage{tikz} +\usetikzlibrary{arrows,matrix,positioning} +\usetikzlibrary{overlay-beamer-styles} +\usetikzlibrary{matrix.skeleton} +\usetikzlibrary{automata,positioning} +\usetikzlibrary{decorations.text} +\usepackage{listings} +\usepackage{multirow} +\usepackage{color} + +\begin{document} + +\begin{tikzpicture}[ampersand replacement=\&] + +\foreach \i in {1,...,4} +{ + \small{ + \matrix (X\i)[matrix of math nodes,nodes in empty cells, + nodes = {draw, minimum size=10mm, + anchor=center, + inner sep=0pt, outer sep=0pt}, + column sep=-\pgflinewidth, + row sep=-\pgflinewidth, + ] at (0,-\i*5) + { + A_{11}B_{11} \& A_{12}B_{11} \& A_{21}B_{11} \& A_{22}B_{11} \\ + A_{11}B_{21} \& A_{12}B_{21} \& A_{21}B_{21} \& A_{22}B_{21} \\ + A_{11}B_{11} \& A_{12}B_{12} \& A_{21}B_{12} \& A_{22}B_{12} \\ + A_{11}B_{22} \& A_{12}B_{22} \& A_{21}B_{22} \& A_{22}B_{22} \\ + };} + + \foreach \j in {1,...,7} + { + \matrix(M\i\j)[matrix of math nodes,nodes in empty cells, + nodes = {draw, minimum size=10mm, + anchor=center, + inner sep=0pt, outer sep=0pt}, + column sep=-\pgflinewidth, + row sep=-\pgflinewidth, + ] at (\j*5,-\i*5) + { + \& \& \& \\ + \& \& \& \\ + \& \& \& \\ + \& \& \& \\ + }; + } +} + +\huge{ + \node at (-3,-20) {$C_{22}=$}; + \node at (-3,-15) {$C_{21}=$} ; + \node at (-3,-10) {$C_{12}=$} ; + \node at (-3,-5) {$C_{11}=$} ; + + \node at (5,-2) {I}; + \node at 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2020 Prof Dr Andreas Müller, Hochschule Rapperswil +% + +\section{L\"osungsmethoden} +\rhead{L\"osungsmethoden} + +In diesem Abschnitt werden mehrere Algorithmen zur Berechnung der Matrizenmultiplikation vorgestellt, auch werden Libraries zur automatisierten Verwendung von vordefinierten Algorithmen gezeigt. + +\subsection{Standard Algorithmus} + +Der Standard Methode kann im Algorithmus \ref{multiplikation:alg:smm} entnommen werden. +Hierf\"ur wurde die Gleichung \eqref{multiplikation:eq:MM} direkt implementiert. +Die \texttt{For i} Schleife iteriert \"uber alle Zeilen der $\mathbf{A}$ Matrix, die \texttt{For j} Schleife iteriert \"uber alle Spalten der $\mathbf{B}$ Matrix und die \texttt{For k} Schleife iteriert \"uber alle Eintr\"age dieser Zeilen bzw. Spalten. + +\begin{algorithm}\caption{Matrix Multiplication} + \label{multiplikation:alg:smm} + \setlength{\lineskip}{7pt} + \begin{algorithmic}[1] + \Function{MM}{$\textbf{A}, \textbf{B}$} + \State $sum \gets 0$ + \State $n \gets columns(\textbf{A}) == rows(\textbf{B})$ + \State $m \gets rows(\textbf{A})$ + \State $p \gets columns(\textbf{B})$ + \State $\textbf{C} \gets zeros(m,p)$ + \For{$i = 0,1,2 \dots,m-1$} + \For{$j = 0,1,2 \dots,p-1$} + \State $sum \gets 0$ + \For{$k = 0,1,2 \dots,n-1$} + \State $sum \gets sum + \textbf{A}[i][k] \cdot \textbf{B}[k][j]$ + \EndFor + \State $\textbf{C}[i][j] \gets sum $ + \EndFor + \EndFor + \State \textbf{return} $\textbf{C}$ + \EndFunction + \end{algorithmic} +\end{algorithm} + +Die Laufzeit dieser Struktur mit drei \texttt{For} Schleifen ist $\mathcal{O}(n^3)$ + +\subsubsection{Divide and Conquer Methode} + +F\"ur gewisse Algorithmen f\"uhren \textit{Divide and Conquer} Ans\"atze zu markant besseren Laufzeiten. +Das bekannteste Beispiel ist wohl die \textit{Fast Fourier Transform} wobei die Laufzeit von $\mathcal{O}(n^2)$ zu $\mathcal{O}(n \log n)$ verbessert werden kann. + +Die Matrizenmultiplikation kann ebenfalls mit solch einem Ansatz berechnet werden. +Zur vereinfachten Veranschaulichung kann die Situation, mit $\mathbf{A}$ und $\mathbf{B}$ der gr\"osse $2^n \times 2^n$ verwendet werden. +Die Matrizen $\mathbf{A}$ und $\mathbf{B}$ werden in jeweils vier Blockmatrizen der gr\"osse $2^{n-1} \times 2^{n-1}$ +\begin{equation} +\mathbf{A}\mathbf{B}= +\begin{bmatrix} +\mathbf{A}_{11} & \mathbf{A}_{12}\\ +\mathbf{A}_{21} & \mathbf{A}_{22} +\end{bmatrix} +\begin{bmatrix} +\mathbf{B}_{11} & \mathbf{B}_{12}\\ +\mathbf{B}_{21} & \mathbf{B}_{22} +\end{bmatrix} += +\begin{bmatrix} +\mathbf{C}_{11} & \mathbf{C}_{12}\\ +\mathbf{C}_{21} & \mathbf{C}_{22} +\end{bmatrix} +\end{equation} +aufgeteilt. +Die Berechnung +\begin{equation} +\mathbf{C}_{ij} = \sum_{k=1}^n \mathbf{A}_{ik} \mathbf{B}_{kj} +\label{multiplikation:eq:MM_block} +\end{equation} +ist identisch zu der Gleichung \eqref{multiplikation:eq:MM}, wobei hier f\"ur die Multiplikation die Matrizenmultiplikation verwendet wird. + +Der Algorithmus \ref{multiplikation:alg:devide_mm} zeigt den \textit{Divide and Conquer} Ansatz, +Der Grundstruktur dieser Methode besteht aus dem rekursiven Aufruf der Funktion mit den erzeugten Blockmatrizen. +Der rekursive Aufruf wird bis zu der Gr\"osse der Matrizen von $N = 2 \times 2$ durchgef\"uhrt. +\begin{algorithm}\caption{Divide and Conquer Matrix Multiplication} + \setlength{\lineskip}{7pt} + \label{multiplikation:alg:devide_mm} + \begin{algorithmic} + \Function{MM}{$\textbf{A}, \textbf{B}, n$} + \If{$n = 2$} + \State $ \mathbf{C} \gets zeros(n, n)$ + \State $C[0, 0] \gets A[0][0]\cdot B[0][0]+A[0][1]\cdot B[1][0]$ + \State $C[0, 1] \gets A[0][0]\cdot B[0][1]+A[0][1]\cdot B[1][1]$ + \State $C[1, 0] \gets A[1][0]\cdot B[0][0]+A[1][1]\cdot B[1][0]$ + \State $C[1, 1] \gets A[1][0]\cdot B[0][1]+A[1][1]\cdot B[1][1]$ + \Else + \State $ m \gets n/2$ + \State $\mathbf{A11}, \mathbf{A12}, \mathbf{A21}, \mathbf{A22} \gets \mathbf{A}[:m][:m], \mathbf{A}[:m][m:], \mathbf{A}[m:][:m], \mathbf{A}[m:][m:]$ + \State $\mathbf{B11}, \mathbf{B12}, \mathbf{B21}, \mathbf{B22} \gets \mathbf{B}[:m][:m], \mathbf{B}[:m][m:], \mathbf{B}[m:][:m], \mathbf{B}[m:][m:]$ + + \State $\mathbf{C11} \gets \text{MM}(\mathbf{A11}, \mathbf{B11},n) + \text{MM}(\mathbf{A12}, \mathbf{B21},n)$ + \State $\mathbf{C12} \gets \text{MM}(\mathbf{A11},\mathbf{B12},n) + \text{MM}(\mathbf{A12}, \mathbf{B22},n)$ + \State $\mathbf{C21} \gets \text{MM}(\mathbf{A21}, \mathbf{B11},n) + \text{MM}(\mathbf{A22}, \mathbf{B21},n)$ + \State $\mathbf{C22} \gets \text{MM}(\mathbf{A21}, \mathbf{B12},n) + \text{MM}(\mathbf{A22}, \mathbf{B22},n)$ + \State $ C \gets vstack(hstack(C11, C12), hstack(C21, C22))$ + + \EndIf + \State \textbf{return} $\textbf{C}$ + + \EndFunction + \end{algorithmic} +\end{algorithm} + +Die Laufzeit dieser rekursiven Funktion kann mit dem \textit{Master Theorem} berechnet werden. +Ohne auf diesen vertieft einzugehen, bestimmt die Anzahl rekursiver Aufrufe der Funktion die Laufzeit. +In diesem Fall wird die Funktion pro Durchlauf acht mal rekursiv aufgerufen, dies f\"uhrt +\begin{equation} \label{multiplikation:eq:laufzeitdac} + \mathcal{T}(n) = + \begin{cases} + 1 & \text{if } n \leq 2\\ + 8 \cdot \mathcal{T}(\frac{n}{2}) + n^2 & \text{if } n > 2 + \end{cases} = \mathcal{O}(n^{\log_2 8}) = \mathcal{O}(n^{3}) +\end{equation} +zu einer kubischen Laufzeit. +Die Addition zweier Matrizen $\mathbf{A} + \mathbf{B} = \mathbf{C}$ hat eine Laufzeit von $\mathcal{O}(n^{2})$ und kann neben dem dominierendem Anteil von $\mathcal{O}(n^{3})$ ignoriert werden. +In diesem Fall hat der \textit{Divide and Conquer} Ansatz zu keiner Verbesserung gef\"uhrt. + + +\subsection{Strassen's Algorithmus} + +Strassen's Algorithmus \cite{multiplikation:strassen_1969} beschreibt die Matrizenmultiplikation mit einer Vielzahl von Additionen, Subtraktionen und Multiplikationen. +Die Grundlegenden Terme +\begin{equation} \label{multiplikation:eq:strassen} +\begin{split} +\text{\textbf{P}} &= (\mathbf{A}_{11} + \mathbf{A}_{22}) \cdot (\mathbf{B}_{11} + \mathbf{B}_{22}) \\ +\text{\textbf{Q}} &= (\mathbf{A}_{21} + \mathbf{A}_{22}) \cdot \mathbf{B}_{11} \\ +\text{\textbf{R}} &= \mathbf{A}_{11} \cdot (\mathbf{B}_{12}-\mathbf{B}_{22}) \\ +\text{\textbf{S}} &= \mathbf{A}_{22} \cdot (-\mathbf{B}_{11}+\mathbf{B}_{21}) \\ +\text{\textbf{T}} &= (\mathbf{A}_{11} + \mathbf{A}_{12}) \cdot \mathbf{B}_{22} \\ +\text{\textbf{U}} &= (-\mathbf{A}_{11} + \mathbf{A}_{21}) \cdot (\mathbf{B}_{11} + \mathbf{B}_{12}) \\ +\text{\textbf{V}} &= (\mathbf{A}_{12} - \mathbf{A}_{22}) \cdot (\mathbf{B}_{21} + \mathbf{B}_{22}) +\end{split} +\end{equation} +aus $\mathbf{A}$ und $\mathbf{B}$, werden f\"ur die Berechnung der Matrix $\mathbf{C}$ +\begin{equation} \label{multiplikation:eq:strassen2} +\begin{split} +\mathbf{C}_{11} &= \text{\textbf{P}} + \text{\textbf{S}} - \text{\textbf{T}} + \text{\textbf{V}} \\ +\mathbf{C}_{21} &= \text{\textbf{R}} + \text{\textbf{T}} \\ +\mathbf{C}_{12} &= \text{\textbf{Q}} + \text{\textbf{S}}\\ +\mathbf{C}_{22} &= \text{\textbf{P}} + \text{\textbf{R}} - \text{\textbf{Q}} + \text{\textbf{U}} +\end{split} +\end{equation} +gebraucht. +\begin{algorithm}\caption{Strassen Matrix Multiplication} + \label{multiplikation:alg:strassen} + \setlength{\lineskip}{7pt} + \begin{algorithmic} + \Function{strassen}{$\textbf{A}, \textbf{B}, n$} + \If{$n = 2$} + \State $ \mathbf{C} \gets zeros((n, n))$ + \State $P \gets (A[0][0]+A[1][1])\cdot( B[0][0]+B[1][1])$ + \State $Q \gets (A[1][0]+A[1][1])\cdot B[0][0]$ + \State $R \gets A[0][0]\cdot (B[0][1]-B[1][1])$ + \State $S \gets A[1][1]\cdot (B[1][0]-B[0][0])$ + \State $T \gets (A[0][0]+A[0][1])\cdot B[1][1]$ + \State $U \gets (A[1][0]-A[0][0])\cdot (B[0][0]+B[0][1])$ + \State $V \gets (A[0][1]-A[1][1])\cdot (B[1][0]+B[1][1])$ + \State $C[0][0] \gets P+S-T+V$ + \State $C[0][1] \gets R+T$ + \State $C[1][0] \gets Q+S$ + \State $C[1][1] \gets P+R-Q+U$ + \Else + \State $ m \gets n/2$ + \State $\mathbf{A11}, \mathbf{A12}, \mathbf{A21}, \mathbf{A22} \gets \mathbf{A}[:m][:m], \mathbf{A}[:m][m:], \mathbf{A}[m:][:m], \mathbf{A}[m:][m:]$ + \State $\mathbf{B11}, \mathbf{B12}, \mathbf{B21}, \mathbf{B22} \gets \mathbf{B}[:m][:m], \mathbf{B}[:m][m:], \mathbf{B}[m:][:m], \mathbf{B}[m:][m:]$ + + \State $ \mathbf{P} \gets \text{strassen}((\mathbf{A11}+ \mathbf{A22}),(\mathbf{B11}+\mathbf{B22}), m)$ + \State $ \mathbf{Q} \gets \text{strassen}((\mathbf{A21}+ \mathbf{A22}), \mathbf{B11},m)$ + \State $ \mathbf{R} \gets \text{strassen}( \mathbf{A11},(\mathbf{B12}- \mathbf{B22}),m)$ + \State $ \mathbf{S} \gets \text{strassen}( \mathbf{A22},(\mathbf{B21}- \mathbf{B11}),m)$ + \State $ \mathbf{T} \gets \text{strassen}((\mathbf{A11}+ \mathbf{A12}), \mathbf{B22},m)$ + \State $ \mathbf{U} \gets \text{strassen}((\mathbf{A21}- \mathbf{A11}),(\mathbf{B11}+\mathbf{B12}),m)$ + \State $ \mathbf{V} \gets \text{strassen}((\mathbf{A12}- \mathbf{A22}),(\mathbf{B21}+\mathbf{B22}),m)$ + + + + \State $\mathbf{C11} \gets \mathbf{P+S-T+V}$ + \State $\mathbf{C12} \gets \mathbf{R+T}$ + \State $\mathbf{C21} \gets \mathbf{Q+S}$ + \State $\mathbf{C22} \gets \mathbf{P+R-Q+U}$ + \State $ C \gets vstack(hstack(C11, C12), hstack(C21, C22))$ + + \EndIf + \State \textbf{return} $\textbf{C}$ + + \EndFunction + \end{algorithmic} +\end{algorithm} +Strassens's Methode wird in der Abbildung \ref{multiplikation:fig:strassen} grafisch dargestellt. +\begin{figure} + \center + \includegraphics[width=\linewidth]{papers/multiplikation/images/strassen.pdf} + \caption{Strassen's Algorithmus} + \label{multiplikation:fig:strassen} +\end{figure} + +Die Funktion wird sieben mal rekursiv aufgerufen. +Dies f\"uhrt zu einer Laufzeit von +\begin{equation} \label{multiplikation:eq:laufzeitstrassen} +\mathcal{T}(n) = +\begin{cases} +1 & \text{if } n \leq 2\\ +7 \cdot \mathcal{T}(\frac{n}{2}) + n^2 & \text{if } n > 2 +\end{cases} = \mathcal{O}(n^{\log_2 7}) = \mathcal{O}(n^{2.8074}) +\end{equation} +und ist somit schneller als die Standard Methode. + +\subsection{Winograd's Algorithmus} + +Ein weiterer Ansatz lieferte Shmuel Winograd im Jahre 1968 \cite{multiplikation:winograd_1968}. +Er zeigte einen neuen Algorithmus f\"ur das +\begin{equation} + \langle x,y \rangle = \sum_{i=1}^{n}x_i y_i +\end{equation} +Skalarprodukt. +F\"ur jeden Vektor berechne +\begin{equation} + \xi = \sum_{j=1}^{ \lfloor n/2 \rfloor} x_{2j-1} \cdot x_{2j} +\end{equation} +und +\begin{equation} + \eta = \sum_{j=1}^{ \lfloor n/2 \rfloor} y_{2j-1} \cdot y_{2j}. +\end{equation} +Das Skalarprodukt ist nun geben mit +\begin{equation} + \langle x,y \rangle = + \begin{cases} + \displaystyle \quad \sum_{j=1}^{ \lfloor n/2 \rfloor} (x_{2j-1} + y_{2j})(x_{2j}+y_{2j-1})-\xi - \eta & \text{if $n$ is even}\\ + \displaystyle \quad \sum_{j=1}^{ \lfloor n/2 \rfloor} (x_{2j-1} + y_{2j})(x_{2j}+y_{2j-1})-\xi - \eta + x_n y_n & \text{if $n$ is odd}. + \end{cases} +\end{equation} + +Angenommen man hat $N$ Vektoren mit welchen man $T$ Skalarprodukte berechnen m\"ochte. +Daf\"ur werden $N\lfloor n/2 \rfloor + T\lfloor (n+1)/2 \rfloor $ Multiplikationen ben\"otigt. +Eine Matrizenmultiplikation mit $\mathbf{A}$ einer $m \times n$ und $\mathbf{B}$ einer $n \times p$ Matrix, entspricht $N=m+p$ Vektoren mit welchen man $T=mp$ Skalarprodukte berechnet. +Dies f\"uhrt zu +\begin{equation} + (m+p) \left \lfloor \frac{n}{2} \right \rfloor + mp \left \lfloor \frac{n+1}{2} \right \rfloor = \frac{mn}{2} + \frac{pn}{2} + \frac{mpn}{2} + \frac{mp}{2} +\end{equation} +Multiplikationen. +Wenn $m,p,n$ gross werden, dominiert der Term $\frac{mpn}{2}$ und es werden $\frac{mpn}{2}$ Multiplikationen ben\"otigt. +Was im Vergleich zu den $mpn$ Multiplikation der Standard Methode nur die H\"alfte ist. +Die Implementation kann im Algorithmus \ref{multiplikation:alg:winograd} entnommen werden. + +\begin{algorithm}\caption{Winograd Matrix Multiplication} + \setlength{\lineskip}{7pt} + \label{multiplikation:alg:winograd} + \begin{algorithmic} + \Function{Winograd}{$\textbf{A}, \textbf{B}, n$} + \State $ m \gets rows(\mathbf{A})$ + \State $ n \gets columns(\mathbf{A}) == rows(\mathbf{B})$ + \State $ p \gets columns(\mathbf{B})$ + \State $ \mathbf{\xi} \gets zeros(m)$ + \State $ \mathbf{\eta} \gets zeros(p)$ + + + \For{$i = 0,1,2 \dots,m-1$} + \For{$j = 0,1,2 \dots,\lfloor n/2 \rfloor-1$} + \State $\xi[i] \gets \xi[i]+A[i,2 j]A[i,2 j+1]$ + \EndFor + \EndFor + + \For{$i = 0,1,2 \dots,p-1$} + \For{$j = 0,1,2 \dots,\lfloor n/2 \rfloor-1$} + \State $\eta[i] \gets \eta[i]+B[2 j,i]B[2 j+1,i]$ + \EndFor + \EndFor + + \If{$n \% 2 == 0$} + \For{$i = 0,1,2 \dots,m-1$} + \For{$j = 0,1,2 \dots,p-1$} + \State $ab \gets 0$ + \For{$k = 0,1,2 \dots,\lfloor n/2 \rfloor-1$} + \State $ab \gets ab + (A[i,2k]+B[2k+1,j])(A[i,2k+1]+B[2k,j])$ + \EndFor + \State $C[i,j] \gets ab-\eta[j]-\xi[i]$ + \EndFor + \EndFor + \Else + \For{$i = 0,1,2 \dots,n-1$} + \For{$j = 0,1,2 \dots,n-1$} + \State $ab \gets 0$ + \For{$k = 0,1,2 \dots,\lfloor n/2 \rfloor-1$} + \State $ab \gets ab + (A[i,2k]+B[2k+1,j])(A[i,2k+1]+B[2k,j])$ + \EndFor + \State $C[i,j] \gets ab-\eta[j]-\xi[i]+A[i,-1]B[-1,j]$ + \EndFor + \EndFor + \EndIf + \State \textbf{return} $\textbf{C}$ + + \EndFunction + \end{algorithmic} +\end{algorithm} + +\subsection{Weitere Algorithmen} + +\textcolor{red}{TODO: BLAS} + +\section{Implementation} +\rhead{Implementation} +\textcolor{red}{TODO: messresultate} + +\section{Fazit} +\rhead{Fazit} diff --git a/buch/papers/multiplikation/main.tex b/buch/papers/multiplikation/main.tex index 42f2768..8d0a8df 100644..100755 --- a/buch/papers/multiplikation/main.tex +++ b/buch/papers/multiplikation/main.tex @@ -1,36 +1,18 @@ +% !TEX root = ../../buch.tex % % main.tex -- Paper zum Thema <multiplikation> % -% (c) 2020 Hochschule Rapperswil +% (c) 2021 Hochschule Rapperswil % -\chapter{Thema\label{chapter:multiplikation}} -\lhead{Thema} +\chapter{Schnelle Matrizen Multiplikation\label{chapter:multiplikation}} +\lhead{FMM} \begin{refsection} -\chapterauthor{Hans Muster} +\chapterauthor{Michael Schmid} -Ein paar Hinweise für die korrekte Formatierung des Textes -\begin{itemize} -\item -Absätze werden gebildet, indem man eine Leerzeile einfügt. -Die Verwendung von \verb+\\+ ist nur in Tabellen und Arrays gestattet. -\item -Die explizite Platzierung von Bildern ist nicht erlaubt, entsprechende -Optionen werden gelöscht. -Verwenden Sie Labels und Verweise, um auf Bilder hinzuweisen. -\item -Beginnen Sie jeden Satz auf einer neuen Zeile. -Damit ermöglichen Sie dem Versionsverwaltungssysteme, Änderungen -in verschiedenen Sätzen von verschiedenen Autoren ohne Konflikt -anzuwenden. -\item -Bilden Sie auch für Formeln kurze Zeilen, einerseits der besseren -Übersicht wegen, aber auch um GIT die Arbeit zu erleichtern. -\end{itemize} -\input{papers/multiplikation/teil0.tex} -\input{papers/multiplikation/teil1.tex} -\input{papers/multiplikation/teil2.tex} -\input{papers/multiplikation/teil3.tex} +\input{papers/multiplikation/einlteung.tex} +\input{papers/multiplikation/problemstellung.tex} +\input{papers/multiplikation/loesungsmethoden.tex} \printbibliography[heading=subbibliography] \end{refsection} diff --git a/buch/papers/multiplikation/packages.tex b/buch/papers/multiplikation/packages.tex index e4173c0..e4173c0 100644..100755 --- a/buch/papers/multiplikation/packages.tex +++ b/buch/papers/multiplikation/packages.tex diff --git a/buch/papers/multiplikation/papers/Strassen_GPU.pdf b/buch/papers/multiplikation/papers/Strassen_GPU.pdf Binary files differnew file mode 100755 index 0000000..4ce7625 --- /dev/null +++ b/buch/papers/multiplikation/papers/Strassen_GPU.pdf diff --git a/buch/papers/multiplikation/papers/Strassen_original_1969.pdf b/buch/papers/multiplikation/papers/Strassen_original_1969.pdf Binary files differnew file mode 100755 index 0000000..b647fc0 --- /dev/null +++ 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file mode 100644 index 0000000..200d244 --- /dev/null +++ b/buch/papers/multiplikation/presentation/common.tex @@ -0,0 +1,79 @@ +% +% common.tex -- gemeinsame Definitionen +% +% (c) 2021 Michael Schmid, OST Campus Rapperswil +% +\usepackage[utf8]{inputenc} +\usepackage[T1]{fontenc} +\usepackage{epic} +\usepackage{color} +\usepackage{array} +\usepackage{algorithm} +\usepackage{ifthen} +\usepackage{adjustbox} +\usepackage[noend]{algpseudocode} +\usepackage{neuralnetwork} +\usepackage{amsmath} +\usepackage{lmodern} +\usepackage{tikz} +\usetikzlibrary{decorations.text} +\usetikzlibrary{arrows,matrix,positioning} +\usetikzlibrary{overlay-beamer-styles} +\usetikzlibrary{matrix.skeleton} +\usepackage{pgfplots} +\usepackage{listings} +\usepackage{svg} + +\definecolor{codegreen}{rgb}{0,0.6,0} +\definecolor{codegray}{rgb}{0.5,0.5,0.5} +\definecolor{codepurple}{rgb}{0.58,0,0.82} +\definecolor{backcolour}{rgb}{0.95,0.95,0.92} +\definecolor{ost}{rgb}{164,0,136} + +\lstdefinestyle{mystyle}{ + backgroundcolor=\color{backcolour}, + commentstyle=\color{codegreen}, + keywordstyle=\color{magenta}, + numberstyle=\tiny\color{codegray}, + stringstyle=\color{codepurple}, + basicstyle=\footnotesize, + breakatwhitespace=false, + breaklines=true, + captionpos=b, + keepspaces=true, + numbers=left, + numbersep=2pt, + showspaces=false, + showstringspaces=false, + showtabs=false, + tabsize=2 +} + +\usetikzlibrary{fit} +\tikzset{% + highlight/.style={rectangle,rounded corners,fill=red!15,draw,fill opacity=0.5,inner sep=0pt} +} +\newcommand{\tikzmark}[2]{\tikz[overlay,remember picture,baseline=(#1.base)] \node (#1) {#2};} +% +\newcommand{\Highlight}[1][submatrix]{% + \tikz[overlay,remember picture]{ + \node[highlight,fit=(left.north west) (right.south east)] (#1) {};} +} + + +\lstset{style=mystyle} +\lstdefinestyle{mystyle}{ + morekeywords={cwt,contourf,datetick} +} + + +\usetikzlibrary{shapes.geometric} +\mode<beamer>{% +\usetheme[]{Frankfurt}} +\beamertemplatenavigationsymbolsempty +\title[]{Fast Matrix Multiplication} +\author[]{Michael Schmid} +\usecolortheme[named=ost]{structure} + +\date[]{31.05.2021} +\newboolean{presentation} diff --git a/buch/papers/multiplikation/presentation/presentation.nav b/buch/papers/multiplikation/presentation/presentation.nav new file mode 100644 index 0000000..2a01568 --- /dev/null +++ b/buch/papers/multiplikation/presentation/presentation.nav @@ -0,0 +1,59 @@ +\headcommand {\slideentry {0}{0}{1}{1/1}{}{0}} +\headcommand {\beamer@framepages {1}{1}} +\headcommand {\beamer@sectionpages {1}{1}} +\headcommand {\beamer@subsectionpages {1}{1}} +\headcommand {\sectionentry {1}{Big $\mathcal {O}$}{2}{Big $\mathcal {O}$}{0}} +\headcommand {\slideentry {1}{0}{1}{2/4}{}{0}} +\headcommand {\beamer@framepages {2}{4}} +\headcommand {\slideentry {1}{0}{2}{5/6}{}{0}} +\headcommand {\beamer@framepages {5}{6}} +\headcommand {\slideentry {1}{0}{3}{7/8}{}{0}} +\headcommand {\beamer@framepages {7}{8}} +\headcommand {\slideentry {1}{0}{4}{9/10}{}{0}} 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0000000..2a4af45 --- /dev/null +++ b/buch/papers/multiplikation/presentation/presentation.tex @@ -0,0 +1,12 @@ +% +% MathSem-yyy-xxx.tex -- Präsentation +% +% (c) 2021 Michael Schmid, OST campus Rapperswil +% + +\documentclass[aspectratio=169]{beamer} +\input{common.tex} +%\setboolean{presentation}{true} +\begin{document} +\input{slides/slides.tex} +\end{document} diff --git a/buch/papers/multiplikation/presentation/slides/algo.tex b/buch/papers/multiplikation/presentation/slides/algo.tex new file mode 100644 index 0000000..0c3d130 --- /dev/null +++ b/buch/papers/multiplikation/presentation/slides/algo.tex @@ -0,0 +1,111 @@ +\begin{frame} + \frametitle{Algorithm} + \begin{columns} + \begin{column}{0.6\textwidth} + \begin{algorithm}[H]\caption{Square Matrix Multiplication} + \setlength{\lineskip}{7pt} + \begin{algorithmic}[1] + \Function{MM}{$\textbf{A}, \textbf{B}, \textbf{C}$} + \State $sum \gets 0$ + \State $n \gets columns(\textbf{A}) == rows(\textbf{B})$ + \State $m \gets rows(\textbf{A})$ + \State $p \gets columns(\textbf{B})$ + + \For{$i = 0,1,2 \dots,m-1$} + \For{$j = 0,1,2 \dots,p-1$} + \State $sum \gets 0$ + \For{$k = 0,1,2 \dots,n-1$} + \State $sum \gets sum + \textbf{A}[i][k] \cdot \textbf{B}[k][j]$ + \EndFor + \State $\textbf{C}[i][j] \gets sum $ + \EndFor + \EndFor + \State \textbf{return} $\textbf{C}$ + \EndFunction + \end{algorithmic} + \end{algorithm} +\end{column} +\begin{column}{0.4\textwidth} + \scalebox{0.6}{\parbox{\linewidth}{ + + \begin{tikzpicture}[ampersand replacement=\&,remember picture,overlay] + + \matrix (A)[matrix of math nodes, label skeleton, left delimiter=[,right delimiter={]}] at (2,-2.8) + { + A_{1,1} \& \cdots \& A_{1,k} \& \cdots \& A_{1,n} \\ + \vdots \& \& \vdots \& \& \vdots \\ + A_{i,1} \& \cdots \& A_{i,k} \& \cdots \& A_{i,n} \\ + \vdots \& \& \vdots \& \& \vdots \\ + A_{m,1} \& \cdots \& A_{m,k} \& \cdots \& A_{m,n} \\ + }; + + \matrix (B)[matrix of math nodes, label skeleton, left delimiter=[,right delimiter={]}] at (7.5,1.2) + { + B_{1,1} \& \cdots \& B_{1,j} \& \cdots \& B_{1,p} \\ + \vdots \& \& \vdots \& \& \vdots \\ + B_{k,1} \& \cdots \& B_{k,j} \& \cdots \& B_{k,p} \\ + \vdots \& \& \vdots \& \& \vdots \\ + B_{n,1} \& \cdots \& B_{n,j} \& \cdots \& B_{n,p} \\ + }; + + \matrix (C)[matrix of math nodes, label skeleton, left delimiter=[,right delimiter={]}] at (7.5,-2.8) + { + C_{1,1} \& \cdots \& C_{1,j} \& \cdots \& C_{1,p} \\ + \vdots \& \& \vdots \& \& \vdots \\ + C_{i,1} \& \cdots \& C_{i,j} \& \cdots \& C_{i,p} \\ + \vdots \& \& \vdots \& \& \vdots \\ + C_{m,1} \& \cdots \& C_{m,j} \& \cdots \& C_{m,p} \\ + }; + + + \begin{scope}[on background layer] + \node[opacity=0.5, rounded corners=2pt, inner sep=-1pt, fill=green, fit=(A-3-1)(A-3-5)] {}; + \node[opacity=0.5, rounded corners=2pt, inner sep=-1pt, fill=blue, fit=(B-1-3)(B-5-3)] {}; + \node[opacity=0.5, rounded corners=2pt, inner sep=-1pt, fill=red, fit=(C-3-3)] {}; + + \end{scope} + + + + + \end{tikzpicture} + }} + \end{column} +\end{columns} +\end{frame} + + +\begin{frame} + \frametitle{Algorithm} + +\begin{columns} + \begin{column}{0.6\textwidth} +\begin{algorithm}[H]\caption{Square Matrix Multiplication} + \setlength{\lineskip}{7pt} + \begin{algorithmic}[1] + \Function{MM}{$\textbf{A}, \textbf{B}, \textbf{C}$} + \State $sum \gets 0$ + \State $n \gets columns(\textbf{A}) == rows(\textbf{B})$ + \State $m \gets rows(\textbf{A})$ + \State $p \gets columns(\textbf{B})$ + + \For{$i = 0,1,2 \dots,m-1$} + \For{$j = 0,1,2 \dots,p-1$} + \State $sum \gets 0$ + \For{$k = 0,1,2 \dots,n-1$} + \State $sum \gets sum + \textbf{A}[i][k] \cdot \textbf{B}[k][j]$ + \EndFor + \State $\textbf{C}[i][j] \gets sum $ + \EndFor + \EndFor + \State \textbf{return} $\textbf{C}$ + \EndFunction + \end{algorithmic} +\end{algorithm} +\end{column} +\begin{column}{0.4\textwidth} +\Huge$\mathcal{O}(n^3)$ +\end{column} +\end{columns} + +\end{frame} diff --git a/buch/papers/multiplikation/presentation/slides/bigO.tex b/buch/papers/multiplikation/presentation/slides/bigO.tex new file mode 100644 index 0000000..d425da8 --- /dev/null +++ b/buch/papers/multiplikation/presentation/slides/bigO.tex @@ -0,0 +1,251 @@ + +\begin{frame} + \frametitle{Big $\mathcal{O}$ notation} +\begin{itemize} + \item <1-> Time complexity of an algorithm + \item <2-> How many multiplications in a function + \item <3-> Drop Constants +\end{itemize} +\end{frame} + + +\begin{frame} + \frametitle{Big $\mathcal{O}$ notation} + \onslide<1->{ + + \begin{algorithm}[H]\caption{Foo 1} + \setlength{\lineskip}{7pt} + \begin{algorithmic}[1] + \Function{foo}{$a, b$} + \State \textbf{return} $a+b$ + \EndFunction + \end{algorithmic} + \end{algorithm} +} +\onslide<2->{ +$\mathcal{O}(1)$ + } +\end{frame} + +\begin{frame} + \frametitle{Big $\mathcal{O}$ notation} + \onslide<1->{ + + \begin{algorithm}[H]\caption{Foo 2} + \setlength{\lineskip}{7pt} + \begin{algorithmic}[1] + \Function{foo}{$a, b$} + \State $ x \gets a+b $ + \State $ y \gets a \cdot b $ + \State \textbf{return} $x+y$ + \EndFunction + \end{algorithmic} + \end{algorithm} +} +\onslide<2->{ +$\mathcal{O}(1) + \mathcal{O}(1) = 2\mathcal{O}(1) = \mathcal{O}(1) $ + } +\end{frame} + +\begin{frame} + \frametitle{Big $\mathcal{O}$ notation} + \onslide<1->{ + + \begin{algorithm}[H]\caption{Foo 3} + \setlength{\lineskip}{7pt} + \begin{algorithmic}[1] + \Function{foo}{$\mathbf{A}, \mathbf{B}$,n} + \State $ sum \gets 0$ + \For{$i = 0,1,2 \dots,n$} + \State $ sum \gets sum + A[i] \cdot B[i] $ + \EndFor + + \State \textbf{return} $sum$ + + \EndFunction + \end{algorithmic} + \end{algorithm} +} +\onslide<2->{ +$\mathcal{O}(n)$ + } +\end{frame} + +\begin{frame} + \frametitle{Big $\mathcal{O}$ notation} + \onslide<1->{ + + \begin{algorithm}[H]\caption{Foo 4} + \setlength{\lineskip}{7pt} + \begin{algorithmic}[1] + \Function{foo}{$\mathbf{A}, \mathbf{B}$,n} + \State $ sum \gets 0$ + \For{$i = 0,1,2 \dots,n$} + \For{$j = 0,1,2 \dots,n$} + \State $ sum \gets sum + A[i] \cdot B[j] $ + \EndFor + \EndFor + \State \textbf{return} $sum$ + \EndFunction + \end{algorithmic} + \end{algorithm} +} +\onslide<2->{ +$\mathcal{O}(n^2)$ + } +\end{frame} + +% \begin{frame} +% \frametitle{Big $\mathcal{O}$ notation} +% \onslide<1->{ +% +% \begin{algorithm}[H]\caption{Fibonacci} +% \setlength{\lineskip}{7pt} +% \begin{algorithmic}[1] +% \Function{fib}{$n$} +% \If{$n <= 1$} +% \State \textbf{return} $1$ +% \Else +% \State \textbf{return} fib($n-1$) + fib($n-2$) +% \EndIf +% +% \EndFunction +% \end{algorithmic} +% \end{algorithm} +% } +% \onslide<2->{ +% \[ +% \langle x,y \rangle = +% \begin{cases} +% \displaystyle $\mathcal{O}(1)$ & \text{if $n \leq 2$}\\ +% \displaystyle $ 2 \mathcal{T}(\frac{n}{2})$ & \text{if $n > 2$} +% \end{cases} +% \] } +% \end{frame} + + +\begin{frame} + \frametitle{Big $\mathcal{O}$ notation} +\begin{tikzpicture} +\begin{axis}[ + axis lines = left, + xlabel = $n$ (Data Input), + ylabel = {$t$ (time)}, + legend pos=north east, + very thick, + ymax = 20, + yticklabels=\empty, + xticklabels=\empty, + scale only axis=true, + width=12cm, height=6cm, + ] +%Below the red parabola is defined +\addplot [ + domain= 1:6, + samples=100, + color=red, +] +{1}; +\addlegendentry{$\mathcal{O}(1)$} +%Here the blue parabloa is defined +\addplot [ + domain= 1:6, + samples=100, + color=green, +] +{x}; +\addlegendentry{$\mathcal{O}(n)$} +\addplot [ + domain= 1:6, + samples=100, + color=blue, +] +{x^2}; +\addlegendentry{$\mathcal{O}(n^2)$} +\addplot [ + domain= 1:6, + samples=100, + color=purple, +] +{x^3}; +\addlegendentry{$\mathcal{O}(n^3)$} +\addplot [ + domain= 1:3, + samples=100, + color=black, +] +{exp(x)}; +\addlegendentry{$\mathcal{O}(e^n)$} +\addplot [ + domain= 1:6, + samples=100, + color=orange, +] +{log2(x)}; +\addlegendentry{$\mathcal{O}(\log n)$} +\end{axis} +\end{tikzpicture} + +\end{frame} + +\begin{frame} + \frametitle{Big $\mathcal{O}$ notation} +\begin{tikzpicture} +\begin{axis}[ + axis lines = left, + xlabel = $n$ (Data Input), + ylabel = {$t$ (time)}, + legend pos=north east, + very thick, + ymax = 500, + yticklabels=\empty, + xticklabels=\empty, + scale only axis=true, + width=12cm, height=6cm, + ] +\addplot [ + domain= 1:20, + samples=100, + color=red, +] +{1}; +\addlegendentry{$\mathcal{O}(1)$} +\addplot [ + domain= 1:20, + samples=100, + color=green, +] +{x}; +\addlegendentry{$\mathcal{O}(n)$} +\addplot [ + domain= 1:20, + samples=100, + color=blue, +] +{x^2}; +\addlegendentry{$\mathcal{O}(n^2)$} +\addplot [ + domain= 1:10, + samples=100, + color=purple, +] +{x^3}; +\addlegendentry{$\mathcal{O}(n^3)$} +\addplot [ + domain= 1:10, + samples=100, + color=black, +] +{exp(x)}; +\addlegendentry{$\mathcal{O}(e^n)$} +\addplot [ + domain= 1:20, + samples=100, + color=orange, +] +{log2(x)}; +\addlegendentry{$\mathcal{O}(\log n)$} +\end{axis} +\end{tikzpicture} + +\end{frame} diff --git a/buch/papers/multiplikation/presentation/slides/blas.tex b/buch/papers/multiplikation/presentation/slides/blas.tex new file mode 100644 index 0000000..ed498a3 --- /dev/null +++ b/buch/papers/multiplikation/presentation/slides/blas.tex @@ -0,0 +1,18 @@ +\begin{frame} +\frametitle{BLAS, LAPACK} +\begin{itemize} + \item Basic Linear Algebra Subprograms + \begin{itemize} + \item $\mathbf{y} = \alpha \mathbf{x}+\mathbf{y}$ + \item $\mathbf{y} = \alpha \mathbf{A}\mathbf{x}+ \beta \mathbf{y}$ + \item $\mathbf{C} = \alpha \mathbf{A}\mathbf{B}+ \beta \mathbf{C}$ + + \end{itemize} + \item Linear Algebra Package + \begin{itemize} + \item QR decomposition + \item Singular value decomposition + \item Eigenvalues + \end{itemize} +\end{itemize} +\end{frame} diff --git a/buch/papers/multiplikation/presentation/slides/conclusuion.tex b/buch/papers/multiplikation/presentation/slides/conclusuion.tex new file mode 100644 index 0000000..e69de29 --- /dev/null +++ b/buch/papers/multiplikation/presentation/slides/conclusuion.tex diff --git a/buch/papers/multiplikation/presentation/slides/logo.pdf b/buch/papers/multiplikation/presentation/slides/logo.pdf Binary files differnew file mode 100644 index 0000000..d78ca88 --- /dev/null +++ b/buch/papers/multiplikation/presentation/slides/logo.pdf diff --git a/buch/papers/multiplikation/presentation/slides/meas.tex b/buch/papers/multiplikation/presentation/slides/meas.tex new file mode 100644 index 0000000..489c010 --- /dev/null +++ b/buch/papers/multiplikation/presentation/slides/meas.tex @@ -0,0 +1,42 @@ +\begin{frame} + \frametitle{Measurements Python} + \only<1>{ + \includegraphics[width=\textwidth,height=0.9\textheight,keepaspectratio]{../code/meas_8.pdf}} + \only<2>{ + \includegraphics[width=\textwidth,height=0.9\textheight,keepaspectratio]{../code/meas_16.pdf}} + \only<3>{ + \includegraphics[width=\textwidth,height=0.9\textheight,keepaspectratio]{../code/meas_32.pdf}} + \only<4>{ + \includegraphics[width=\textwidth,height=0.9\textheight,keepaspectratio]{../code/meas_64.pdf}} + \only<5>{ + \includegraphics[width=\textwidth,height=0.9\textheight,keepaspectratio]{../code/meas_128.pdf}} + \only<6>{ + \includegraphics[width=\textwidth,height=0.9\textheight,keepaspectratio]{../code/meas_256.pdf}} + \only<7>{ + \includegraphics[width=\textwidth,height=0.9\textheight,keepaspectratio]{../code/meas_512.pdf}} + \only<8>{ + \includegraphics[width=\textwidth,height=0.9\textheight,keepaspectratio]{../code/meas_1024.pdf}} +\end{frame} + + +\begin{frame} + \frametitle{Measurements C} + \only<1>{ + \includegraphics[width=\textwidth,height=0.9\textheight,keepaspectratio]{../code/c_meas_8.pdf}} + \only<2>{ + \includegraphics[width=\textwidth,height=0.9\textheight,keepaspectratio]{../code/c_meas_16.pdf}} + \only<3>{ + \includegraphics[width=\textwidth,height=0.9\textheight,keepaspectratio]{../code/c_meas_32.pdf}} + \only<4>{ + \includegraphics[width=\textwidth,height=0.9\textheight,keepaspectratio]{../code/c_meas_64.pdf}} + \only<5>{ + \includegraphics[width=\textwidth,height=0.9\textheight,keepaspectratio]{../code/c_meas_128.pdf}} + \only<6>{ + \includegraphics[width=\textwidth,height=0.9\textheight,keepaspectratio]{../code/c_meas_256.pdf}} + \only<7>{ + \includegraphics[width=\textwidth,height=0.9\textheight,keepaspectratio]{../code/c_meas_512.pdf}} + \only<8>{ + \includegraphics[width=\textwidth,height=0.9\textheight,keepaspectratio]{../code/c_meas_1024.pdf}} + \only<9>{ + \includegraphics[width=\textwidth,height=0.9\textheight,keepaspectratio]{../code/c_meas_2048.pdf}} +\end{frame} diff --git a/buch/papers/multiplikation/presentation/slides/nn.tex b/buch/papers/multiplikation/presentation/slides/nn.tex new file mode 100644 index 0000000..e74e970 --- /dev/null +++ b/buch/papers/multiplikation/presentation/slides/nn.tex @@ -0,0 +1,97 @@ + +\begin{frame} + \frametitle{Neural Network} + \centering +\newcommand{\inputnum}{4} + +% Hidden layer neurons'number +\newcommand{\hiddennumA}{5} +\newcommand{\hiddennumB}{6} + +% Output layer neurons'number +\newcommand{\outputnum}{4} + +\begin{tikzpicture} + + +% Input Layer +\foreach \i in {1,...,\inputnum} +{ + \node[circle, + minimum size = 6mm, + fill=blue!30] (Input-\i) at (0,-\i) {}; +} + +% Hidden Layer1 +\foreach \i in {1,...,\hiddennumA} +{ + \node[circle, + minimum size = 6mm, + fill=red!50, + yshift=(\hiddennumA-\inputnum)*5 mm + ] (Hidden1-\i) at (2.5,-\i) {}; +} + +% Hidden Layer2 +\foreach \i in {1,...,\hiddennumB} +{ + \node[circle, + minimum size = 6mm, + fill=red!50, + yshift=(\hiddennumB-\inputnum)*5 mm + ] (Hidden2-\i) at (5,-\i) {}; +} + +% Output Layer +\foreach \i in {1,...,\outputnum} +{ + \node[circle, + minimum size = 6mm, + fill=green!50, + yshift=(\outputnum-\inputnum)*5 mm + ] (Output-\i) at (7.5,-\i) {}; +} + +% Connect neurons In-Hidden +\foreach \i in {1,...,\inputnum} +{ + \foreach \j in {1,...,\hiddennumA} + { + \draw[->, shorten >=1pt] (Input-\i) -- (Hidden1-\j); + } +} + +% Connect neurons In-Hidden +\foreach \i in {1,...,\hiddennumA} +{ + \foreach \j in {1,...,\hiddennumB} + { + \draw[->, shorten >=1pt] (Hidden1-\i) -- (Hidden2-\j); + } +} + +% Connect neurons Hidden-Out +\foreach \i in {1,...,\hiddennumB} +{ + \foreach \j in {1,...,\outputnum} + { + \draw[->, shorten >=1pt] (Hidden2-\i) -- (Output-\j); + } +} + +% Inputs +\foreach \i in {1,...,\inputnum} +{ + \draw[<-, shorten <=1pt] (Input-\i) -- ++(-1,0) + node[left]{\LARGE{$x_{\i}$}}; +} + +% Outputs +\foreach \i in {1,...,\outputnum} +{ + \draw[->, shorten <=1pt] (Output-\i) -- ++(1,0) + node[right]{\LARGE{$y_{\i}$}}; +} + +\end{tikzpicture} +\end{frame} diff --git a/buch/papers/multiplikation/presentation/slides/parcomp.tex b/buch/papers/multiplikation/presentation/slides/parcomp.tex new file mode 100644 index 0000000..1ba39ee --- /dev/null +++ b/buch/papers/multiplikation/presentation/slides/parcomp.tex @@ -0,0 +1,66 @@ +% !TEX root = presentation.tex + +\begin{frame} + \frametitle{Vector-Matrix Multiplication} +\center{ + \begin{tikzpicture}[ampersand replacement=\&] + + \matrix (A)[matrix of math nodes, label skeleton, left delimiter=[,right delimiter={]}] + { + A_{1,1} \& A_{1,2} \& A_{1,3} \& A_{1,4} \\ + }; + + \matrix (B)[matrix of math nodes, label skeleton, left delimiter=[,right delimiter={]}] at (5,-0.95) + { + B_{1,1} \& B_{1,2} \& B_{1,3} \& B_{1,4} \& B_{1,5} \\ + B_{2,1} \& B_{2,2} \& B_{2,3} \& B_{2,4} \& B_{2,5} \\ + B_{3,1} \& B_{3,2} \& B_{3,3} \& B_{3,4} \& B_{3,5} \\ + B_{4,1} \& B_{4,2} \& B_{4,3} \& B_{4,4} \& B_{4,5} \\ + }; + + \matrix (C)[matrix of math nodes, label skeleton, left delimiter=[,right delimiter={]}] at (5,-3) + { + C_{1,1} \& C_{1,2} \& C_{1,3} \& C_{1,4} \& C_{1,5}\\ + }; + + \foreach \i in {1,...,4} + { + \pgfmathtruncatemacro{\ii}{\i+1} + \onslide<\ii>{ + + \foreach \j in {1,...,5} + { + \draw[thick] (A-1-\i.south) to [out=-90,in=135]node[visible on=<\i->, anchor=north]{} (B-\i-\j.center); + + } + } + } + + + \end{tikzpicture} +} +\end{frame} + + +\begin{frame} + \frametitle{DSP Architecture} +\scalebox{2}{ + \begin{tikzpicture} + \node (mul) at (0,0) [circle,draw=black,inner sep=0pt,minimum size=0.5cm] {X}; + \node (mac) at (2,0) [circle,draw=black,inner sep=0pt,minimum size=0.5cm] {\textbf{+}}; + + \node at (-2,0.3) {$A[n]$}; + \node at (0.4,2) {$B[n]$}; + \node at (4,0.3) {$C[n]$}; + + \draw[thick, ->] (-2,0) --++ (mul); + \draw[thick, ->] (0,2) --++ (mul); + \draw[thick, ->] (mul) -- (mac); + \draw[thick] (mac) --++ (1,0) node (i) {}; + \draw[thick, ->] (i.center) --++ (0,1) --++ (-1,0) -- (mac); + \draw[thick, ->] (i.center) --++ (1,0); + + + \end{tikzpicture} + } +\end{frame} diff --git a/buch/papers/multiplikation/presentation/slides/slides.tex b/buch/papers/multiplikation/presentation/slides/slides.tex new file mode 100644 index 0000000..64edb86 --- /dev/null +++ b/buch/papers/multiplikation/presentation/slides/slides.tex @@ -0,0 +1,15 @@ +% !TEX root = presentation.tex +\begin{frame} +\titlepage +\end{frame} +% +\section{Big $\mathcal{O}$} +\input{slides/BigO.tex} +\section{Strassen's Algorithm} +\input{slides/strassen.tex} +% \input{slides/nn.tex} +\section{Measurements} +\input{slides/meas.tex} +% \input{slides/parcomp.tex} +\section{How To Matrix Multiply} +\input{slides/blas.tex} diff --git a/buch/papers/multiplikation/presentation/slides/strassen.tex b/buch/papers/multiplikation/presentation/slides/strassen.tex new file mode 100644 index 0000000..c3398d5 --- /dev/null +++ b/buch/papers/multiplikation/presentation/slides/strassen.tex @@ -0,0 +1,429 @@ +\begin{frame} + \frametitle{Strassen's Algorithm} + \includegraphics[page=1,width=\textwidth,height=0.8\textheight,keepaspectratio]{../papers/Strassen_original_1969.pdf} + \includegraphics[page=2,width=\textwidth,height=0.8\textheight,keepaspectratio]{../papers/Strassen_original_1969.pdf} \includegraphics[page=3,width=\textwidth,height=0.8\textheight,keepaspectratio]{../papers/Strassen_original_1969.pdf} + \end{frame} + +\begin{frame} + \frametitle{Strassen's Algorithm} + \centering + \large +\onslide<1->{ + $ + \mathbf{A B = C} + $ +} + +\onslide<2->{ + + +\medskip + $ + \begin{bmatrix} + A_{11} & A_{12}\\ + A_{21} & A_{22} + \end{bmatrix} + \begin{bmatrix} + B_{11} & B_{12}\\ + B_{21} & B_{22} + \end{bmatrix} + = + \begin{bmatrix} + C_{11} & C_{12}\\ + C_{21} & C_{22} + \end{bmatrix} + $ + } + + + \onslide<3->{ + +\medskip +$ +C_{11} = A_{11} \cdot B_{11} + A_{12} \cdot B_{21} +$ + +$ +C_{12} = A_{11} \cdot B_{12} + A_{12} \cdot B_{22} +$ + +$ +C_{21} = A_{21} \cdot B_{11} + A_{22} \cdot B_{21} +$ + +$ +C_{22} = A_{21} \cdot B_{12} + A_{22} \cdot B_{22} +$ +} +\end{frame} + +\input{slides/algo.tex} + + + +\begin{frame} + \frametitle{Strassen's Algorithm} + \begin{columns} + \begin{column}{0.5\textwidth} + \onslide<1->{ + \large + \begin{math} + \begin{aligned} + \text{I} &= (A_{11} + A_{22}) \cdot (B_{11} + B_{22}) \\ + \text{II} &= (A_{21} + A_{22}) \cdot B_{11} \\ + \text{III} &= A_{11} \cdot (B_{12}-B_{22}) \\ + \text{IV} &= A_{22} \cdot (-B_{11}+B_{21}) \\ + \text{V} &= (A_{11} + A_{12}) \cdot B_{22} \\ + \text{VI} &= (-A_{11} + A_{21}) \cdot (B_{11} + B_{12}) \\ + \text{VII} &= (A_{12} - A_{22}) \cdot (B_{21} + B_{22}) \\ + \end{aligned} + \end{math} + } + \end{column} + + \begin{column}{0.5\textwidth} + \onslide<2->{ + \large + \begin{math} + \begin{aligned} + C_{11} &= \text{I} + \text{IV} - \text{V} + \text{VII} \\ + C_{21} &= \text{II} + \text{IV} \\ + C_{12} &= \text{III} + \text{V}\\ + C_{22} &= \text{I} + \text{III} - \text{II} + \text{VI} \\ + \end{aligned} + \end{math} + } + \end{column} +\end{columns} + +\onslide<3->{ + +\bigskip +\centering +\tiny +\begin{math} +\begin{aligned} + C_{11} &= (A_{11} + A_{22}) \cdot (B_{11} + B_{22}) + A_{22} \cdot (-B_{11}+B_{21}) - (A_{11} + A_{12}) \cdot B_{22} + (A_{12} - A_{22}) \cdot (B_{21} + B_{22}) \\ + C_{11} &= A_{11}B_{11} + A_{11}B_{22} + A_{22}B_{11} + A_{22}B_{22} -A_{22}B_{11}+A_{22}B_{21} - A_{11}B_{22} - A_{12}B_{22}+ A_{12}B_{21} + A_{12}B_{22} - A_{22}B_{21} - A_{22}B_{22} \\ + C_{11} &= A_{11}B_{11} + A_{12}B_{21} +\end{aligned} +\end{math} +} + +\end{frame} + + +\begin{frame} +\begin{adjustbox}{width=\textwidth} +\begin{tikzpicture}[ampersand replacement=\&] + + \foreach \i in {1,...,4} + { + \small{ + \matrix (X\i)[matrix of math nodes,nodes in empty cells, + nodes = {draw, minimum size=10mm, + anchor=center, + inner sep=0pt, outer sep=0pt}, + column sep=-\pgflinewidth, + row sep=-\pgflinewidth, + ] at (0,-\i*5) + { + A_{11}B_{11} \& A_{12}B_{11} \& A_{21}B_{11} \& A_{22}B_{11} \\ + A_{11}B_{21} \& A_{12}B_{21} \& A_{21}B_{21} \& A_{22}B_{21} \\ + A_{11}B_{11} \& A_{12}B_{12} \& A_{21}B_{12} \& A_{22}B_{12} \\ + A_{11}B_{22} \& A_{12}B_{22} \& A_{21}B_{22} \& A_{22}B_{22} \\ + };} + + \foreach \j in {1,...,7} + { + \matrix(M\i\j)[matrix of math nodes,nodes in empty cells, + nodes = {draw, minimum size=10mm, + anchor=center, + inner sep=0pt, outer sep=0pt}, + column sep=-\pgflinewidth, + row sep=-\pgflinewidth, + ] at (\j*5,-\i*5) + { + \& \& \& \\ + \& \& \& \\ + \& \& \& \\ + \& \& \& \\ + }; + } + } + +\huge{ + \node at (-3,-20) {$C_{22}=$}; + \node at (-3,-15) {$C_{21}=$} ; + \node at (-3,-10) {$C_{12}=$} ; + \node at (-3,-5) {$C_{11}=$} ; + + \node at (5,-2) {I}; + \node at (10,-2) {II}; + \node at (15,-2) {III}; + \node at (20,-2) {IV}; + \node at (25,-2) {V}; + \node at (30,-2) {VI}; + \node at (35,-2) {VII}; + } + + + \node[opacity=0.5, rounded corners=0pt, inner sep=-1pt, fill=green, fit=(X1-1-1)] {}; + \node[opacity=0.5, rounded corners=0pt, inner sep=-1pt, fill=green, fit=(X1-2-2)] {}; + \node[opacity=0.5, rounded corners=0pt, inner sep=-1pt, fill=green, fit=(X2-3-1)] {}; + \node[opacity=0.5, rounded corners=0pt, inner sep=-1pt, fill=green, fit=(X2-4-2)] {}; + \node[opacity=0.5, rounded corners=0pt, inner sep=-1pt, fill=green, fit=(X3-1-3)] {}; + \node[opacity=0.5, rounded corners=0pt, inner sep=-1pt, fill=green, fit=(X3-2-4)] {}; + \node[opacity=0.5, rounded 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+\end{tikzpicture} +\end{adjustbox} +\end{frame} + + +\begin{frame} + \frametitle{Strassen's Algorithm} + \begin{columns} + \begin{column}{0.5\textwidth} + \large + \begin{math} + \begin{aligned} + \text{I} &= (A_{11} + A_{22}) \cdot (B_{11} + B_{22}) \\ + \text{II} &= (A_{21} + A_{22}) \cdot B_{11} \\ + \text{III} &= A_{11} \cdot (B_{12}-B_{22}) \\ + \text{IV} &= A_{22} \cdot (-B_{11}+B_{21}) \\ + \text{V} &= (A_{11} + A_{12}) \cdot B_{22} \\ + \text{VI} &= (-A_{11} + A_{21}) \cdot (B_{11} + B_{12}) \\ + \text{VII} &= (A_{12} - A_{22}) \cdot (B_{21} + B_{22}) \\ + \end{aligned} + \end{math} + + \end{column} + + \begin{column}{0.5\textwidth} + \large + \begin{math} + \begin{aligned} + C_{11} &= \text{I} + \text{IV} - \text{V} + \text{VII} \\ + C_{21} &= \text{II} + \text{IV} \\ + C_{12} &= \text{III} + \text{V}\\ + C_{22} &= \text{I} + \text{III} - \text{II} + \text{VI} \\ + \end{aligned} + \end{math} + + \end{column} +\end{columns} +\end{frame} + + + +\begin{frame} + \frametitle{Strassen's Algorithm} + +\begin{columns} + \begin{column}{0.5\textwidth} +\large +\begin{math} +\begin{aligned} +\text{\textbf{I}} &= (\mathbf{A_{11}} + \mathbf{A_{22}}) \cdot (\mathbf{B_{11}} + \mathbf{B_{22}}) \\ +\text{\textbf{II}} &= (\mathbf{A_{21}} + \mathbf{A_{22}}) \cdot \mathbf{B_{11}} \\ +\text{\textbf{III}} &= \mathbf{A_{11}} \cdot (\mathbf{B_{12}}-\mathbf{B_{22}}) \\ +\text{\textbf{IV}} &= \mathbf{A_{22}} \cdot (-\mathbf{B_{11}}+\mathbf{B_{21}}) \\ +\text{\textbf{V}} &= (\mathbf{A_{11}} + \mathbf{A_{12}}) \cdot \mathbf{B_{22}} \\ +\text{\textbf{VI}} &= (-\mathbf{A_{11}} + \mathbf{A_{21}}) \cdot (\mathbf{B_{11}} + \mathbf{B_{12}}) \\ +\text{\textbf{VII}} &= (\mathbf{A_{12}} - \mathbf{A_{22}}) \cdot (\mathbf{B_{21}} + \mathbf{B_{22}}) \\ +\end{aligned} +\end{math} + +\end{column} + +\begin{column}{0.5\textwidth} + \large + \begin{math} + \begin{aligned} + \mathbf{C_{11}} &= \text{\textbf{I}} + \text{\textbf{IV}} - \text{\textbf{V}} + \text{\textbf{VII}} \\ + \mathbf{C_{21}} &= \text{\textbf{II}} + \text{\textbf{IV}} \\ + \mathbf{C_{12}} &= \text{\textbf{III}} + \text{\textbf{V}}\\ + \mathbf{C_{22}} &= \text{\textbf{I}} + \text{\textbf{III}} - \text{\textbf{II}} + \text{\textbf{VI}} \\ + \end{aligned} + \end{math} + +\end{column} +\end{columns} + +\end{frame} + +\begin{frame} + \frametitle{Algorithm} + \onslide<1->{ + + \scalebox{0.45}{\parbox{\linewidth}{ + \begin{algorithm}[H]\caption{Strassen Matrix Multiplication} + \setlength{\lineskip}{7pt} + \begin{algorithmic}[1] + \Function{strassen}{$\textbf{A}, \textbf{B}, n$} + \If{$n = 2$} + \State $ \mathbf{C} \gets zeros((n, n))$ + \State $P \gets (A[0][0]+A[1][1])\cdot( B[0][0]+B[1][1])$ + \State $Q \gets (A[1][0]+A[1][1])\cdot B[0][0]$ + \State $R \gets A[0][0]\cdot (B[0][1]-B[1][1])$ + \State $S \gets A[1][1]\cdot (B[1][0]-B[0][0])$ + \State $T \gets (A[0][0]+A[0][1])\cdot B[1][1]$ + \State $U \gets (A[1][0]-A[0][0])\cdot (B[0][0]+B[0][1])$ + \State $V \gets (A[0][1]-A[1][1])\cdot (B[1][0]+B[1][1])$ + \State $C[0][0] \gets P+S-T+V$ + \State $C[0][1] \gets R+T$ + \State $C[1][0] \gets Q+S$ + \State $C[1][1] \gets P+R-Q+U$ + \Else + \State $ m \gets n/2$ + \State $\mathbf{A11}, \mathbf{A12}, \mathbf{A21}, \mathbf{A22} \gets \mathbf{A}[:m][:m], \mathbf{A}[:m][m:], \mathbf{A}[m:][:m], \mathbf{A}[m:][m:]$ + \State $\mathbf{B11}, \mathbf{B12}, \mathbf{B21}, \mathbf{B22} \gets \mathbf{B}[:m][:m], \mathbf{B}[:m][m:], \mathbf{B}[m:][:m], \mathbf{B}[m:][m:]$ + + \State $ \mathbf{P} \gets \text{strassen}((\mathbf{A11}+ \mathbf{A22}),(\mathbf{B11}+\mathbf{B22}), m)$ + \State $ \mathbf{Q} \gets \text{strassen}((\mathbf{A21}+ \mathbf{A22}), \mathbf{B11},m)$ + \State $ \mathbf{R} \gets \text{strassen}( \mathbf{A11},(\mathbf{B12}- \mathbf{B22}),m)$ + \State $ \mathbf{S} \gets \text{strassen}( \mathbf{A22},(\mathbf{B21}- \mathbf{B11}),m)$ + \State $ \mathbf{T} \gets \text{strassen}((\mathbf{A11}+ \mathbf{A12}), \mathbf{B22},m)$ + \State $ \mathbf{U} \gets \text{strassen}((\mathbf{A21}- \mathbf{A11}),(\mathbf{B11}+\mathbf{B12}),m)$ + \State $ \mathbf{V} \gets \text{strassen}((\mathbf{A12}- \mathbf{A22}),(\mathbf{B21}+\mathbf{B22}),m)$ + + + + \State $\mathbf{C11} \gets \mathbf{P+S-T+V}$ + \State $\mathbf{C12} \gets \mathbf{R+T}$ + \State $\mathbf{C21} \gets \mathbf{Q+S}$ + \State $\mathbf{C22} \gets \mathbf{P+R-Q+U}$ + \State $ C \gets vstack((hstack((C11, C12)), hstack((C21, C22))))$ + + \EndIf + \State \textbf{return} $\textbf{C}$ + + \EndFunction + \end{algorithmic} + \end{algorithm} + }}} +% \[ +% \mathcal{T}(n) = \left\{\begin{array}{lr} +% 1, & \text{if} n \leq 2\\ +% 7 \mathcal{T}(\frac{n}{2}) + n^2, & \text{if} n > 2\\ +% \end{array}\right\} +% \] +\only<2>{ + $ + \mathcal{T}(n) = + \begin{cases} + 1 & \text{if } n \leq 2\\ + 7 \cdot \mathcal{T}(\frac{n}{2}) + n^2 & \text{if } n > 2 + \end{cases} = \mathcal{O}(n^{\log_2 7})$ + +} +\only<3>{ + $ + \mathcal{T}(n) = + \begin{cases} + 1 & \text{if } n \leq 2\\ + 7 \cdot \mathcal{T}(\frac{n}{2}) + n^2 & \text{if } n > 2 + \end{cases} = \mathcal{O}(n^{2.81})$ + +} + +\end{frame} + +\begin{frame} + \frametitle{Algorithm} + \onslide<1->{ + + \scalebox{0.45}{\parbox{\linewidth}{ + \begin{algorithm}[H]\caption{Strassen Matrix Multiplication} + \setlength{\lineskip}{7pt} + \begin{algorithmic}[1] + \Function{MM}{$\textbf{A}, \textbf{B}, n$} + \If{$n = 2$} + \State $ \mathbf{C} \gets zeros((n, n))$ + \State $C[0, 0] \gets A[0][0]*B[0][0]+A[0][1]*B[1][0]$ + \State $C[0, 1] \gets A[0][0]*B[0][1]+A[0][1]*B[1][1]$ + \State $C[1, 0] \gets A[1][0]*B[0][0]+A[1][1]*B[1][0]$ + \State $C[1, 1] \gets A[1][0]*B[0][1]+A[1][1]*B[1][1]$ + \Else + \State $ m \gets n/2$ + \State $\mathbf{A11}, \mathbf{A12}, \mathbf{A21}, \mathbf{A22} \gets \mathbf{A}[:m][:m], \mathbf{A}[:m][m:], \mathbf{A}[m:][:m], \mathbf{A}[m:][m:]$ + \State $\mathbf{B11}, \mathbf{B12}, \mathbf{B21}, \mathbf{B22} \gets \mathbf{B}[:m][:m], \mathbf{B}[:m][m:], \mathbf{B}[m:][:m], \mathbf{B}[m:][m:]$ + + \State $\mathbf{C11} \gets \text{MM}(\mathbf{A11}, \mathbf{B11}) + \text{MM}(\mathbf{A12}, \mathbf{B21})$ + \State $\mathbf{C12} \gets \text{MM}(\mathbf{A11},\mathbf{B12}) + \text{MM}(\mathbf{A12},\mathbf{B22})$ + \State $\mathbf{C21} \gets \text{MM}(\mathbf{A21}, \mathbf{B11}) + \text{MM}(\mathbf{A22}, \mathbf{B21})$ + \State $\mathbf{C22} \gets \text{MM}(\mathbf{A21}, \mathbf{B12}) + \text{MM}(\mathbf{A22}, \mathbf{B22})$ + \State $ C \gets vstack((hstack((C11, C12)), hstack((C21, C22))))$ + + \EndIf + \State \textbf{return} $\textbf{C}$ + + \EndFunction + \end{algorithmic} + \end{algorithm} + \bigskip + \bigskip + \bigskip + \bigskip + \bigskip + }}} + +\only<2>{ + + + $ + \mathcal{T}(n) = + \begin{cases} + 1 & \text{if } n \leq 2\\ + 8 \cdot \mathcal{T}(\frac{n}{2}) + n^2 & \text{if } n > 2 + \end{cases} = \mathcal{O}(n^{\log_2 8})$ + +} +\only<3>{ + $ + \mathcal{T}(n) = + \begin{cases} + 1 & \text{if } n \leq 2\\ + 8 \cdot \mathcal{T}(\frac{n}{2}) + n^2 & \text{if } n > 2 + \end{cases} = \mathcal{O}(n^{3})$ + +} + +\end{frame} diff --git a/buch/papers/multiplikation/presentation/tikz/algo.pdf b/buch/papers/multiplikation/presentation/tikz/algo.pdf Binary files differnew file mode 100644 index 0000000..752f42e --- /dev/null +++ b/buch/papers/multiplikation/presentation/tikz/algo.pdf diff --git a/buch/papers/multiplikation/presentation/tikz/algo.tex b/buch/papers/multiplikation/presentation/tikz/algo.tex new file mode 100644 index 0000000..0b2c567 --- /dev/null +++ b/buch/papers/multiplikation/presentation/tikz/algo.tex @@ -0,0 +1,52 @@ +\documentclass[border=10pt]{article} +\usepackage[left=25mm,right=25mm,top=25mm,bottom=25mm]{geometry} +\usepackage[utf8]{inputenc} +\usepackage[T1]{fontenc} +\usepackage{times} +\usepackage{geometry} +\usepackage{amsmath} +\usepackage{amssymb} +\usepackage{mathrsfs} +\usepackage{amsfonts} +\usepackage{amsthm} +\usepackage{lipsum} +\usepackage{amscd} +\usepackage{graphicx} +\usepackage{fancyhdr} +\usepackage{textcomp} +\usepackage{txfonts} +\usepackage[all]{xy} +\usepackage{paralist} +\usepackage[colorlinks=true]{hyperref} +\usepackage{array} +\usepackage{tikz} +\usepackage{slashed} +\usepackage{pdfpages} +\usepackage{cite} +\usepackage{url} +\usepackage{algorithm} +\usepackage[noend]{algpseudocode} +\usepackage{listings} +\usepackage{multirow} +\usepackage{color} + +\begin{document} + +\begin{algorithm}[H]\caption{Square Matrix Multiplication} + \setlength{\lineskip}{7pt} + \begin{algorithmic}[1] + \Function{MM}{$\textbf{A}, \textbf{B}, \textbf{C}, n$} + \State $sum \gets 0$ + \For{$i = 0,1,2 \dots,n-1$} + \For{$j = 0,1,2 \dots,n-1$} + \State $sum \gets 0$ + \For{$k = 0,1,2 \dots,n-1$} + \State $sum \gets sum + \textbf{A}[i][k] \cdot \textbf{B}[k][j]$ + \EndFor + \State $\textbf{C}[i][j] \gets sum $ + \EndFor + \EndFor + \EndFunction + \end{algorithmic} +\end{algorithm} +\end{document} diff --git a/buch/papers/multiplikation/problemstellung.tex b/buch/papers/multiplikation/problemstellung.tex new file mode 100755 index 0000000..b20a791 --- /dev/null +++ b/buch/papers/multiplikation/problemstellung.tex @@ -0,0 +1,104 @@ +% +% teil1.tex -- Beispiel-File für das Paper +% +% (c) 2020 Prof Dr Andreas Müller, Hochschule Rapperswil +% +\section{Problemstellung} +\rhead{Problemstellung} +Dank der breiten Anwendung der Matrizenmultiplikation ist eine effiziente L\"osung dieser Operation von grosser Bedeutung. +Das Ziel dieses Papers ist verschiedenen Algorithmen der Matrizenmultiplikation vorzustellen. +Wobei gezielt auf Algorithmen, welche das Problem schneller als der Standard Algorithmus L\"osen eingegangen wird. + +\subsection{Big $\mathcal{O}$ Notation} +Die Big $\mathcal{O}$ Notation beschreibt die Laufzeitkomplexit\"at eines Algorithmus \cite{multiplikation:bigo}. +$f(x) \in \mathcal{O}(g(x))$ besagt das die Funktion $f$ nicht wesentlich schneller w\"achst als $g$ wenn $x \rightarrow \infty$. +Vereinfacht werden f\"ur Algorithmen die folgende Notation verwendet: +\begin{itemize} + \item $f \in \mathcal{O}(1) \rightarrow f$ ist beschr\"ankt + \item $f \in \mathcal{O}(n) \rightarrow f$ w\"achst linear + \item $f \in \mathcal{O}(n^2) \rightarrow f$ w\"achst quadratisch + \item $f \in \mathcal{O}(\log n) \rightarrow f$ w\"achst logarithmisch + \item $f \in \mathcal{O}(n \log n) \rightarrow f$ hat super-lineares Wachstum + \item $f \in \mathcal{O}(e^n) \rightarrow f$ w\"achst exponentiell + \item usw. +\end{itemize} + +In der Abbildung \ref{multiplikation:fig:bigo} k\"onnen die Verschiedenen Laufzeiten miteinander verglichen werden. + +\begin{figure} + \center + \includegraphics[]{papers/multiplikation/images/bigo} + \caption{Verschiedene Laufzeiten} + \label{multiplikation:fig:bigo} +\end{figure} + +\subsubsection{Beispiel Algorithmen} +\paragraph{Beschr\"ankter Algorithmus} + +Ein Beispiel eines Beschr\"ankter Verhalten $\mathcal{O}(1)$, kann im Algorithmus \ref{multiplikation:alg:b1} entnommen werden. + +\begin{algorithm}\caption{} + \label{multiplikation:alg:b1} + \setlength{\lineskip}{7pt} + \begin{algorithmic} + \Function{B1}{$a, b$} + \State \textbf{return} $a+b$ + \EndFunction + \end{algorithmic} +\end{algorithm} + +Wobei Konstanten nicht beachtet werden, der Algorithmus \ref{multiplikation:alg:b2} f\"uhrt ebenso zu $\mathcal{O}(1)$ und nicht zu $\mathcal{O}(2)$. + +\begin{algorithm}\caption{} + \label{multiplikation:alg:b2} + \setlength{\lineskip}{7pt} + \begin{algorithmic} + \Function{B2}{$a, b$} + \State $ x \gets a+b $ + \State $ y \gets a \cdot b $ + \State \textbf{return} $x+y$ + \EndFunction + \end{algorithmic} +\end{algorithm} + +\paragraph{Linearer Algorithmus} + +Folgender Algorithmus \ref{multiplikation:alg:l1} hat ein lineares $\mathcal{O}(n)$ Verhalten. + +\begin{algorithm}\caption{} + \setlength{\lineskip}{7pt} + \begin{algorithmic} + \label{multiplikation:alg:l1} + \Function{L}{$\mathbf{A}, \mathbf{B}$,n} + \State $ sum \gets 0$ + \For{$i = 0,1,2 \dots,n$} + \State $ sum \gets sum + A[i] \cdot B[i] $ + \EndFor + + \State \textbf{return} $sum$ + + \EndFunction + \end{algorithmic} +\end{algorithm} + +\paragraph{Quadratischer Algorithmus} + +Folgender Algorithmus \ref{multiplikation:alg:q1} hat ein quadratisches $\mathcal{O}(n^2)$ Verhalten. + +\begin{algorithm}[H]\caption{} + \label{multiplikation:alg:q1} + \setlength{\lineskip}{7pt} + \begin{algorithmic} + \Function{Q}{$\mathbf{A}, \mathbf{B}$,n} + \State $ sum \gets 0$ + \For{$i = 0,1,2 \dots,n$} + \For{$j = 0,1,2 \dots,n$} + \State $ sum \gets sum + A[i] \cdot B[j] $ + \EndFor + \EndFor + \State \textbf{return} $sum$ + \EndFunction + \end{algorithmic} +\end{algorithm} + + diff --git a/buch/papers/multiplikation/references.bib b/buch/papers/multiplikation/references.bib index 7149fb1..9d76e8e 100644..100755 --- a/buch/papers/multiplikation/references.bib +++ b/buch/papers/multiplikation/references.bib @@ -33,3 +33,33 @@ url = {https://doi.org/10.1016/j.acha.2017.11.004} } +@article{multiplikation:winograd_1968, + title={A New Algorithm for Inner Product}, + volume={C-17}, + DOI={10.1109/tc.1968.227420}, + number={7}, + journal={IEEE Transactions on Computers}, + author={Winograd, S.}, + year={1968}, + pages={693–694} +} + +@article{multiplikation:strassen_1969, + title={Gaussian elimination is not optimal}, + volume={13}, + DOI={10.1007/bf02165411}, + number={4}, + journal={Numerische Mathematik}, + author={Strassen, Volker}, + year={1969}, + pages={354–356} +} + +@online{multiplikation:bigo, + title = {Big O notation}, + url = {https://en.wikipedia.org/wiki/Big_O_notation}, + date = {2021-07-27}, + year = {2021}, + month = {7}, + day = {27} +} diff --git a/buch/papers/multiplikation/teil0.tex b/buch/papers/multiplikation/teil0.tex deleted file mode 100644 index 082b7f5..0000000 --- a/buch/papers/multiplikation/teil0.tex +++ /dev/null @@ -1,22 +0,0 @@ -% -% einleitung.tex -- Beispiel-File für die Einleitung -% -% (c) 2020 Prof Dr Andreas Müller, Hochschule Rapperswil -% -\section{Teil 0\label{multiplikation:section:teil0}} -\rhead{Teil 0} -Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam -nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam -erat, sed diam voluptua \cite{multiplikation:bibtex}. -At vero eos et accusam et justo duo dolores et ea rebum. -Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum -dolor sit amet. - -Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam -nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam -erat, sed diam voluptua. -At vero eos et accusam et justo duo dolores et ea rebum. Stet clita -kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit -amet. - - diff --git a/buch/papers/multiplikation/teil1.tex b/buch/papers/multiplikation/teil1.tex deleted file mode 100644 index 0a6903a..0000000 --- a/buch/papers/multiplikation/teil1.tex +++ /dev/null @@ -1,55 +0,0 @@ -% -% teil1.tex -- Beispiel-File für das Paper -% -% (c) 2020 Prof Dr Andreas Müller, Hochschule Rapperswil -% -\section{Teil 1 -\label{multiplikation:section:teil1}} -\rhead{Problemstellung} -Sed ut perspiciatis unde omnis iste natus error sit voluptatem -accusantium doloremque laudantium, totam rem aperiam, eaque ipsa -quae ab illo inventore veritatis et quasi architecto beatae vitae -dicta sunt explicabo. -Nemo enim ipsam voluptatem quia voluptas sit aspernatur aut odit -aut fugit, sed quia consequuntur magni dolores eos qui ratione -voluptatem sequi nesciunt -\begin{equation} -\int_a^b x^2\, dx -= -\left[ \frac13 x^3 \right]_a^b -= -\frac{b^3-a^3}3. -\label{multiplikation:equation1} -\end{equation} -Neque porro quisquam est, qui dolorem ipsum quia dolor sit amet, -consectetur, adipisci velit, sed quia non numquam eius modi tempora -incidunt ut labore et dolore magnam aliquam quaerat voluptatem. - -Ut enim ad minima veniam, quis nostrum exercitationem ullam corporis -suscipit laboriosam, nisi ut aliquid ex ea commodi consequatur? -Quis autem vel eum iure reprehenderit qui in ea voluptate velit -esse quam nihil molestiae consequatur, vel illum qui dolorem eum -fugiat quo voluptas nulla pariatur? - -\subsection{De finibus bonorum et malorum -\label{multiplikation:subsection:finibus}} -At vero eos et accusamus et iusto odio dignissimos ducimus qui -blanditiis praesentium voluptatum deleniti atque corrupti quos -dolores et quas molestias excepturi sint occaecati cupiditate non -provident, similique sunt in culpa qui officia deserunt mollitia -animi, id est laborum et dolorum fuga \eqref{000tempmlate:equation1}. - -Et harum quidem rerum facilis est et expedita distinctio -\ref{multiplikation:section:loesung}. -Nam libero tempore, cum soluta nobis est eligendi optio cumque nihil -impedit quo minus id quod maxime placeat facere possimus, omnis -voluptas assumenda est, omnis dolor repellendus -\ref{multiplikation:section:folgerung}. -Temporibus autem quibusdam et aut officiis debitis aut rerum -necessitatibus saepe eveniet ut et voluptates repudiandae sint et -molestiae non recusandae. -Itaque earum rerum hic tenetur a sapiente delectus, ut aut reiciendis -voluptatibus maiores alias consequatur aut perferendis doloribus -asperiores repellat. - - diff --git a/buch/papers/multiplikation/teil2.tex b/buch/papers/multiplikation/teil2.tex deleted file mode 100644 index efbf31a..0000000 --- a/buch/papers/multiplikation/teil2.tex +++ /dev/null @@ -1,40 +0,0 @@ -% -% teil2.tex -- Beispiel-File für teil2 -% -% (c) 2020 Prof Dr Andreas Müller, Hochschule Rapperswil -% -\section{Teil 2 -\label{multiplikation:section:teil2}} -\rhead{Teil 2} -Sed ut perspiciatis unde omnis iste natus error sit voluptatem -accusantium doloremque laudantium, totam rem aperiam, eaque ipsa -quae ab illo inventore veritatis et quasi architecto beatae vitae -dicta sunt explicabo. Nemo enim ipsam voluptatem quia voluptas sit -aspernatur aut odit aut fugit, sed quia consequuntur magni dolores -eos qui ratione voluptatem sequi nesciunt. Neque porro quisquam -est, qui dolorem ipsum quia dolor sit amet, consectetur, adipisci -velit, sed quia non numquam eius modi tempora incidunt ut labore -et dolore magnam aliquam quaerat voluptatem. Ut enim ad minima -veniam, quis nostrum exercitationem ullam corporis suscipit laboriosam, -nisi ut aliquid ex ea commodi consequatur? Quis autem vel eum iure -reprehenderit qui in ea voluptate velit esse quam nihil molestiae -consequatur, vel illum qui dolorem eum fugiat quo voluptas nulla -pariatur? - -\subsection{De finibus bonorum et malorum -\label{multiplikation:subsection:bonorum}} -At vero eos et accusamus et iusto odio dignissimos ducimus qui -blanditiis praesentium voluptatum deleniti atque corrupti quos -dolores et quas molestias excepturi sint occaecati cupiditate non -provident, similique sunt in culpa qui officia deserunt mollitia -animi, id est laborum et dolorum fuga. Et harum quidem rerum facilis -est et expedita distinctio. Nam libero tempore, cum soluta nobis -est eligendi optio cumque nihil impedit quo minus id quod maxime -placeat facere possimus, omnis voluptas assumenda est, omnis dolor -repellendus. Temporibus autem quibusdam et aut officiis debitis aut -rerum necessitatibus saepe eveniet ut et voluptates repudiandae -sint et molestiae non recusandae. Itaque earum rerum hic tenetur a -sapiente delectus, ut aut reiciendis voluptatibus maiores alias -consequatur aut perferendis doloribus asperiores repellat. - - diff --git a/buch/papers/multiplikation/teil3.tex b/buch/papers/multiplikation/teil3.tex deleted file mode 100644 index f58508b..0000000 --- a/buch/papers/multiplikation/teil3.tex +++ /dev/null @@ -1,40 +0,0 @@ -% -% teil3.tex -- Beispiel-File für Teil 3 -% -% (c) 2020 Prof Dr Andreas Müller, Hochschule Rapperswil -% -\section{Teil 3 -\label{multiplikation:section:teil3}} -\rhead{Teil 3} -Sed ut perspiciatis unde omnis iste natus error sit voluptatem -accusantium doloremque laudantium, totam rem aperiam, eaque ipsa -quae ab illo inventore veritatis et quasi architecto beatae vitae -dicta sunt explicabo. Nemo enim ipsam voluptatem quia voluptas sit -aspernatur aut odit aut fugit, sed quia consequuntur magni dolores -eos qui ratione voluptatem sequi nesciunt. Neque porro quisquam -est, qui dolorem ipsum quia dolor sit amet, consectetur, adipisci -velit, sed quia non numquam eius modi tempora incidunt ut labore -et dolore magnam aliquam quaerat voluptatem. Ut enim ad minima -veniam, quis nostrum exercitationem ullam corporis suscipit laboriosam, -nisi ut aliquid ex ea commodi consequatur? Quis autem vel eum iure -reprehenderit qui in ea voluptate velit esse quam nihil molestiae -consequatur, vel illum qui dolorem eum fugiat quo voluptas nulla -pariatur? - -\subsection{De finibus bonorum et malorum -\label{multiplikation:subsection:malorum}} -At vero eos et accusamus et iusto odio dignissimos ducimus qui -blanditiis praesentium voluptatum deleniti atque corrupti quos -dolores et quas molestias excepturi sint occaecati cupiditate non -provident, similique sunt in culpa qui officia deserunt mollitia -animi, id est laborum et dolorum fuga. Et harum quidem rerum facilis -est et expedita distinctio. Nam libero tempore, cum soluta nobis -est eligendi optio cumque nihil impedit quo minus id quod maxime -placeat facere possimus, omnis voluptas assumenda est, omnis dolor -repellendus. Temporibus autem quibusdam et aut officiis debitis aut -rerum necessitatibus saepe eveniet ut et voluptates repudiandae -sint et molestiae non recusandae. 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+\usepackage{amsmath} +\usepackage{amssymb} +\usepackage{mathrsfs} +\usepackage{amsfonts} +\usepackage{amsthm} +\usepackage{lipsum} +\usepackage{amscd} +\usepackage{graphicx} +\usepackage{fancyhdr} +\usepackage{textcomp} +\usepackage{txfonts} +\usepackage[all]{xy} +\usepackage{paralist} +\usepackage[colorlinks=true]{hyperref} +\usepackage{array} +\usepackage{tikz} +\usepackage{slashed} +\usepackage{pdfpages} +\usepackage{cite} +\usepackage{url} +\usepackage{amsmath,amsfonts,amssymb} +\usepackage{tikz} +\usetikzlibrary{arrows,matrix,positioning} +\usetikzlibrary{overlay-beamer-styles} +\usetikzlibrary{matrix.skeleton} +\usetikzlibrary{automata,positioning} +\usepackage{listings} +\usepackage{multirow} +\usepackage{color} + +\begin{document} + +$ +A= +\begin{bmatrix} +A_{11} & A_{12}\\ +A_{21} & A_{22} +\end{bmatrix}, +B= +\begin{bmatrix} +B_{11} & B_{12}\\ +B_{21} & B_{22} +\end{bmatrix}, +C= +\begin{bmatrix} +C_{11} & C_{12}\\ +C_{21} & C_{22} +\end{bmatrix} +$ + +\medskip +$ +A \cdot B = C +$ + +\medskip +$ +C_{11} = A_{11} \cdot B_{11} + A_{12} \cdot B_{21}\\ +C_{12} = A_{11} \cdot B_{12} + A_{12} \cdot B_{22}\\ +C_{21} = A_{21} \cdot B_{11} + A_{22} \cdot B_{21}\\ +C_{22} = A_{21} \cdot B_{12} + A_{22} \cdot B_{22} +$ + +\medskip +\begin{math} +\begin{aligned} +\text{I} &= (A_{11} + A_{22}) \cdot (B_{11} + B_{22}) \\ +\text{II} &= (A_{21} + A_{22}) \cdot B_{11} \\ +\text{III} &= A_{11} \cdot (B_{12}-B_{22}) \\ +\text{IV} &= A_{22} \cdot (-B_{11}+B_{21}) \\ +\text{V} &= (A_{11} + A_{12}) \cdot B_{22} \\ +\text{VI} &= (-A_{11} + A_{21}) \cdot (B_{11} + B_{12})) \\ +\text{VII} &= (A_{12} - A_{22}) \cdot (B_{21} + B_{22}) \\ +\end{aligned} +\end{math} + + +\medskip +\begin{math} +\begin{aligned} +C_{11} &= \text{I} + \text{IV} - \text{V} + \text{VII} \\ +C_{21} &= \text{II} + \text{IV} \\ +C_{12} &= \text{III} + \text{V}\\ +C_{22} &= \text{I} + \text{III} - \text{II} + \text{VI} \\ +\end{aligned} +\end{math} + + +\medskip +\begin{math} +\begin{aligned} +C_{11} &= \text{II} + \text{IV} \\ +C_{11} &= (A_{11} + A_{22}) \cdot (B_{11} + B_{22}) + A_{22} \cdot (-B_{11}+B_{21}) - (A_{11} + A_{12}) \cdot B_{22} + (A_{12} - A_{22}) \cdot (B_{21} + B_{22})C_{21} \\ +C_{11} &= A_{11}B_{11} + A_{11}B_{22} + A_{22}B_{11} + A_{22}B_{22} -A_{22}B_{11}+A_{22}B_{21} - A_{11}B_{22} - A_{12}B_{22}+ A_{12}B_{21} + A_{12}B_{22} - A_{22}B_{21} - A_{22}B_{22} \\ +C_{11} &= A_{11}B_{11} + A_{12}B_{21} +\end{aligned} +\end{math} + +\section{Winograd} + +$ +x_1 y_1 + x_2 y_2 = (x_1 +y_2)(y_1 + x_2)-x_1 x_2 - y_1 y_2 +$ + +$ +x = (x_1, \cdots, x_n), y=(y_1, \cdots, y_n) +$ + +\[ +\xi = \sum_{j=1}^{ \lfloor n/2 \rfloor} x_{2j-1} \cdot x_{2j} +\] + +\[ +\eta = \sum_{j=1}^{ \lfloor n/2 \rfloor} y_{2j-1} \cdot y_{2j} +\] + +\[ +\langle x,y \rangle = +\begin{cases} + \displaystyle \sum_{j=1}^{ \lfloor n/2 \rfloor} (x_{2j-1} + y_{2j})(x_{2j}+y_{2j-1})-\xi - \eta & \text{if $n$ is even}\\ +\displaystyle \sum_{j=1}^{ \lfloor n/2 \rfloor} (x_{2j-1} + y_{2j})(x_{2j}+y_{2j-1})-\xi - \eta + x_n y_n & \text{if $n$ is odd} +\end{cases} +\] + +\end{document} diff --git a/buch/papers/multiplikation/tikz_formulas/algo_graph.fdb_latexmk b/buch/papers/multiplikation/tikz_formulas/algo_graph.fdb_latexmk new file mode 100644 index 0000000..ddfa880 --- /dev/null +++ b/buch/papers/multiplikation/tikz_formulas/algo_graph.fdb_latexmk @@ -0,0 +1,245 @@ +# Fdb version 3 +["pdflatex"] 1621585121 "algo_graph.tex" "algo_graph.pdf" "algo_graph" 1621585184 + "/dev/null" 1621583990 0 d41d8cd98f00b204e9800998ecf8427e "" + "/etc/texmf/web2c/texmf.cnf" 1619433543 475 c0e671620eb5563b2130f56340a5fde8 "" + "/usr/share/texlive/texmf-dist/fonts/enc/dvips/base/8r.enc" 1165713224 4850 80dc9bab7f31fb78a000ccfed0e27cab "" + "/usr/share/texlive/texmf-dist/fonts/map/fontname/texfonts.map" 1577235249 3524 cb3e574dea2d1052e39280babc910dc8 "" + "/usr/share/texlive/texmf-dist/fonts/tfm/jknappen/ec/ecrm1000.tfm" 1136768653 3584 adb004a0c8e7c46ee66cad73671f37b4 "" + 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sep=-1pt, fill=red, fit=(M46-3-1)] {}; +\end{tikzpicture} + + + +\end{document} diff --git a/buch/papers/punktgruppen/crystals.tex b/buch/papers/punktgruppen/crystals.tex index 21e29c9..42008e1 100644 --- a/buch/papers/punktgruppen/crystals.tex +++ b/buch/papers/punktgruppen/crystals.tex @@ -1,8 +1,7 @@ \section{Kristalle} -%einleitung sollte noch an das ende von der Symmetrie angepasst werden -Unter dem Begriff Kristall sollte sich jeder ein Bild machen können. -Wir werden uns aber nicht auf sein Äusseres fokussieren, sondern was ihn im Inneren ausmacht. -Die Innereien eines Kristalles sind glücklicherweise relativ einfach definiert. +Eine nicht allzu häufig gestellte Frage ist, wie ein Kristall definiert ist. +Um zu klären, was ein Kristall mit Symmetrien zu tun hat, ist jedoch genau diese Frage äusserst relevant. +Glücklicherweise ist das Innere eines Kristalles relativ einfach definiert. \begin{definition}[Kristall] Ein Kristall besteht aus Atomen, welche sich in einem Muster arrangieren, welches sich in drei Dimensionen periodisch wiederholt. \end{definition} @@ -22,7 +21,7 @@ Die eingezeichneten Vektoren \(\vec{a}_1\) und \(\vec{a}_2\) sind die kleinstmö Wird ein beliebiger grauer Gitterpunkt in \ref{fig:punktgruppen:lattice} gewählt und um eine ganzzahlige Linearkombination von \(\vec{a}_1\) und \(\vec{a}_2\) verschoben, endet er zwangsweise auf einem Gitterpunkt, wenn nicht wieder am selben Ort. Im dreidimensionalen Raum können alle Gitterpunkte mit derselben Idee und einem zusätzlichen Vektor \(\vec{c}\) also \[ - \vec{r} = n_1 \vec{a}_1 + n_2 \vec{a}_2 + n_3 \vec{a}_3 = \sum_i n_i \vec{a}_i + \vec{r} = n_1 \vec{a}_1 + n_2 \vec{a}_2 + n_3 \vec{a}_3 = \sum_i n_i \vec{a}_i \] erreicht werden sofern \(n_1,n_2,n_3 \in \mathbb{Z}\) sind. Sind die Vektoren \(\vec{a}_1\), \(\vec{a}_2\), \(\vec{a}_3\) gegeben, ist ein Kristallgitter eindeutig beschrieben, weswegen sie auch als Grundvektoren bekannt sind. @@ -30,20 +29,20 @@ Sind die Vektoren \(\vec{a}_1\), \(\vec{a}_2\), \(\vec{a}_3\) gegeben, ist ein \subsection{Translationssymmetrie} Da sich das ganze Kristallgitter wiederholt, wiederholen sich auch dessen Eigenschaften periodisch mit den Grundvektoren. Sollte man sich auf einem Gitterpunkt in einem Kristall aufhalten, ist es unmöglich zu wissen, auf welchem Gitterpunkt man sich befindet, da die Umgebungen aller Punkte identisch sind. -Mit anderen Worten: Jedes Kristallgitter $ G $ ist \emph{Translationssymmetrisch} in der Translation +Mit anderen Worten: Jedes Kristallgitter $ G $ ist \emph{translationssymmetrisch} in der Translation \[ - \vec{Q}_i(G) = G + \vec{a}_i -\] wobei der Vektor $\vec{a}_i$ ein Grundvektor sein muss. + \vec{Q}_i(G) = G + \vec{a}_i, +\] +wobei der Vektor $\vec{a}_i$ ein Grundvektor sein muss. Da die Translationssymmetrie beliebig oft mit allen Grundvektoren angewendet werden kann, können wir auch sagen, dass alle Verschiebungen um eine Linearkombination -der Vektoren $\vec{a}_1$ , $\vec{a}_2$ und $\vec{a}_3$ erlaubt sind oder kurz, um $\vec{r}$. -Verschiebungen um $\vec{r}$ bewirken demnach keine Veränderungen, -solange wir ein unendlich grosses Kristallgitter verschieben. +der Vektoren $\vec{a}_1$ , $\vec{a}_2$ und $\vec{a}_3$ erlaubt sind. +Dabei sollte erwähnt werden, dass eine Translationssymmetrie nur in unendlich grossen Kristallgittern besteht. \subsection{Limitierte Kristallsymmetrien} \label{txt:punktgruppen:Translationssymmetrie} Die Translationssymmetrie ist wohl keine grosse Überraschung, wenn man die Abbildung \ref{fig:punktgruppen:lattice} betrachtet. - Was nicht direkt ersichtlich ist, dass bei beliebigen Grundvektoren nicht beliebige Symmetrien erstellt werden können. - Die geforderte Translationssymmetrie eines Kristalles schränkt weitere Symmetrien deutlich ein. + Was nicht direkt ersichtlich ist, ist dass bei beliebigen Grundvektoren nicht beliebige Symmetrien erstellt werden können. + Dies weil die Translationssymmetrie eines Kristalles weitere Symmetrien deutlich einschränkt. \begin{figure} \centering @@ -62,7 +61,7 @@ solange wir ein unendlich grosses Kristallgitter verschieben. 90\(^{\circ}\), 120\(^{\circ}\) und 180\(^{\circ}\) - erlaubt. + m\"oglich. \end{satz} \begin{proof} @@ -78,10 +77,8 @@ solange wir ein unendlich grosses Kristallgitter verschieben. Für uns bedeutet dies lediglich, dass unser zweiter Punkt \(A'\) abgedreht wird. An der neuen Position \(B\) von \(A'\) muss also auch ein Punkt des Gitters sein, um die Rotationssymmetrie zu erfüllen. \item \(B\) ist unser Name für diesen neuen Punkt. - Da auch die Eigenschaften des Kristallgittes periodisch mit dem Gitter sein müssen, dürfen wir \(C_n\) auch auf \(A'\) anwenden. - Also wenden wir \(C_n\) invertiert\footnote{Eine Rotationssymmetrie muss auch in die inverse Richtung funktionieren. - Genauere Überlegungen hierzu werden dem Leser überlassen, da sich die Autoren nicht explizit mit dieser Frage Auseinander gesetzt haben.} - auch auf \(A'\) an. + Da auch die Eigenschaften des Kristallgitters periodisch mit dem Gitter sein müssen, dürfen wir \(C_n\) auch auf \(A'\) anwenden. + Also wenden wir \(C_n^{-1}\) auch auf \(A'\) an. Dies dreht \(A\) auf einen neuen Punkt. \item \(B'\) ist kein zufälliger Name für diesen neuen Punkt, denn wir wissen, dass zwischen allen Punkten eine Translationssymmetrie bestehen muss. Die Translationssymmetrie zwischen \(B\) und \(B'\) ist hier als \(\vec{Q}'\) bezeichnet. @@ -89,14 +86,14 @@ solange wir ein unendlich grosses Kristallgitter verschieben. Mit den gegebenen Punkten lassen sich geometrische Folgerungen ziehen. Wir beginnen, indem wir die Länge der Verschiebung \(|\vec{Q}| = Q\) setzen und \(|\vec{Q}'| = Q'\). Aus Abbildung \ref{fig:punktgruppen:rot-geometry} ist ersichtlich, dass \(Q' = Q + 2x\). - Da \(\vec{Q}\) eine Translation um ein Grundvektor ist , muss \(\vec{Q}'\) ein ganzes vielfaches von \(\vec{Q}\) sein. - Demnach auch die Längen + Da \(\vec{Q}\) eine Translation um ein Grundvektor ist , muss \(\vec{Q}'\) ein ganzes Vielfaches von \(\vec{Q}\) sein. + Demnach auch die Länge \[ - Q' = nQ = Q + 2x + Q' = nQ = Q + 2x . \] - Die Strecke \(x\) lässt sich auch mit hilfe der Trigonometrie und dem angenommenen Rotationswinkel \(\alpha\) ausdrücken: + Die Strecke \(x\) lässt sich auch mit Hilfe der Trigonometrie und dem angenommenen Rotationswinkel \(\alpha\) ausdrücken: \[ - nQ = Q + 2Q\sin(\alpha - \pi/2) + nQ = Q + 2Q\sin(\alpha - \pi/2) . \] Wir können durch \(Q\) dividieren um unabhängig von der Läge des Grundvektors zu werden, was auch Sinn macht, da eine Skalierung eines Kristalles seine Symmetrieeigenschaften nicht tangiert. @@ -126,10 +123,10 @@ ein. \subsection{Kristallklassen} -Vorgehend wurde gezeigt, dass in einem zweidimensionalen Kristallgitter nicht alle Symmetrien möglich sind. +Im vorausgegangenen Abschnitt wurde gezeigt, dass in einem zweidimensionalen Kristallgitter nicht alle Symmetrien möglich sind. Mit weiteren ähnlichen Überlegungen kann gezeigt werden, dass Kristalle im dreidimensionalen Raum nur auf genau 32 Arten rein punktsymmetrische Symmetriegruppen bilden können. Diese 32 möglichen Symmetriegruppen scheinen durchaus relevant zu sein, denn sie werden unter anderem als Kristallklassen bezeichnet. - Die 32 möglichen Kristallklassen sind auf Abbildung \ref{fig:punktgruppen:Kristallkassen} zu sehen. + Die 32 möglichen Kristallklassen sind auf Abbildung \ref{fig:punktgruppen:kristallklassen} zu sehen. Die Darstellung von dreidimensionalen Punktsymmetrien wurde mit der stereographischen Projektion ermöglicht (siehe Abbildung \ref{fig:punktgruppen:stereographic-projections}), wobei die gestrichelten Klassen aus Gründen der Überschaubarkeit nicht im Detail gezeichnet wurden. @@ -137,23 +134,34 @@ Vorgehend wurde gezeigt, dass in einem zweidimensionalen Kristallgitter nicht al \centering \includegraphics[]{papers/punktgruppen/figures/projections} \caption{Kristallklassen mit zugehörigem Schönflies-Symbol} - \label{fig:punktgruppen:Kristallkassen} + \label{fig:punktgruppen:kristallklassen} \end{figure} -\subsubsection{Schönflies-Symbilok} +\subsubsection{Schönflies-Symbolik} -Jede der 32 Kristallklassen auf der Abbildung \ref{fig:punktgruppen:Kristallkassen} ist mit ihrem zugehörigen Schöönflies-Symbol bezeichnet. +Jede der 32 Kristallklassen auf der Abbildung \ref{fig:punktgruppen:kristallklassen} ist mit ihrem zugehörigen Schönflies-Symbol bezeichnet. Die Schönflies-Symbolik stammt von dem Mathematiker Arthur Moritz Schönflies, welcher sich unter anderem mit der Klasifizierung der Punktgruppen auseinandergesetzt hat. - Er hat Untergruppen gebildet, welche als Grossbuchstaben in Abbildung \ref{fig:punktgruppen:Kristallkassen} zu sehen sind. - Da nicht alle Symmetriegruppen in Kristallen möglich sind, werden nicht alle Untergruppen von Schönflies verwendet. - Es ist nur die Drehgruppe \(C\), Diedergruppe \(D\), Drehspiegelgruppe \(S\), Tetraedergruppe \(T\) und die Oktaedergruppe \(O\). - Für die eindeutige zuweisung in eine Kristallklasse werden noch identifizierende Merkmale als Subskript notiert. - Bei der Untergruppe \(C\) werden beispielsweise die möglichen Rotationssymmetrien gezeigt. - Dank Abschintt \ref{txt:punktgruppen:Translationssymmetrie} wissen wir, wieso auf \(C\) nur ganz bestimmte Subskripte folgen, weil das Subskript \(n\) von \(C_n\) zeigt, dass es sich um eine \(n\)-fache Rotationssymmetrie handelt. - Daher darf \(C_5\) auf der Abbildung \ref{fig:punktgruppen:Kristallkassen} nicht vorkommen darf, da \(360^\circ/5 = 72^\circ\) was nach Abschnitt \ref{txt:punktgruppen:Translationssymmetrie} in einem Kristall keine mögliche Rotationssymmetrie ist. - Sind im Subskript Buchstaben, definieren diese weitere Symmetrieeigenschaften der Klasse. - Wie zum Beispiel ein Inversionszentrum\footnote{Ein Objekt mit Inversionszentrum ist Punktsymmetrisch im Inversionszentrum.} \(i\) oder eine horizontale\footnote{Als Orientierungspunkt wird die Symmetrieachse höchster Ordnung (\(n\)) als vertikal definiert} Spiegelachse \(h\). - Zu beachten ist jedoch, dass manche Symmetriegruppen mit mehreren Schönflies-Symbolen beschieben werden können. + Er hat Untergruppen gebildet, welche als Grossbuchstaben in Abbildung \ref{fig:punktgruppen:kristallklassen} zu sehen sind. + \begin{itemize} + \item In Kristallen ist nur die Drehgruppe \(C\), Diedergruppe \(D\), Drehspiegelgruppe \(S\), Tetraedergruppe \(T\) und die Oktaedergruppe \(O\) zu finden. + Es gäbe auch die Ikosaedergruppe \(I\) und die Kugelgruppe \(K\), diese sind aber nicht kompatibel mit der Translationssymmetrie eines Kristalles und daher in der Kristallographie nicht relevant. + \item Dank Abschintt \ref{txt:punktgruppen:Translationssymmetrie} wissen wir, wieso in Abbildung \ref{fig:punktgruppen:kristallklassen} auf \(C\) nur ganz bestimmte Subskripte folgen. + Ist im Subskript eine Zahl \(n\) zu finden, steht dies für eine \(n\)-fache Symmetrie. + Daher darf \(C_5\) auf der Abbildung \ref{fig:punktgruppen:kristallklassen} nicht vorkommen, da \(360^\circ/5 = 72^\circ\) was nach Abschnitt \ref{txt:punktgruppen:Translationssymmetrie} keine mögliche Rotationssymmetrie eines Kristalles ist. + \item Sind im Subskript Buchstaben, definieren diese weitere Symmetrieeigenschaften der Klasse. + Für die folgenden Betrachtungen müssen wir uns Abbildung \ref{fig:punktgruppen:kristallklassen} genauer ansehen. + Dabei ist mit horizontal flach auf dem Papier gemeint. + \begin{itemize} + \item[\(h\)] bezeichnet eine horizontale Spiegelebene und + \item[\(v\)] eine Symmetrieebene, was eine Spiegelebene ist, die sich mit der Symmetrie mitdreht. + Zum Beispiel hat \(C_{3v}\) eine vertikale Spiegelebene, die durch die 3-fache Drehsymmetrie als 3 Spiegelebenen erscheinen. + \item[\(s\)] ist ein spezielles Subskript um die beiden Symmetriegruppen \(C_{1v}\) und \(C_{1h}\) zu beschreiben, weil \(C_{1v} = C_{1h}\). + \item[\(d\)] symbolisiert eine diagonale Symmetrieebene. + Es wird ersichtlich wie diagonal gemeint ist, wenn man \(D_2\) zu \(D_{2d}\) vergleicht. + \item[\(i\)] steht für ein Inversionszentrum. Hat eine Symmetriegruppe ein Inversionszentrum, bedeutet dies dass sie im Ursprung punktsymmetrisch ist. + \end{itemize} + \end{itemize} +Zu beachten ist jedoch, dass manche Symmetriegruppen mit mehreren Schönflies-Symbolen beschieben werden können. \(C_{3i}\) beschreibt genau das selbe wie \(S_6\), da eine dreifache Rotationssymmetrie mit einem Inversionszentrum einer sechsfachen Drehspiegelsymmetrie entspricht. diff --git a/buch/papers/punktgruppen/figures/projections.pdf b/buch/papers/punktgruppen/figures/projections.pdf Binary files differindex bc04313..202fc8d 100644 --- a/buch/papers/punktgruppen/figures/projections.pdf +++ b/buch/papers/punktgruppen/figures/projections.pdf diff --git a/buch/papers/punktgruppen/intro.tex b/buch/papers/punktgruppen/intro.tex index b6a72b5..1293234 100644 --- a/buch/papers/punktgruppen/intro.tex +++ b/buch/papers/punktgruppen/intro.tex @@ -1,6 +1,6 @@ \section{Einleitung} Es gibt viele Möglichkeiten sich in Kristallen zu verlieren. -Auch wen man nur die mathematischen Betrachtungsweisen berücksichtigt, +Auch wenn man nur die mathematischen Betrachtungsweisen berücksichtigt, hat man noch viel zu viele Optionen sich mit Kristallen zu beschäftigen. In diesem Kapitel wird daher der Fokus ``nur'' auf die Symmetrie gelegt. Zu Beginn werden wir zeigen was eine Symmetrie ausmacht und @@ -15,9 +15,9 @@ und sich kategorisieren lassen. Kategorien sind nicht nur für einen besseren Überblick nützlich, sondern kann man aus ihnen auch auf Physikalische Eigenschaften schliessen. Als spannendes Beispiel: Die Piezoelektrizität. -Die Piezoelektrizität ist vielleicht noch nicht jedem bekannt, -sie versteckt sich aber in diversen Altagsgegenständen -zum Beispiel sorgen sie in den meisten Feuerzeugen für die Zündung. +Piezoelektrizität ist kein weit verbreiteter Begriff, +jedoch beschreibt er ein Effekt, ohne welchen diverse Altagsgegenständen nicht besonders nützlich wären. +Wie zum Beispiel sorgt er in den allermeisten Feuerzeugen für die Zündung. Hiermit ist hoffentlich ein Funken Interesse geweckt um sich mit dem scheinbar trivialen Thema der Symmetrie auseinander zu setzten. diff --git a/buch/papers/punktgruppen/main.tex b/buch/papers/punktgruppen/main.tex index ea19421..556fc2b 100644 --- a/buch/papers/punktgruppen/main.tex +++ b/buch/papers/punktgruppen/main.tex @@ -19,6 +19,7 @@ \nocite{punktgruppen:sands-crystal} \nocite{punktgruppen:lang-elt2} \nocite{punktgruppen:ouchem} +\nocite{punktgruppen:restriction} \printbibliography[heading=subbibliography] \end{refsection} diff --git a/buch/papers/punktgruppen/piezo.tex b/buch/papers/punktgruppen/piezo.tex index 6defcdc..6ed7ee9 100644 --- a/buch/papers/punktgruppen/piezo.tex +++ b/buch/papers/punktgruppen/piezo.tex @@ -1,6 +1,6 @@ \section{Piezoelektrizität} -Die Piezoelektrizität ist per Definition spannend. -Sie beschreibt die Eigenschaft, dass gewisse Kristalle eine elektrische Spannung erzeugen, wenn machanischer Druck auf sie ausgeübt wird. +%% TODO: improve this paragraph +Die Piezoelektrizität ist die spannende Eigenschaft, dass gewisse Kristalle eine elektrische Spannung erzeugen, wenn mechanischer Druck auf sie ausgeübt wird. \begin{figure} \centering @@ -10,11 +10,11 @@ Sie beschreibt die Eigenschaft, dass gewisse Kristalle eine elektrische Spannung \end{figure} \subsection{Polarisierung} -Piezoelektrizität basiert darauf, dass zwischen den Oberflächen des Kristalles ein Ladungsungleichgewicht entsteht siehe Abbildung\ref{fig:punktgruppen:basicPiezo}. +Piezoelektrizität basiert darauf, dass zwischen den Oberflächen des Kristalles ein Ladungsungleichgewicht entsteht (siehe Abbildung\ref{fig:punktgruppen:basicPiezo}). Dieses Ungleichgewicht resultiert, -weil durch den mechanischen Druck auf der einen Oberfläche des Kristalles positiv Ione näher an die Oberfläche gelangen, -wärend auf der gegenüberliegenden Oberfläche sich mehr negative Ionen Sammeln. -Das sich die atomare Struktur eines Kristalles unter Druck genau so verformt ist nicht bei jedem Kristall gegeben. +weil durch den mechanischen Druck auf der einen Oberfläche des Kristalles positive Ionen näher an die Oberfläche gelangen, +wärend auf der gegenüberliegenden Seite dasselbe mit negativen Ionen passiert. +Es besitzt jedoch nicht jeder Kristall eine atomare Struktur welche sich unter Druck genau so verformt. Der Aufbau und somit auch die Symmetrie des Kristalles sind daher relevant für die Entstehung dieses Effektes. \begin{figure} @@ -35,49 +35,47 @@ Der Aufbau und somit auch die Symmetrie des Kristalles sind daher relevant für \end{figure} \subsection{Atomarer Aufbau} -Die Polarisation resultiert über eine gesamte Oberfläche eines Kristalles, entscheidend ist aber der atomare Aufbau. +Die Polarisation entsteht an der Oberfläche eines Kristalles, die Erklärung dazu finden wir jedoch im atomaren Aufbau. Wir wollen dazu die verschiedenen Kristallstrukturen auf Abbildung \ref{fig:punktgruppen:atomPiezo} diskutieren. -In Abbildung \ref{fig:punktgruppen:atomPiezo} gilt für alle Strukturen, dass rote Kreise Positive Ionen und blaue negative Ionen repräsentieren. -%liste oder anderes format?.. +In Abbildung \ref{fig:punktgruppen:atomPiezo} gilt für alle Strukturen, dass rote Kreise positive Ionen und blaue negative Ionen repräsentieren. Struktur \subref{fig:punktgruppen:atoms-piezo} zeigt ein piezoelektrisches Material in Ruhe. Struktur \subref{fig:punktgruppen:atoms-piezo-fv} ist dasselbe Kristallgitter, jedoch wird es senkrecht belastet. -Eingezeichnet ist auch das elektrische Feld, welches entsteht, weil mitlleren Ladungsträger weiter auseinander gerdrückt werden. -Als hilfe zur Vorstellung kann man \subref{fig:punktgruppen:atoms-piezo-fv} zwischen zwei leitende Platten setzen, so wird ersichtlich, +Eingezeichnet ist auch das elektrische Feld, welches entsteht, weil die Ladungsträger ganz links und rechts weiter auseinander gedrückt werden. +Als Hilfe zur Vorstellung kann man \subref{fig:punktgruppen:atoms-piezo-fv} zwischen zwei leitende Platten setzen, so wird ersichtlich, dass mit wachsendem Druck eine negative Ladung an die rechte Platte gedrückt wird, während sich die positiven Ionen weiter entfernen. -\subref{fig:punktgruppen:atoms-grid} ist nicht piezoelektrisch. -Dies wird ersichtlich, wenn man \subref{fig:punktgruppen:atoms-grid} unterdruck setzt und sich die Struktur zu \subref{fig:punktgruppen:atoms-grid-f} verformt. +\par +Die Struktur \subref{fig:punktgruppen:atoms-grid} ist nicht piezoelektrisch. +Dies wird ersichtlich, wenn man \subref{fig:punktgruppen:atoms-grid} unter Druck setzt und sich die Struktur zu \subref{fig:punktgruppen:atoms-grid-f} verformt. Setzt man \subref{fig:punktgruppen:atoms-grid-f} gedanklich auch zwischen zwei leitende Platten, -scheint es als würden rechts mehr Positive Ionen in die Platte gedrückt werden und links umgekehrt. -Dies ist aber nicht mehr der Fall, wenn die Struktur sich nach oben und unten periodisch wiederholt. +scheint es als würden rechts mehr positive Ionen in die Platte gedrückt werden und links umgekehrt. +Dies ist aber nicht mehr der Fall, wenn sich die Struktur nach oben und unten periodisch wiederholt. +\par Struktur \subref{fig:punktgruppen:atoms-piezo-fh} zeigt \subref{fig:punktgruppen:atoms-piezo} in unter horizontaler Belastung. Was zwischen \subref{fig:punktgruppen:atoms-piezo-fv} und \subref{fig:punktgruppen:atoms-piezo-fh} zu beobachten ist, -ist dass das entstandene Ladungsdifferenz orthogonal zu der angelegten Kraft entsteht, +ist, dass die entstandene Ladungsdifferenz orthogonal zu der angelegten Kraft entsteht, im Gegensatz zu \subref{fig:punktgruppen:atoms-piezo-fh}. -Daraus kann man schlissen, dass \subref{fig:punktgruppen:atoms-piezo} keine Rotationssymmetrie von $90^\circ$ besitzen kann, -weil die Eigenschaften ändern bei einer $90^\circ$ Drehung. -Das Fehlen dieser Rotationssymmetrie kann mit betrachten von \subref{fig:punktgruppen:atoms-piezo} bestätigt werden. +Daraus kann man schliessen, dass \subref{fig:punktgruppen:atoms-piezo} keine Rotationssymmetrie von \(90^\circ\) besitzen kann, +weil die Eigenschaften der Struktur sich bei einer \(90^\circ\) Drehung ändern. +Das Fehlen dieser Rotationssymmetrie bestätigt sich auch wenn \subref{fig:punktgruppen:atoms-piezo} als Hexagon betrachtet wird. \subsection{Punktsymmetrie} -Piezoelektrische Kristalle können nicht Punktsymmetrisch sein. +Piezoelektrische Kristalle können nicht punktsymmetrisch sein. Kristallgitter, bei welchen eine Punktspiegelung eine symmetrische Operation ist, können keine piezoelektrische Kristalle bilden. -Auf Abbildung \ref{fig:punktgruppen:atomPiezo} ist bewusst \subref{fig:punktgruppen:atoms-piezo} ein nicht Punktsymmetrischer Kristall -mit einem Punktsymmetrischen \subref{fig:punktgruppen:atoms-grid}verglichen worden. -Als vereinfachte Erklärung kann mann sich wieder das Bild vor augen führen, eines Kristalles, +Auf Abbildung \ref{fig:punktgruppen:atomPiezo} ist bewusst \subref{fig:punktgruppen:atoms-piezo} ein nicht punktsymmetrischer Kristall +mit einem punktsymmetrischen \subref{fig:punktgruppen:atoms-grid} verglichen worden. +Als vereinfachte Erklärung kann man sich wieder das Bild eines Kristalles wie \subref{fig:punktgruppen:atoms-piezo} vor Augen führen, welcher unter Druck auf der einen Seite negative und der anderen Seite positive Ionen an seine Oberfläche verdrängt. -Spiegelt man nun den Kristall um den Gitterpunkt in der mitte des Kristalles, so würden die negativen Ionen auf den Positiven auf der anderen seite landen, +Spiegelt man nun den Kristall um den Gitterpunkt in der Mitte des Kristalles, so würden die negativen Ionen auf den positiven auf der anderen Seite landen, was der Definition einer Symmetrie deutlich widerspricht. \subsection{Vom Kristall zum Feuer} -Piezoelektrizität hat durchaus nutzen im Alltag. +Piezoelektrizität hat durchaus Nutzen im Alltag. Feuerzeuge welche nicht auf dem Prinzip beruhen einen Zündstein abzuschleifen, sonder ohne Verschleiss auf Knopfdruck einen Zündfunken erzeugen, basieren auf dem Prinzip der Piezoelektrizität. -Drückt der Nutzende auf den Zündknopf spannt sich eine Feder bis zu einer Konfigurierten Spannung. -Wird vom Nutzenden weiter gedrückt entspannt sich die Feder schlagartig und beschleunigt mit der gespeicherten Energie ein Hammer, +Drückt der Nutzende auf den Zündknopf, spannt sich eine Feder bis zu einer konfigurierten Spannung. +Wird vom Nutzenden fester zugedrückt entspannt sich die Feder schlagartig und beschleunigt mit der gespeicherten Energie ein Hammer, welcher auf das Piezoelement aufschlägt. -Der augenblicklich hohe Druck sorgt an den Piezokontakten für eine eben so Kurze aber hohe elekrische Spannung. +Der augenblicklich hohe Druck sorgt an den Piezokontakten für eine eben so kurze aber hohe elektrische Spannung. Die Spannung reicht aus, um eine Funkenstrecke zu überwinden und so eine entflammbares Gas zu entzünden. -Sollten Sie also eines Tages in die Situation geraten, in welcher Sie zwei verschiedene Kristalle vor sich haben -und ein piezoelektrisches Feuerzeug bauen müssen, -wobei Sie aber wissen, dass einer eine Punktsymmetrie aufweist, -versuche sie es mit dem anderen. +Sollte der Leser eines Tages in die Situation geraten, in welcher er zwei verschiedene Kristalle vor sich hat und ein piezoelektrisches Feuerzeug bauen musst, wobei bekannt ist, dass der eine eine Punktsymmetrie aufweist, empfiehlt es sich, sich am anderen zu versuchen. diff --git a/buch/papers/punktgruppen/references.bib b/buch/papers/punktgruppen/references.bib index a29640c..05c803f 100644 --- a/buch/papers/punktgruppen/references.bib +++ b/buch/papers/punktgruppen/references.bib @@ -26,7 +26,7 @@ @book{punktgruppen:lang-elt2, title = {Elektrotechnik 2}, - author = {Hans-Dieter Lang}, + author = {Prof. Hans-Dieter Lang Ph.D}, publisher = {Fachhochschule Ostschweiz Rapperswil}, year = {2020}, month = {2}, @@ -35,10 +35,20 @@ @online{punktgruppen:ouchem, title = {Symmetry in Crystallography}, - author = {Dept. of Chemistry \& Biochemistry, Chemical Crystallography Laboratory, University of Oklahoma}, + author = {Dept. of Chemistry \& Biochemistry{,} Chemical Crystallography Laboratory{,} University of Oklahoma}, year = {2019}, month = {11}, day = {17}, url = {http://archive.today/2021.07.22-083802/http://xrayweb.chem.ou.edu/notes/symmetry.html}, urldate = {2021-07-22}, } + +@online{punktgruppen:restriction, + title = {Structure of Materials: Allowed Rotational Symmetry in Crystals}, + author = {Prof. Silvija Gradecak-Garaj{,} Massachusetts Institute of Technology (MIT)}, + year = {2020}, + month = {4}, + day = {9}, + url = {https://www.youtube.com/watch?v=Ia2eHF1ZKoI}, + urldate = {2021-07-30}, +} diff --git a/buch/papers/punktgruppen/symmetry.tex b/buch/papers/punktgruppen/symmetry.tex index 0bb4aec..2067663 100644 --- a/buch/papers/punktgruppen/symmetry.tex +++ b/buch/papers/punktgruppen/symmetry.tex @@ -22,27 +22,28 @@ Wie wir jedoch später sehen werden, ist das Konzept der Symmetrie eigentlich vi In Abbildung \ref{fig:punktgruppen:geometry-example} haben wir einige Formen, die offensichtlich symmetrisch sind. Zum Beispiel hat das Quadrat eine Gerade, an deren es gespiegelt werden kann, ohne sein Aussehen zu verändern. Regelmässige Polygone mit \(n\) Seiten sind auch gute Beispiele, um eine diskrete Rotationssymmetrie zu veranschaulichen, was bedeutet, dass eine Drehung um einen Punkt um einen bestimmten Winkel \(360^\circ/n\) die Figur unverändert lässt. -Das letzte Beispiel auf der rechten Seite ist eine unendliche Rotationssymmetrie. Sie wird so genannt, weil es unendlich viele Werte für \(\alpha \in \mathbb{R}\) gibt, die die Form unverändert lassen. +Das letzte Beispiel auf der rechten Seite ist eine unendliche Rotationssymmetrie. Sie wird so genannt, weil es unendlich viele Werte für den Drehwinkel \(\alpha \in \mathbb{R}\) gibt, die die Form unverändert lassen. Ein Objekt kann mehr als nur eine Symmetrie aufweisen. -Als Beispiel, kann das Quadrat in Abbildung \ref{fig:punktgruppen:geometry-example} nicht nur um \(\sigma\) sondern auch Diagonal gespiegelt werden oder um \(90^\circ\) gedreht werden. +Als Beispiel, kann das Quadrat in Abbildung \ref{fig:punktgruppen:geometry-example} nicht nur um \(\sigma\) sondern auch diagonal gespiegelt werden oder um \(90^\circ\) gedreht werden. Fasst man die möglichen Symmetrien zusammen, entsteht eine Symmetriegruppe. \begin{definition}[Symmetriegruppe] - \(g\) und \(h\) sein umkehrbare Operationen, die ein mathematisches Objekt unverändert lassen. + Seien \(g\) und \(h\) umkehrbare Operationen, sogenannte Symmetrieoperationen, die ein mathematisches Objekt unverändert lassen. Die Komposition \(h\circ g\) definieren wir als die Anwendung der Operationen nacheinander. - Alle möglichen Operationen bilden unter Komposition eine Gruppe, die Symmetriegruppe genannt wird. + Alle möglichen Symmetrieoperationen bilden unter Komposition eine Gruppe, die Symmetriegruppe genannt wird. \end{definition} Eine Gruppe benötigt ausserdem auch zwingend ein neutrales Element, welches wir mit \(\mathds{1}\) bezeichnen. Die Anwendung der neutralen Operation ist gleichbedeutend damit, alles unverändert zu lassen. -\(\mathds{1}\) ist auch äquivalent dazu, eine Operation anzuwenden und sie dann rückgängig zu machen (ihre Inverse anzuwenden). - Die Definition der Symmetriegruppe ist mit der Kompositionsoperation gegeben, es wird aber auch oft als Multiplikation geschrieben. +Weiterhin muss in einer Gruppe für jede Operation \(g\) auch eine inverse Operation \(g^{-1}\) vorkommen, die intuitiv rückgängig macht, was \(g\) getan hat. % intuitiv weglassen oder anstelle sinnbildlich +Somit ist \(\mathds{1}\) auch äquivalent dazu, eine Operation und dann ihre Inverse anzuwenden. + Die Definition der Symmetriegruppe ist mit der Kompositionsoperation gegeben, sie wird aber auch oft als Multiplikation geschrieben. Das liegt daran, dass in manchen Fällen die Zusammensetzung algebraisch durch eine Multiplikation berechnet wird. Die Verwendung einer multiplikativen Schreibweise ermöglicht es, einige Ausdrücke kompakter zu schreiben, z.B. durch Verwendung von Potenzen \(r^n = r\circ r \circ \cdots r\circ r\) für eine wiederholte Komposition. \begin{definition}[Zyklische Untergruppe, Erzeuger] - \(g\) sei ein Element einer Symmetriegruppe \(G\). + Sei \(g\) ein Element einer Symmetriegruppe \(G\). Alle möglichen Kompositionen von \(g\) und \(g^{-1}\) bilden eine sogenannte zyklische Untergruppe von \(G\), wobei \(g\) Erzeuger der Untergruppe genannt wird. Die von \(g\) erzeugte Untergruppe \(\langle g \rangle = \left\{ g^k : k \in \mathbb{Z} \right\}\) wird mit spitzen Klammern bezeichnet. \end{definition} @@ -51,7 +52,7 @@ durch Verwendung von Potenzen \(r^n = r\circ r \circ \cdots r\circ r\) für eine Das bedeutet, dass \(G\) die Elemente \(a, aa, aaa, \ldots\) sowie \(a^{-1}, a^{-1}a^{-1}, \ldots\) und ein neutrales Element \(\mathds{1} = aa^{-1}\) enthält. \end{beispiel} \begin{beispiel} - Als anschaulicheres Beispiel, können wir eine Zyklische Untergruppe des \(n\)-Gon formalisieren. + Als anschaulicheres Beispiel, können wir eine zyklische Untergruppe des \(n\)-Gon formalisieren. Wir bezeichnen mit \(r\) eine Drehung im Gegenuhrzeigersinn von \(360^\circ/n\) um einen Punkt. Diese Definition reicht aus, um die gesamte Symmetriegruppe \[ @@ -63,15 +64,16 @@ durch Verwendung von Potenzen \(r^n = r\circ r \circ \cdots r\circ r\) für eine In ähnlicher Weise, aber weniger interessant enthält die Reflexionssymmetriegruppe \(\langle\sigma\rangle\) nur \(\left\{\mathds{1}, \sigma\right\}\), weil \(\sigma^2 = \mathds{1}\). \end{beispiel} -Wenn wir diese Idee nun erweitern, können wir mit einem Erzeugendensystemen +Wenn wir diese Idee nun erweitern, können wir mit einem Erzeugendensystem komplexere Strukturen aufbauen. -\begin{definition}[Erzeugendensysteme] +%@Naoki Are you ok with my grammar fixes I'm not 101% shore how to use the word Erzeugendensystem? +\begin{definition}[Erzeugendensystem] Jede disktrete Gruppe kann durch eines oder mehrere ihrer Elemente generiert werden. Wir lassen \(g_1, g_2, \ldots, g_n\) erzeugenden Elemente einer Symmetriegruppe sein. - Da es mehrere Erzeuger gibt, müssen auch die sogenannte Definitionsgleichungen gegeben werden, die die Multiplikationstabelle vollständig definieren. + Da es mehrere Erzeuger gibt, müssen auch die sogenannten Definitionsgleichungen gegeben werden, die die Multiplikationstabelle vollständig definieren. Die Gleichungen sind ebenfalls in den Klammern angegeben. - Die erzeugende Elementen zusammen mit der Definitionsgleichungen bauen ein Erzeugendensysteme. + Die erzeugenden Elementen bauen zusammen mit den Definitionsgleichungen ein Erzeugendensystem. \end{definition} \begin{beispiel} Wir werden nun alle Symmetrien eines \(n\)-Gons beschreiben, was bedeutet, dass wir die Operationen \(r\) und \(\sigma\) kombinieren. @@ -98,7 +100,7 @@ Dies ist jedoch keine Voraussetzung für eine Symmetrie, da es Symmetrien gibt, \subsection{Algebraische Symmetrien} Wir haben nun unseren Operationen Symbole gegeben, mit denen es tatsächlich möglich ist, Gleichungen zu schreiben. -Die anschliesende Frage ist dann, ob wir bereits mathematische Objekte haben, mit denen wir Gleichungen schreiben, die sich auf die gleiche Weise verhalten. +Die anschliessende Frage ist dann, ob wir bereits mathematische Objekte haben, mit denen wir Gleichungen schreiben, die sich auf die gleiche Weise verhalten. Die Antwort lautet natürlich ja. Um es formaler zu beschreiben, werden wir einige Begriffe einführen. \begin{definition}[Gruppenhomomorphismus] diff --git a/buch/papers/punktgruppen/tikz/projections.tex b/buch/papers/punktgruppen/tikz/projections.tex index 64ab468..e8a4a2e 100644 --- a/buch/papers/punktgruppen/tikz/projections.tex +++ b/buch/papers/punktgruppen/tikz/projections.tex @@ -44,7 +44,7 @@ \node[classcirc] (C2h) {} node[classlabel] {\(C_{2h}\)}; & \node[classcirc] (D2) {} node[classlabel] {\(D_{2}\)}; \\ - \node[classcirc] (D3d) {} node[classlabel] {\(D_{3d}\)}; & + \node[classcirc] (D3d) {} node[classlabel] {\(C_{3v}\)}; & \node[classcirc] (C2v) {} node[classlabel] {\(C_{2v}\)}; & \node[classcirc] (D2h) {} node[classlabel] {\(D_{2h}\)}; & \node[classcirc] (D3) {} node[classlabel] {\(D_{3}\)}; & diff --git a/buch/papers/reedsolomon/Makefile b/buch/papers/reedsolomon/Makefile index 9c96e88..25fd98b 100644 --- a/buch/papers/reedsolomon/Makefile +++ b/buch/papers/reedsolomon/Makefile @@ -4,6 +4,52 @@ # (c) 2020 Prof Dr Andreas Mueller # -images: - @echo "no images to be created in reedsolomon" +SOURCES := \ + anwendungen.tex \ + codebsp.tex \ + decmitfehler.tex \ + decohnefehler.tex \ + dtf.tex \ + einleitung.tex \ + endlichekoerper.tex \ + hilfstabellen.tex \ + idee.tex \ + main.tex \ + packages.tex \ + rekonstruktion.tex \ + restetabelle1.tex \ + restetabelle2.tex \ + standalone.tex \ + zusammenfassung.tex + +TIKZFIGURES := \ + tikz/polynom2.tex \ + tikz/plotfft.tex + +FIGURES := $(patsubst tikz/%.tex, figures/%.pdf, $(TIKZFIGURES)) + + +all: images standalone + + +.PHONY: images +images: $(FIGURES) + +figures/%.pdf: tikz/%.tex + mkdir -p figures + pdflatex --output-directory=figures $< + +.PHONY: standalone +standalone: standalone.tex $(SOURCES) $(FIGURES) + mkdir -p standalone + cd ../..; \ + pdflatex \ + --halt-on-error \ + --shell-escape \ + --output-directory=papers/reedsolomon/standalone \ + papers/reedsolomon/standalone.tex; + cd standalone; \ + bibtex standalone; \ + makeindex standalone; + diff --git a/buch/papers/reedsolomon/anwendungen.tex b/buch/papers/reedsolomon/anwendungen.tex index c03b1a4..b9b1d69 100644 --- a/buch/papers/reedsolomon/anwendungen.tex +++ b/buch/papers/reedsolomon/anwendungen.tex @@ -7,21 +7,20 @@ \label{reedsolomon:section:anwendung}} \rhead{Anwendungen} -In den vorherigen Abschnitten haben wir betrachtet, wie Reed-Solomon-Codes in der Theorie Funktionieren. +In den vorherigen Abschnitten haben wir betrachtet, wie Reed-Solomon-Codes in der Theorie funktionieren. In diesem Abschnitt werden wir einige Anwendungen vorstellen, bei denen ein Reed-Solomon-Code zum Einsatz kommt. -Dabei teilen all diese Anwendungen das gleiche Problem: Die Daten können nur durch einen (höchst Wahrscheinlichen) fehlerbehafteten Kanal empfangen werden. Es gibt keine andere Methode an diese Daten zu kommen als über diesen Kanal. +Dabei teilen all diese Anwendungen das gleiche Problem: Die Daten können nur durch einen (höchst Wahrscheinlichen) fehlerbehafteten Kanal empfangen werden. Es gibt keine andere Methode, an diese Daten zu kommen, als über diesen Kanal. - -In der Netzwerktechnik zum Beispiel ist es üblich, dass bei Paketverluste oder beschädigt empfangene Datenpakete diese einfach noch einmal inert wenigen Millisekunden angefordert werden können. +In der Netzwerktechnik zum Beispiel ist es üblich, dass bei Paketverluste oder beschädigt empfangene Datenpaketen diese einfach noch einmal innert wenigen Millisekunden angefordert werden können. In der Raumfahrt ist dies nicht möglich, da aufgrund der beschränkten Speichermöglichkeit die gesammelten Daten so rasch wie möglich zur Erde gesendet werden. Diese Daten wiederum brauchen aufgrund der grossen Distanz Stunden bis die Daten beim Empfänger ankommen. Fehlerhafte Daten kann also auf Grund der Zeitverzögerung nicht mehr angefordert werden. -Bei CDs oder DVDs gibt es zwar kein Zeitliches Problem, jedoch erschweren Kratzer, Verschmutzungen oder Produktionsfehler das Lesen einer solchen Disk. +Bei CDs oder DVDs gibt es zwar kein zeitliches Problem, jedoch erschweren Kratzer, Verschmutzungen oder Produktionsfehler das Lesen einer solchen Disk. Da vor allem Produktionsfehler und Kratzer irreversibel sind und die Disk nicht nach jedem Kratzer ersetzt werden muss, so wird die korrekte Ausgabe der gespeicherten Information durch die Fehlerkorrektur sichergestellt. -Ein ähnlicher Ansatz verfolgen QR-Codes, wobei die Information auch dann noch gelesen werden kann wenn der Code nicht mehr vollständig vorhanden ist. +Einen ähnlichen Ansatz verfolgen QR-Codes, wobei die Information auch dann noch gelesen werden kann wenn der Code nicht mehr vollständig vorhanden ist. %Wie man sieht, eignen sich Reed-Solomon-Codes vor allem für Anwendungen, bei der die Informationen nicht auf einen Anderen Weg beschafft werden kann. % @@ -33,7 +32,6 @@ Ein ähnlicher Ansatz verfolgen QR-Codes, wobei die Information auch dann noch g % da aufgrund der grossen Distanz Stunden vergehen können bis gesendete Daten auf der Erde empfangen werden kann. % - Obwohl alle diese Codes nach dem gleichen Prinzip arbeiten gibt es starke Unterschiede in deren Funktionsweise. Dies kommt vor allem daher, da die Codes nur Ressourcen zur Verfügung haben, die von der Hardware bereitstellt wird, auf denen die Codes implementiert wurden. Diese Codes bedienen sich daher verschiedener Tricks und Optimierungen um möglichst effizient zu arbeiten. @@ -75,8 +73,14 @@ Obwohl Reed-Solomon-Codes bereits in den 1960er entwickelt wurden fanden sie ers Codiert. Der Nachrichtenblock hat somit eine Länge von $255$ Zahlen, wovon $233$ als Nutzlast zur Verfügung stehen. Damit ist es möglich bis zu $11$ Fehler im Nachrichtenblock zu korrigieren. -Der Codierte Nachrichtenblock wird in kleinere Blöcke aufgeteilt, mit einem Faltungscode erneut Codiert und anschliessend gesendet. Ein Faltungscode ist wie ein Reed-Solomon-Code in der Lage Fehler zu korrigieren, Funktioniert aber nach einem ganz anderen Prinzip. -Durch diese doppelte Codierung wird eine äusserst hohe Übertragungssicherheit garantiert. +Der Codierte Nachrichtenblock wird in kleinere Blöcke aufgeteilt, mit einem Faltungscode erneut Codiert und anschliessend gesendet. +Ein Faltungscode ist wie ein Reed-Solomon-Code in der Lage Fehler zu korrigieren, +Codiert seine Information aber auf eine andere weise. Aus jedem unterteilten Block wird vor dem Versenden ein Paritätsbit erzeugt und dem Block angehängt. Anhand diesem Paritätsbit überprüft der Empfänger, ob bei der Übertragung der Block beschädigt wurde. Ist dies der Fall, wird der Block bei der Decodierung nicht beachtet. Diese so entstandenen ``Lücken'' im Datenstrom werden wiederum vom Reed-Solomon-Code korrigiert. Dieses Zusammenspiel beider Codes garantiert so eine hohe Robustheit gegenüber Übertragungsfeher. + +% +% Funktioniert aber nach einem ganz anderen Prinzip. +% +%Durch diese doppelte Codierung wird eine äusserst hohe Übertragungssicherheit garantiert. % %Dabei steht die Zahl 255 für grösse des Nachrichtenblocks, der die Anzahl 233 % @@ -107,13 +111,18 @@ Die Digital Video Disc funktioniert nach dem selben Konzept mit grösseren Codeb \begin{figure} \centering - \includegraphics[width=0.5\textwidth]{papers/reedsolomon/images/Compact_Disc} - \caption{CDs kamen 1982 auf den Markt. Sie funktioniert durch das ``einbrennen'' von Punkten und Strichen, die die Daten repräsentieren. Gelesen werden diese wiederum durch die Reflektion eines Lasers an diesen Punkten und Strichen.} + \subfigure[]{ + \includegraphics[width=0.45\textwidth]{papers/reedsolomon/images/Compact_Disc} + } + \subfigure[]{ + \includegraphics[width=0.45\textwidth]{papers/reedsolomon/images/Compact_Disc_zoomed_in} + } + \caption{CDs kamen 1982 auf den Markt. Sie funktioniert durch das Einpressen oder Einbrennen von Punkten und Strichen, die die Daten repräsentieren. Gelesen werden diese wiederum durch die Reflektion eines Lasers an diesen Punkten und Strichen.} \label{fig:cd} \end{figure} \subsection{QR-Codes} -Quick Response Codes oder auch QR-Codes funktionieren nach einem sehr ähnlichen Prinzip wie in unserem Beispiel der Abschnitte \ref{reedsolomon:section:codebsp} - \ref{reedsolomon:section:rekonstruktion} nur das QR-Codes in einem $\mathbb{F}_{256}$ Körper arbeiten. Die Physische Grösse eines Codes ist stark abhängig von der Grösse der Codierung sowie dem Fehlerkorrektur-Level. Es ist so auf dem ersten Blick nicht ersichtlich, wie viel Nutzinformationen ein Qr-Code enthält. Die QR-Codes in Abbildung \ref{fig:qr} zeigen jeweils die Gleiche Information mit unterschiedlichem Fehlerkorrektur-Level. Codes mit einem höheren Korrektur-Level können auch für Designer-Codes Zweckentfremdet werden. Dabei wird z.B. das Firmenlogo oder einen Schriftzug über den Qr-Code gelegt, ohne das die Funktion des Codes beeinträchtigt wird. Ein Beispiel dazu ist unter Abbildung \ref{fig:designqr} zu finden. +Quick Response Codes oder auch QR-Codes funktionieren nach einem sehr ähnlichen Prinzip wie in unserem Beispiel der Abschnitte \ref{reedsolomon:section:codebsp} - \ref{reedsolomon:section:rekonstruktion} nur das QR-Codes in einem $\mathbb{F}_{256}$ Körper arbeiten. Die physische Grösse eines Codes ist stark abhängig von der Menge an codierten Daten sowie dem verwendeten Fehlerkorrektur-Level. Es ist so auf dem ersten Blick nicht ersichtlich, wie viel Nutzinformationen ein Qr-Code enthält. Die QR-Codes in Abbildung \ref{fig:qr} zeigen jeweils die Gleiche Information mit unterschiedlichem Fehlerkorrektur-Level. Codes mit einem höheren Korrektur-Level können auch für Designer-Codes Zweckentfremdet werden. Dabei wird z.B. das Firmenlogo oder einen Schriftzug über den Qr-Code gelegt, ohne das die Funktion des Codes beeinträchtigt wird. Ein Beispiel dazu ist unter Abbildung \ref{fig:designqr} zu finden. % @@ -154,6 +163,6 @@ Quick Response Codes oder auch QR-Codes funktionieren nach einem sehr ähnlichen \subfigure[]{ \includegraphics[width=0.4\textwidth]{papers/reedsolomon/images/designer_qrcode} } - \caption{Während (a) noch ein unveränderter QR-Code repräsentiert, handelt es sich bei (b) nun um einen Designer-QR-Code. Beide Codes verfügen über einen mittleren Fehlerkorrektur-Level von theoretisch 15\%. Da bei (b) jetzt einen Teil des Codes durch ein Logo verdeckt wird, schränkt sich dadurch die Fehlerkorrekturfähigkeit je nach grösse des verdeckten Teils mehr oder weniger stark ein. Unser Designer-Code in (b) ist nur noch in der Lage etwa 9\% des Codes zu rekonstruieren.} + \caption{Während (a) noch einen unveränderten QR-Code repräsentiert, handelt es sich bei (b) nun um einen Designer-QR-Code. Beide Codes verfügen über einen mittleren Fehlerkorrektur-Level von theoretisch 15\%. Da bei (b) jetzt einen Teil des Codes durch ein Logo verdeckt wird, schränkt sich die Fehlerkorrekturfähigkeit je nach Grösse des verdeckten Teils mehr oder weniger stark ein. Unser Designer-Code in (b) ist nur noch in der Lage etwa 9\% des Codes zu rekonstruieren.} \label{fig:designqr} \end{figure}
\ No newline at end of file diff --git a/buch/papers/reedsolomon/dtf.tex b/buch/papers/reedsolomon/dtf.tex index a111527..4552bed 100644 --- a/buch/papers/reedsolomon/dtf.tex +++ b/buch/papers/reedsolomon/dtf.tex @@ -1,55 +1,85 @@ % -% teil3.tex -- Beispiel-File für Teil 3 +% dtf.tex -- Idee mit DFT % -% (c) 2020 Prof Dr Andreas Müller, Hochschule Rapperswil -% -\section{Diskrete Fourier Transformation +\section{Übertragung mit Hilfe der Diskrten Fourientransformation \label{reedsolomon:section:dtf}} \rhead{Umwandlung mit DTF} -Um die Polynominterpolation zu umgehen, gehen wir nun über in die Fourientransformation. -Dies wird weder eine erklärung der Forientransorfmation noch ein genauer gebrauch -für den Reed-Solomon-Code. Dieser Abschnitt zeigt nur wie die Fourientransformation auf Fehler reagiert. -wobei sie dann bei späteren Berchnungen ganz nützlich ist. +Um die Polynominterpolation zu umgehen, gehen wir nun über in die Fourietransformation. +Dies wird weder eine Erklärung der Forientransorfmation, noch ein genauer gebrauch für den Reed-Solomon-Code. +Dieser Abschnitt zeigt nur wie die Fourietransformation auf Fehler reagiert. +Das ganze zeigen wir mit einem Beispiel einer Übertragung von Zahlen mit Hilfe der Fourietransformation. -\subsection{Diskrete Fourientransformation Zusamenhang +\subsection{Diskrete Fourietransformation Zusamenhang \label{reedsolomon:subsection:dtfzusamenhang}} -Die Diskrete Fourientransformation ist definiert als - \[ - \label{ft_discrete} - \hat{c}_{k} - = \frac{1}{N} \sum_{n=0}^{N-1} - {f}_n \cdot e^{-\frac{2\pi j}{N} \cdot kn} - \] -, wenn man nun - \[ - w = e^{-\frac{2\pi j}{N} k} - \] -ersetzte, und $N$ konstantbleibt, erhält man - \[ - \hat{c}_{k}=\frac{1}{N}( {f}_0 w^0 + {f}_1 w^1 + {f}_2 w^2 + \dots + {f}_{N-1} w^N) - \] -was überaust ähnlich zu unserem Polynomidee ist. -\subsection{Übertragungsabfolge -\label{reedsolomon:subsection:Übertragungsabfolge}} +Mit hilfe der Fourietransformation werden die \textcolor{blue}{blauen Datenpunkte} transformiert, +zu den \textcolor{darkgreen}{grünen Übertragungspunkten}. +Durch eine Rücktransformation könnnen die \textcolor{blue}{blauen Datenpunkte} wieder rekonstruiert werden. -\begin{enumerate}[1)] -\item Das Signal hat 64 die Daten, Zahlen welche übertragen werden sollen. -Dabei zusätzlich nach 16 Fehler abgesichert, macht insgesamt 96 Übertragungszahlen. -\item Nun wurde mittels der schnellen diskreten Fourientransformation diese 96 codiert. -Das heisst alle information ist in alle Zahlenvorhanden. -\item Nun kommen drei Fehler dazu an den Übertragungsstellen 7, 21 und 75. -\item Dieses wird nun Empfangen und mittels inversen diskreten Fourientransormation, wieder rücktransformiert. -\item Nun sieht man den Fehler im Decodieren in den Übertragungsstellen 64 bis 96. -\item Nimmt man nun nur diese Stellen 64 bis 96, auch Syndrom genannt, und Transformiert diese. -\item Bekommt man die Fehlerstellen im Locator wieder, zwar nichtso genau, dennoch erkkent man wo die Fehler stattgefunden haben. -\end{enumerate} +\subsubsection{Beispiel einer Übertragung +\label{reedsolomon:subsection:Übertragungsabfolge}} +Der Auftrag ist nun 64 Daten zu übertragen und nach 32 Fehler abzusicheren, +16 Fehler erkennen und rekonstruieren. +Dieser Auftrag soll mittels Fouriertransformation bewerkstelligt werden. +In der Abbildung \ref{reedsolomon:subsection:Übertragungsabfolge} sieht man dies Schritt für Schritt, +und hier werden die einzelne Schritte erklärt: +\begin{enumerate}[(1)] + \item Das Signal hat 64 die Daten $k$, hier zufällige Zahlen, welche übertragen werden sollen. + Zusätzlich soll nach 16 Fehler $t$, die rekonstruierbar sind abgesichert werden. + Das macht dann insgesamt $k + 2t = + 64 +2 \cdot 16= 96$ Übertragungszahlen. + (siehe Abschnitt \externaldocument{papers/reedsolomon/idee}\ref{reedsolomon:section:Fehlerkorrekturstellen}) + Die 32 Fehlerkorrekturstellen werden als Nullzahlen Übertragen. + \item Nun werden mittels der diskreten Fourietransformation diese 96 codiert, transformiert. + Das heisst alle Informationen ist in alle Zahlenvorhanden, auch die Fehlerkorrekturstellen Nullzahlen. + \item Nun kommen drei Fehler dazu an den Übertragungsstellen 7, 21 und 75. + Die Fehler können auf den ganzen 96 Übertragungswerten liegen, wie die 75 zeigt. +Zu Beachten ist auch noch, dass der Fehler um das 20- bis 150-Fache kleiner ist.Die Fehlerskala ist rechts. + \item Dieses wird nun Empfangen, man kann keine Fehler erkennen, da diese soviel kleiner sind. + Für das Decodieren wird die Inverse Fourietransformation angewendet, und alle Fehler werden mittransformiert. + \item Nun sieht man die Fehler im decodierten Signal in den Übertragungszahlen. + Von den Übertragungsstellen 64 bis 96 erkennt man, das diese nicht mehr Null sind. + \item Diese Fehlerkorrekturstellen 64 bis 96, dies definieren wir als Syndrom. + In diesem Syndrom ist die Fehlerinformation gespeichert und muss nur noch transformiert werden. + \item Hier sieht man genau wo die Fehler stattgefunden haben. + Leider nicht mehr mit der Qualtiätt der Ursprünglichen Fehler, sie sind nur noch 0.6 oder 0.4 gross. + Obwohl der Fehler um das 20Fache kleiner ist erkennt man im Locator die Fehlerstellen wieder. + \end{enumerate} + Nun haben wir mit Hilfe der Fourietransformation die 3 Fehlerstellen durch das Syndrom lokalisiert, + jetzt gilt es nur noch diese zu korrigieren und wir haben unser originales Signal wieder. \begin{figure} \centering - \resizebox{0.9\textwidth}{!}{ - %\includegraphics[width=0.5\textwidth]{papers/reedsolomon/images/plot.pdf} - \input{papers/reedsolomon/images/plotfft.tex} + \resizebox{1.1\textwidth}{!}{ + \includegraphics[width=\textwidth]{papers/reedsolomon/figures/plotfft} + %\input{papers/reedsolomon/tikz/plotfftraw.tex} } \caption{Übertragungsabfolge \ref{reedsolomon:subsection:Übertragungsabfolge}} \label{fig:sendorder} -\end{figure}
\ No newline at end of file +\end{figure} + +Nun zur Definition der Diskrete Fourietransformation, diese ist definiert als + \begin{equation} + \hat{c}_{k} + = \frac{1}{N} \sum_{n=0}^{N-1} + {f}_n \cdot e^{-\frac{2\pi j}{N} \cdot kn}. + ,\label{reedsolomon:DFT} + \end{equation} + Wenn man nun + \begin{equation} + w = + e^{-\frac{2\pi j}{N} k} + \label{reedsolomon:DFT_summand} + \end{equation} + ersetzte, und $N$ konstantbleibt, erhält man + \begin{equation} + \hat{c}_{k}= + \frac{1}{N}( {f}_0 w^0 + {f}_1 w^1 + {f}_2 w^2 + \dots + {f}_{N-1} w^N) + \label{reedsolomon:DFT_polynom} + \end{equation} + was überaust ähnlich zu unserem Polynomidee ist. +Die Polynominterpolation und die Fourietransformation rechnen beide mit reelen Zahlen. +Wenn die Fehlerabweichung sehr sehr klein ist, erkennt man diese irgendwann nicht mehr. +Zusätzlich muss mann immer Grenzen bestimmen auf wieviel Stellen gerechnet wird und wie die Fehler erkannt werden im Locator. +Deshalb haben Mathematiker einen neuen Körper gesucht und ihn in der Endlichkeit gefunden, +dies wird nun im nächsten Abschnitt genauer erklärt. + diff --git a/buch/papers/reedsolomon/einleitung.tex b/buch/papers/reedsolomon/einleitung.tex index 2b1d878..074df05 100644 --- a/buch/papers/reedsolomon/einleitung.tex +++ b/buch/papers/reedsolomon/einleitung.tex @@ -7,13 +7,11 @@ \label{reedsolomon:section:einleitung}} \rhead{Einleitung} Der Reed-Solomon-Code ist entstanden um, -das Problem der Fehler, bei der Datenübertragung, zu lösen. -In diesem Abschnitt wird möglichst verständlich die mathematische Abfolge, Funktion oder Algorithmus erklärt. +das Problem der Fehler bei der Datenübertragung, zu lösen. +In diesem Abschnitt wird möglichst verständlich die mathematische Abfolge, +Funktion oder Algorithmus des Reed-Solomon-Code erklärt. Es wird jedoch nicht auf die technische Umsetzung oder Implementierung eingegangen. -Um beim Datenübertragen Fehler zu erkennen, könnte man die Daten jeweils doppelt senden, -und so jeweilige Fehler zu erkennen. -Doch nur schon um weinige Fehler zu erkennen werden überproportional viele Daten doppelt und dreifach gesendet. -Der Reed-Solomon-Code macht dies auf eine andere, clevere Weise. + diff --git a/buch/papers/reedsolomon/experiments/plot.tex b/buch/papers/reedsolomon/experiments/plot.tex index 2196c82..4b156bb 100644 --- a/buch/papers/reedsolomon/experiments/plot.tex +++ b/buch/papers/reedsolomon/experiments/plot.tex @@ -90,7 +90,7 @@ \draw[ultra thick, ->] (zoom) to[out=180, in=90] (syndrom.north); %item - \node[circle, draw, fill =lightgray] at (signal.north west)+(1,0) {1}; + \node[circle, draw, fill =lightgray] at (signal.north west) {1}; \node[circle, draw, fill =lightgray] at (codiert.north west) {2}; \node[circle, draw, fill =lightgray] at (fehler.north west) {3}; \node[circle, draw, fill =lightgray] at (empfangen.north west) {4}; diff --git a/buch/papers/reedsolomon/figures/plotfft.pdf b/buch/papers/reedsolomon/figures/plotfft.pdf Binary files differnew file mode 100644 index 0000000..80d17d2 --- /dev/null +++ b/buch/papers/reedsolomon/figures/plotfft.pdf diff --git a/buch/papers/reedsolomon/figures/polynom2.pdf b/buch/papers/reedsolomon/figures/polynom2.pdf Binary files differnew file mode 100644 index 0000000..55a50ac --- /dev/null +++ b/buch/papers/reedsolomon/figures/polynom2.pdf diff --git a/buch/papers/reedsolomon/idee.tex b/buch/papers/reedsolomon/idee.tex index 39adbbf..41e0d4c 100644 --- a/buch/papers/reedsolomon/idee.tex +++ b/buch/papers/reedsolomon/idee.tex @@ -1,21 +1,30 @@ % -% teil1.tex -- Beispiel-File für das Paper -% -% (c) 2020 Prof Dr Andreas Müller, Hochschule Rapperswil +% idee.tex -- Polynom Idee % \section{Idee \label{reedsolomon:section:idee}} \rhead{Problemstellung} +Um beim Datenübertragen Fehler zu erkennen, könnte man die Daten jeweils doppelt senden, +und so jeweilige Fehler zu erkennen. +Doch nur schon um Fehler zu erkennen werden überproportional viele Daten doppelt und dreifach gesendet. +Der Reed-Solomon-Code macht dies auf eine andere, clevere Weise. Das Problem liegt darin Informationen, Zahlen, zu Übertragen und Fehler zu erkennen. -Beim Reed-Solomon-Code kann man nicht nur Fehler erkennen, +Speziell beim Reed-Solomon-Code kann man nicht nur Fehler erkennen, man kann sogar einige Fehler korrigieren. +Der Unterschied des Fehler erkennen und korrigiren, ist das beim Erkennen nur die Frage beantwortet wird: Ist die Übertragung fehlerhaft oder nicht? +Beim Korrigieren werden Fehler erkannt und dann zusätzlich noch den original Wert rekonstruieren. +Auch eine Variante wäre die Daten nach einer Fehlerhaften sendung, nochmals zum senden auffordern(auch hier wird doppelt und dreifach gesendung), +was bei Reed-Solomon-Code-Anwendungen nicht immer sinnvoll ist. +Anwendungen finden sind im Abchnitt \externaldocument{papers/reedsolomon/anwendungen} +\ref{reedsolomon:section:anwendung} beschrieben. +\subsection{Polynom-Ansatz +\label{reedsolomon:section:polynomansatz}} \rhead{Polynom-Ansatz} -Eine Idee ist aus den Daten -ein Polynom zu bilden. -Diese Polynomfunktion bei bestimmten Werten, ausrechnet und diese Punkte dann überträgt. -Nehmen wir als beisbiel die Zahlen \textcolor{blue}{2}, \textcolor{blue}{1}, \textcolor{blue}{5}, +Eine Idee ist, aus den Daten ein Polynom zu bilden. +Diese Polynomfunktion bei bestimmten Werten errechnet und diese Punkte dann überträgt. +\begin{beispiel} Nehmen wir die Zahlen \textcolor{blue}{2}, \textcolor{blue}{1}, \textcolor{blue}{5}, welche uns dann das Polynom \begin{equation} p(x) @@ -24,49 +33,64 @@ p(x) \label{reedsolomon:equation1} \end{equation} ergeben. -Übertragen werden nun die Werte an den stellen 1, 2, 3\dots 7 dieses Polynomes. +Übertragen werden nun die \textcolor{darkgreen}{grünen Werte} +dieses \textcolor{blue}{blauen Polynomes} an den Stellen 1, 2, 3\dots 7 dieses Polynomes. Grafisch sieht man dies dann in Abbildung \ref{fig:polynom}, -mit den Punkten, $p(1),p(2),...,p(7) = (\textcolor{green}{8}, -\textcolor{green}{15}, \textcolor{green}{26}, -\textcolor{green}{41}, \textcolor{green}{60}, -\textcolor{green}{83}, \textcolor{green}{110})$ -Wenn ein Fehler sich in die Übertragung eingeschlichen hatt, muss der Leser/Empfänger diesen erkennen und das Polynom rekonstruieren. -Der Leser/Empfänger weiss, den Grad des Polynoms und dessen Werte übermittelt wurden. +mit den Punkten, $p(1),p(2),...,p(7) = (\textcolor{darkgreen}{8}, +\textcolor{darkgreen}{15}, \textcolor{darkgreen}{26}, +\textcolor{darkgreen}{41}, \textcolor{darkgreen}{60}, +\textcolor{darkgreen}{83}, \textcolor{darkgreen}{110})$ +Wenn ein Fehler sich in die Übertragung eingeschlichen hat, muss der Leser/Empfänger diesen erkennen und das Polynom rekonstruieren. +Der Leser/Empfänger weiss, den Grad des Polynoms und dessen \textcolor{darkgreen}{Werte} übermittelt wurden. +Die Farbe blau brauchen wir für die \textcolor{blue}{Daten} welche wir mit der Farbe grün \textcolor{darkgreen}{Übermitteln}. +\end{beispiel} -\subsection{Beispiel} -Für das Beispeil aus der Gleichung \eqref{reedsolomon:equation1}, -ist ein Polynome zweiten Grades durch drei Punkte eindeutig bestimmbar. -Hat es Fehler in der Übertragunge gegeben,(Bei Abbildung \ref{fig:polynom}\textcolor{red}{roten Punkte}) kann man diese erkennen, -da alle Punkte, die korrekt sind, auf dem Polynom liegen müssen. -(Bei Abbildung \ref{fig:polynom}\textcolor{green}{grünen Punkte}) +\begin{beispiel} +Ein Polynome zweiten Grades ist durch drei Punkte eindeutig bestimmbar. +Hat es Fehler in der Übertragunge gegeben,in der Abbilbung \ref{fig:polynom} die \textcolor{red}{roten Punkte}). +Erkennt man diese Fehler, da alle korrekten Punkte auf der Parabel liegen müssen. +Die \textcolor{darkgreen}{grünen Punkte} bestimmen die Parabel, und die Fehler können zu den +\textcolor{gray}{Orginalpunkte} rekonstruiert werden. Ab wie vielen Fehler ist das Polynom nicht mehr erkennbar beim Übertragen von 7 Punkten? Bei 2 Fehlern kann man noch eindeutig bestimmen, dass das Polynom mit 4 Punkten, -gegenüber dem mit 5 Punkten falsch liegt.\ref{fig:polynom} -Werden es mehr Fehler kann nur erkennt werden, dass das Polynom nicht stimmt. +gegenüber dem mit 5 Punkten falsch liegt. \ref{fig:polynom} +Werden es mehr Fehler kann nur erkannt werden, dass das Polynom nicht stimmt. Das orginale Polynom kann aber nicht mehr gefunden werden. -Dafür sind mehr übertragene Werte nötig. +Da andere Polynome oder das Konkurrenzpolynom, grau gestrichelt in Abbildung \ref{fig:polynom}, das orginal fehlleitet. +Um das Konkurrenzpolynom auszuschliessen, währen mehr \textcolor{darkgreen}{Übertragungspunkte} nötig. +\end{beispiel} -\begin{figure} +\begin{figure}%[!ht] \centering - %\includegraphics[width=0.5\textwidth]{papers/reedsolomon/images/polynom2} - \input{papers/reedsolomon/images/polynom2.tex} - \caption{Polynom $p(x)$ \eqref{reedsolomon:equation1}} + %\includegraphics[width=\textwidth]{papers/reedsolomon/figures/polynom2} + \input{papers/reedsolomon/tikz/polynomraw.tex} + \caption{Polynom $p(x)$ von der Gleichung\eqref{reedsolomon:equation1}} \label{fig:polynom} \end{figure} -\section{Fehlerbestimmung -\label{reedsolomon:section:Fehlerbestimmmung}} -So wird ein Muster indentifiziert, welches genau vorherbestimmen kann, -wie gross das Polynom sein muss und wie viele Übertragungspunkte gegeben werden müssen. -Um zu bestimmen wie viel Fehler erkennt und korriegiert werden können. -Die Anzahl Zahlen (Daten, ab hier verwenden wir das Wort Nutzlast), -die Entschlüsselt werden sollen, brauchen die gleiche Anzahl an Polynomgraden, beginnend bei Grad 0. ( \( k-1 \) ) -Für die Anzahl an Übertragungspunkte, muss bestimmt werden wieviel Fehler erkennt und korrigiert werden sollen. -Mit Hilfe der Tabelle, sieht man das es bei $t$ Fehlern und $k$ Nutzlast Zahlen, -$k+2t$ Punkte übertragen werden müssen. +\section{Fehlerkorekturstellen bestimmen +\label{reedsolomon:section:Fehlerkorrekturstellen}} +Um zu bestimmen wieviel zusätzliche \textcolor{darkgreen}{Übertragungspunkte} notwendig sind, um die Fehler zu korrigieren, +muss man zuerst wissen, wieviel \textcolor{blue}{Daten} gesendet und wieviel \textcolor{red}{Fehler} erkennt werden sollen. +Die Anzahl \textcolor{blue}{Daten} (ab hier verwenden wir das Wort Nutzlast), die als Polynomkoeffizente $k$ übergeben werden, +brauchen die gleiche Anzahl an Polynomkoeffizententräger, beginnend bei Grad 0 somit ergibt sich der Polynomgrad mit $k-1$. +Für die Anzahl der Fehler $t$, welche korrigiert werden können, gehen wir zum Beispiel. +\begin{beispiel} von den Polynom \ref{reedsolomon:equation1} in, welchem wir \textcolor{darkgreen}{7 Übertragungspunkte} senden. +Durch 3 Punkte wird das Polyom eindeutig bestimmt, nun haben wir mehrere Konkurrenzpolynome, doch mit maximal 2 Fehler liegen auf einem Konkurrenzpolynom, +maximal 4 Punkte und auf unserem orginal 5 Punkte. Ansonsten hatt es mehr Fehler oder unser Konkurrenzpolynom ist das gleiche wie das Original. +Somit können wir nun bestimmen, dass von den \textcolor{darkgreen}{7 Übertragungspunkten$u$} bis zu 2 Fehler korrigiert werden können und 4 Übertragungspunkte zusätzlich gesendet werden müssen. +\end{beispiel} +Man könnte auch dies in der Tabelle \ref{tab:fehlerkorrekturstellen} erkennen, doch mit dieser Gleichung +\begin{equation} + \frac{\textcolor{darkgreen}{u}-\textcolor{blue}{k}}{\textcolor{red}{t}} + =2 + \label{reedsolomon:equation2} +\end{equation} +zeigt sich, dass es $k+2t$ Übertragungspunkte braucht. -\begin{center} - \begin{tabular}{ c c c } +\begin{table} + \centering + \begin{tabular}{ c c | c} \hline Nutzlas & Fehler & Übertragen \\ \hline @@ -77,12 +101,11 @@ $k+2t$ Punkte übertragen werden müssen. $k$ & $t$ & $k+2t$ Werte eines Polynoms vom Grad $k-1$ \\ \hline \end{tabular} -\end{center} + \caption{ Fehlerkorrekturstellen Bestimmung.} + \label{tab:fehlerkorrekturstellen} +\end{table} -Ein toller Nebeneffekt ist das dadurch auch $2t$ Fehler erkannt werden. -Um zurück auf unser Beispiel zu kommen, -können von den 7 Übertragungspunkten bis zu $2t = 2\cdot2 = 4 $ Punkten falsch liegen -und es wird kein eindeutiges Polynom zweiten Grades erkannt, und somit die Nutzlast Daten als fehlerhaft deklariert. -Um aus den Übertragenen Zahlen wieder die Nutzlastzahlen zu bekommen könnte man eine Polynominterpolation anwenden, -doch die Punkte mit Polynominterpolation zu einem Polynom zu rekonstruieren ist schwierig und Fehleranfällig. +Ein Nebeneffekt ist, dass dadurch auch $2t$ Fehler erkannt werden können, nicht aber korrigiert. +Um aus den übertragenen Zahlen wieder die Nutzlastzahlen zu bekommen könnte man eine Polynominterpolation anwenden, +doch die Punkte mit Polynominterpolation zu einem Polynom zu rekonstruieren ist schwierig und fehleranfällig. diff --git a/buch/papers/reedsolomon/images/Compact_Disc_zoomed_in.png b/buch/papers/reedsolomon/images/Compact_Disc_zoomed_in.png Binary files differnew file mode 100644 index 0000000..69556d0 --- /dev/null +++ b/buch/papers/reedsolomon/images/Compact_Disc_zoomed_in.png diff --git a/buch/papers/reedsolomon/images/codiert.txt b/buch/papers/reedsolomon/images/codiert.txt deleted file mode 100644 index 4a481d8..0000000 --- a/buch/papers/reedsolomon/images/codiert.txt +++ /dev/null @@ -1,96 +0,0 @@ -0,284 -1,131.570790435043 -2,41.9840308053375 -3,12.1189172092243 -4,23.8408857476069 -5,69.1793197789512 -6,24.0186013379153 -7,37.3066577242559 -8,18.2010889773887 -9,12.3214904922455 -10,15.6627133315015 -11,24.5237955316204 -12,32.1114345314062 -13,44.9845039238714 -14,13.5324640263625 -15,10.1736266929292 -16,4.58257569495584 -17,23.217268502288 -18,16.5769107917917 -19,6.89948680823017 -20,4.84567134895776 -21,10.4219666223433 -22,43.6179140616243 -23,35.9073375743642 -24,15.0332963783729 -25,21.7594021268945 -26,23.2496572716993 -27,17.9815599423852 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a/buch/papers/reedsolomon/images/fehler.txt b/buch/papers/reedsolomon/images/fehler.txt deleted file mode 100644 index 23f1a83..0000000 --- a/buch/papers/reedsolomon/images/fehler.txt +++ /dev/null @@ -1,96 +0,0 @@ -0,0 -1,0 -2,0 -3,0 -4,0 -5,0 -6,2 -7,0 -8,0 -9,0 -10,0 -11,0 -12,0 -13,0 -14,0 -15,0 -16,0 -17,0 -18,0 -19,0 -20,2 -21,0 -22,0 -23,0 -24,0 -25,0 -26,0 -27,0 -28,0 -29,0 -30,0 -31,0 -32,0 -33,0 -34,0 -35,0 -36,0 -37,0 -38,0 -39,0 -40,0 -41,0 -42,0 -43,0 -44,0 -45,0 -46,0 -47,0 -48,0 -49,0 -50,0 -51,0 -52,0 -53,0 -54,0 -55,0 -56,0 -57,0 -58,0 -59,0 -60,0 -61,0 -62,0 -63,0 -64,0 -65,0 -66,0 -67,0 -68,0 -69,0 -70,0 -71,0 -72,0 -73,0 -74,1 -75,0 -76,0 -77,0 -78,0 -79,0 -80,0 -81,0 -82,0 -83,0 -84,0 -85,0 -86,0 -87,0 -88,0 -89,0 -90,0 -91,0 -92,0 -93,0 -94,0 -95,0 diff --git a/buch/papers/reedsolomon/images/locator.txt b/buch/papers/reedsolomon/images/locator.txt deleted file mode 100644 index b28988c..0000000 --- a/buch/papers/reedsolomon/images/locator.txt +++ /dev/null @@ -1,96 +0,0 @@ -0,0.0301224340567056 -1,0.141653026854885 -2,0.138226631799377 -3,0.0339903276086929 -4,0.310585462557496 -5,0.551427312631385 -6,0.628514858396814 -7,0.51102386251559 -8,0.275861355940449 -9,0.0502396354182268 -10,0.090185502547573 -11,0.110759344849756 -12,0.0684618905063001 -13,0.0362855426992259 -14,0.0697096919781468 -15,0.109288539370248 -16,0.0923187999496653 -17,0.0512198536768088 -18,0.274192386987782 -19,0.51349614953654 -20,0.633154426602466 -21,0.553283743533942 -22,0.307840573214514 -23,0.0341664350328392 -24,0.140270857957 -25,0.138527177682831 -26,0.029637547736156 -27,0.0816962563186052 -28,0.0944383203811073 -29,0.0263932110686261 -30,0.0585881348402056 -31,0.0737117341599984 -32,0.0239973937701886 -33,0.0464215468420038 -34,0.0616218854220964 -35,0.0221963086695009 -36,0.0390764778127646 -37,0.0537637218396934 -38,0.0208333333333332 -39,0.0343107696069045 -40,0.0483441215964552 -41,0.0198077862118806 -42,0.0311207395968725 -43,0.0444955089373458 -44,0.0190533549944159 -45,0.0290049795038723 -46,0.0417536642697558 -47,0.0185261550443084 -48,0.0277059929762261 -49,0.0398606084144816 -50,0.0181978813094817 -51,0.0271098219177584 -52,0.0386836665079729 -53,0.0180518611046889 -54,0.0272138992557141 -55,0.0381891287148314 -56,0.0180809085252469 -57,0.0281418959420061 -58,0.0384596362516637 -59,0.0182864418432272 -60,0.0302250788423173 -61,0.0397874837986351 -62,0.0186786556701694 -63,0.0342489348284216 -64,0.0429932815348666 -65,0.0192777878591759 -66,0.0422808966931999 -67,0.0506815964680563 -68,0.0201167847752226 -69,0.0615048274405271 -70,0.0744953894508454 -71,0.021246054596492 -72,0.142602265816215 -73,0.273502052865436 -74,0.325309673287599 -75,0.272705389655349 -76,0.149074257381345 -77,0.0247199397628712 -78,0.0680137859566976 -79,0.075388270873485 -80,0.0273637831604903 -81,0.0407867704453274 -82,0.0632964886441949 -83,0.0309749128751093 -84,0.0315202035072035 -85,0.0627625211892184 -86,0.0360843918243497 -87,0.02794920551495 -88,0.0677921493367236 -89,0.0437167157553067 -90,0.0270640150996317 -91,0.0783380025231622 -92,0.0561293738314281 -93,0.0278742033265809 -94,0.0981443889498639 -95,0.0794543457386548 diff --git a/buch/papers/reedsolomon/images/plotfft.tex b/buch/papers/reedsolomon/images/plotfft.tex deleted file mode 100644 index 83a89eb..0000000 --- a/buch/papers/reedsolomon/images/plotfft.tex +++ /dev/null @@ -1,89 +0,0 @@ -% -% Plot der Übertrangungsabfolge ins FFT und zurück mit IFFT -% -\begin{tikzpicture}[] - -%--------------------------------------------------------------- - %Knote -\matrix[draw = none, column sep=25mm, row sep=2mm]{ - \node(signal) [] { - \begin{tikzpicture} - \begin{axis} - [title = {\Large {Signal}}, - xlabel={Anzahl Übertragene Zahlen}, - xtick={0,20,40,64,80,98},] - \addplot[blue] table[col sep=comma] {papers/reedsolomon/images/signal.txt}; - \end{axis} - \end{tikzpicture}}; & - - \node(codiert) [] { - \begin{tikzpicture} - \begin{axis}[title = {\Large {Codiert}}] - \addplot[] table[col sep=comma] {papers/reedsolomon/images/codiert.txt}; - \end{axis} - \end{tikzpicture}}; \\ - - &\node(fehler) [] { - \begin{tikzpicture} - \begin{axis}[scale=0.6, title = {\Large {Fehler}}, - xtick={7,21,75}] - \addplot[red] table[col sep=comma] {papers/reedsolomon/images/fehler.txt}; - \end{axis} - \end{tikzpicture}};\\ - - \node(decodiert) [] { - \begin{tikzpicture} - \begin{axis}[title = {\Large {Decodiert}}] - \addplot[blue] table[col sep=comma] {papers/reedsolomon/images/decodiert.txt}; - \end{axis} - \end{tikzpicture}}; & - - \node(empfangen) [] { - \begin{tikzpicture} - \begin{axis}[title = {\Large {Empfangen}}] - \addplot[] table[col sep=comma] {papers/reedsolomon/images/empfangen.txt}; - \end{axis} - \end{tikzpicture}};\\ - - \node(syndrom) [] { - \begin{tikzpicture} - \begin{axis}[title = {\Large {Syndrom}}] - \addplot[blue] table[col sep=comma] {papers/reedsolomon/images/syndrom.txt}; - \end{axis} - \end{tikzpicture}}; & - - \node(locator) [] { - \begin{tikzpicture} - \begin{axis}[title = {\Large {Locator}}] - \addplot[] table[col sep=comma] {papers/reedsolomon/images/locator.txt}; - \end{axis} - \end{tikzpicture}};\\ -}; -%------------------------------------------------------------- - %FFT & IFFT deskription - -\draw[thin,gray,dashed] (0,12) to (0,-12); -\node(IFFT) [scale=0.7] at (0,12.3) {IFFT}; -\draw[<-](IFFT.south west)--(IFFT.south east); -\node(FFT) [scale=0.7, above of=IFFT] {FFT}; -\draw[->](FFT.north west)--(FFT.north east); - -\draw[thick, ->,] (fehler.west)++(-1,0) +(0.05,0.5) -- +(-0.1,-0.1) -- +(0.1,0.1) -- +(0,-0.5); -%Arrows -\draw[ultra thick, ->] (signal.east) to (codiert.west); -\draw[ultra thick, ->] (codiert.south) to (fehler.north); -\draw[ultra thick, ->] (fehler.south) to (empfangen.north); -\draw[ultra thick, ->] (empfangen.west) to (decodiert.east); -\draw[ultra thick, ->] (syndrom.east) to (locator.west); -\draw(decodiert.south east)++(-1.8,1) ellipse (1.3cm and 0.8cm) ++(-1.3,0) coordinate(zoom) ; -\draw[ultra thick, ->] (zoom) to[out=180, in=90] (syndrom.north); - -%item -\node[circle, draw, fill =lightgray] at (signal.north west) {1}; -\node[circle, draw, fill =lightgray] at (codiert.north west) {2}; -\node[circle, draw, fill =lightgray] at (fehler.north west) {3}; -\node[circle, draw, fill =lightgray] at (empfangen.north west) {4}; -\node[circle, draw, fill =lightgray] at (decodiert.north west) {5}; -\node[circle, draw, fill =lightgray] at (syndrom.north west) {6}; -\node[circle, draw, fill =lightgray] at (locator.north west) {7}; -\end{tikzpicture}
\ No newline at end of file diff --git a/buch/papers/reedsolomon/images/signal.txt b/buch/papers/reedsolomon/images/signal.txt deleted file mode 100644 index c4fa5f8..0000000 --- a/buch/papers/reedsolomon/images/signal.txt +++ /dev/null @@ -1,96 +0,0 @@ -0,6 -1,6 -2,0 -3,6 -4,4 -5,0 -6,5 -7,2 -8,1 -9,2 -10,1 -11,2 -12,0 -13,6 -14,3 -15,5 -16,7 -17,5 -18,5 -19,4 -20,1 -21,5 -22,9 -23,9 -24,3 -25,2 -26,6 -27,6 -28,4 -29,2 -30,9 -31,1 -32,1 -33,1 -34,2 -35,6 -36,6 -37,1 -38,9 -39,7 -40,7 -41,1 -42,9 -43,9 -44,10 -45,9 -46,8 -47,5 -48,2 -49,4 -50,1 -51,0 -52,9 -53,3 -54,3 -55,3 -56,5 -57,6 -58,0 -59,8 -60,6 -61,9 -62,3 -63,4 -64,0 -65,0 -66,0 -67,0 -68,0 -69,0 -70,0 -71,0 -72,0 -73,0 -74,0 -75,0 -76,0 -77,0 -78,0 -79,0 -80,0 -81,0 -82,0 -83,0 -84,0 -85,0 -86,0 -87,0 -88,0 -89,0 -90,0 -91,0 -92,0 -93,0 -94,0 -95,0 diff --git a/buch/papers/reedsolomon/images/syndrom.txt b/buch/papers/reedsolomon/images/syndrom.txt deleted file mode 100644 index 8ca9eed..0000000 --- a/buch/papers/reedsolomon/images/syndrom.txt +++ /dev/null @@ -1,96 +0,0 @@ -0,0 -1,0 -2,0 -3,0 -4,0 -5,0 -6,0 -7,0 -8,0 -9,0 -10,0 -11,0 -12,0 -13,0 -14,0 -15,0 -16,0 -17,0 -18,0 -19,0 -20,0 -21,0 -22,0 -23,0 -24,0 -25,0 -26,0 -27,0 -28,0 -29,0 -30,0 -31,0 -32,0 -33,0 -34,0 -35,0 -36,0 -37,0 -38,0 -39,0 -40,0 -41,0 -42,0 -43,0 -44,0 -45,0 -46,0 -47,0 -48,0 -49,0 -50,0 -51,0 -52,0 -53,0 -54,0 -55,0 -56,0 -57,0 -58,0 -59,0 -60,0 -61,0 -62,0 -63,0 -64,0.0275599094902563 -65,0.0115837187254191 -66,0.025877761014238 -67,0.0224618032819697 -68,0.04410594689944 -69,0.0474504002669341 -70,0.0227694695500626 -71,0.0271436638090525 -72,0.0104166666666667 -73,0.0271436638090523 -74,0.0227694695500608 -75,0.0474504002669343 -76,0.0441059468994397 -77,0.0224618032819701 -78,0.0258777610142379 -79,0.0115837187254183 -80,0.027559909490256 -81,0.0245124379481793 -82,0.0499782237195209 -83,0.0401432022864265 -84,0.0232923747656228 -85,0.0237974288564099 -86,0.0143895905726624 -87,0.0271745729691685 -88,0.0275599094902567 -89,0.0515501672184983 -90,0.0358255004834542 -91,0.024700508366373 -92,0.0210194725405171 -93,0.0177592928994296 -94,0.0261327016093158 -95,0.0314909067039411 diff --git a/buch/papers/reedsolomon/main.tex b/buch/papers/reedsolomon/main.tex index e68b947..017fe94 100644 --- a/buch/papers/reedsolomon/main.tex +++ b/buch/papers/reedsolomon/main.tex @@ -8,29 +8,9 @@ \begin{refsection} \chapterauthor{Joshua Bär und Michael Steiner} -Ein paar Hinweise für die korrekte Formatierung des Textes -\begin{itemize} -\item -Absätze werden gebildet, indem man eine Leerzeile einfügt. -Die Verwendung von \verb+\\+ ist nur in Tabellen und Arrays gestattet. -\item -Die explizite Platzierung von Bildern ist nicht erlaubt, entsprechende -Optionen werden gelöscht. -Verwenden Sie Labels und Verweise, um auf Bilder hinzuweisen. -\item -Beginnen Sie jeden Satz auf einer neuen Zeile. -Damit ermöglichen Sie dem Versionsverwaltungssysteme, Änderungen -in verschiedenen Sätzen von verschiedenen Autoren ohne Konflikt -anzuwenden. -\item -Bilden Sie auch für Formeln kurze Zeilen, einerseits der besseren -Übersicht wegen, aber auch um GIT die Arbeit zu erleichtern. -\end{itemize} - % Joshua \input{papers/reedsolomon/einleitung.tex} \input{papers/reedsolomon/idee.tex} -%\input{papers/reedsolomon/teil2.tex} \input{papers/reedsolomon/dtf.tex} % Michael @@ -49,6 +29,7 @@ Bilden Sie auch für Formeln kurze Zeilen, einerseits der besseren \nocite{reedsolomon:voyager} \nocite{reedsolomon:cd_wiki} \nocite{reedsolomon:cd} +\nocite{reedsolomon:strichepunkte} \nocite{reedsolomon:qr_wiki} \nocite{reedsolomon:qr} %\nocite{reedsolomon:mendezmueller} diff --git a/buch/papers/reedsolomon/packages.tex b/buch/papers/reedsolomon/packages.tex index b84e228..40c6ea3 100644 --- a/buch/papers/reedsolomon/packages.tex +++ b/buch/papers/reedsolomon/packages.tex @@ -10,3 +10,5 @@ \usepackage{pgfplots} \usepackage{filecontents} +\usepackage{xr} + diff --git a/buch/papers/reedsolomon/references.bib b/buch/papers/reedsolomon/references.bib index e0a75a8..b84b5a4 100644 --- a/buch/papers/reedsolomon/references.bib +++ b/buch/papers/reedsolomon/references.bib @@ -51,7 +51,7 @@ } @online{reedsolomon:cd, - title = {Funktionsweise des QR-Codes}, + title = {Abbildung einer CD}, url = {https://www.stickpng.com/img/electronics/compact-discs/stack-compact-disc}, date = {2021-07-19}, year = {2021}, @@ -59,6 +59,15 @@ day = {19} } +@online{reedsolomon:strichepunkte, + title = {Abbildung der Striche und Punkte einer CD}, + url = {https://www.researchgate.net/figure/The-readable-area-of-a-CD-is-magnified-in-order- to-see-the-pit-and-land-sizing-The_fig7_303401629}, + date = {2021-07-26}, + year = {2021}, + month = {7}, + day = {26} +} + @online{reedsolomon:qr_wiki, title = {Funktionsweise des QR-Codes}, url = {https://de.wikipedia.org/wiki/QR-Code}, diff --git a/buch/papers/reedsolomon/standalone.tex b/buch/papers/reedsolomon/standalone.tex new file mode 100644 index 0000000..c850d1f --- /dev/null +++ b/buch/papers/reedsolomon/standalone.tex @@ -0,0 +1,30 @@ +\documentclass{book} + +\input{common/packages.tex} + +% additional packages used by the individual papers, add a line for +% each paper +\input{papers/common/addpackages.tex} + +% workaround for biblatex bug +\makeatletter +\def\blx@maxline{77} +\makeatother +\addbibresource{chapters/references.bib} + +% Bibresources for each article +\input{papers/common/addbibresources.tex} + +% make sure the last index starts on an odd page +\AtEndDocument{\clearpage\ifodd\value{page}\else\null\clearpage\fi} +\makeindex + +%\pgfplotsset{compat=1.12} +\setlength{\headheight}{15pt} % fix headheight warning +\DeclareGraphicsRule{*}{mps}{*}{} + +\begin{document} + \input{common/macros.tex} + \def\chapterauthor#1{{\large #1}\bigskip\bigskip} + \input{papers/reedsolomon/main.tex} +\end{document} diff --git a/buch/papers/reedsolomon/standalone/standalone.pdf b/buch/papers/reedsolomon/standalone/standalone.pdf Binary files differnew file mode 100644 index 0000000..4a44333 --- /dev/null +++ b/buch/papers/reedsolomon/standalone/standalone.pdf diff --git a/buch/papers/reedsolomon/experiments/codiert.txt b/buch/papers/reedsolomon/tikz/codiert.txt index 4a481d8..4a481d8 100644 --- a/buch/papers/reedsolomon/experiments/codiert.txt +++ b/buch/papers/reedsolomon/tikz/codiert.txt diff --git a/buch/papers/reedsolomon/experiments/decodiert.txt b/buch/papers/reedsolomon/tikz/decodiert.txt index f6221e6..f6221e6 100644 --- a/buch/papers/reedsolomon/experiments/decodiert.txt +++ b/buch/papers/reedsolomon/tikz/decodiert.txt diff --git a/buch/papers/reedsolomon/experiments/empfangen.txt b/buch/papers/reedsolomon/tikz/empfangen.txt index 38c13b0..38c13b0 100644 --- a/buch/papers/reedsolomon/experiments/empfangen.txt +++ b/buch/papers/reedsolomon/tikz/empfangen.txt diff --git a/buch/papers/reedsolomon/experiments/fehler.txt b/buch/papers/reedsolomon/tikz/fehler.txt index 23f1a83..23f1a83 100644 --- a/buch/papers/reedsolomon/experiments/fehler.txt +++ b/buch/papers/reedsolomon/tikz/fehler.txt diff --git a/buch/papers/reedsolomon/experiments/locator.txt b/buch/papers/reedsolomon/tikz/locator.txt index b28988c..b28988c 100644 --- a/buch/papers/reedsolomon/experiments/locator.txt +++ b/buch/papers/reedsolomon/tikz/locator.txt diff --git a/buch/papers/reedsolomon/tikz/plotfft.tex b/buch/papers/reedsolomon/tikz/plotfft.tex new file mode 100644 index 0000000..bb74dfb --- /dev/null +++ b/buch/papers/reedsolomon/tikz/plotfft.tex @@ -0,0 +1,94 @@ +% +% Plot der Übertrangungsabfolge ins FFT und zurück mit IFFT +% +\documentclass[tikz]{standalone} +\usepackage{amsmath} +\usepackage{times} +\usepackage{pgfplots} +\usepackage{pgfplotstable} +\usepackage{csvsimple} +\usepackage{filecontents} + + +\begin{document} +\begin{tikzpicture}[] + + %--------------------------------------------------------------- + %Knote + \matrix(m) [draw = none, column sep=25mm, row sep=2mm]{ + + \node(signal) [] { + \begin{tikzpicture} + \begin{axis} + [title = {\Large {Signal}}, + xtick={0,20,40,64,80,98}] + \addplot[blue] table[col sep=comma] {tikz/signal.txt}; + \end{axis} + \end{tikzpicture}}; & + + \node(codiert) [] { + \begin{tikzpicture}[] + \begin{axis}[ title = {\Large {Codiert \space + \space Fehler}}, + xtick={0,40,60,100}, axis y line*=left] + \addplot[green] table[col sep=comma] {tikz/codiert.txt}; + \end{axis} + \begin{axis}[xtick={7,21,75}, axis y line*=right] + \addplot[red] table[col sep=comma] {tikz/fehler.txt}; + \end{axis} + \end{tikzpicture}}; \\ + + \node(decodiert) [] { + \begin{tikzpicture} + \begin{axis}[title = {\Large {Decodiert}}] + \addplot[blue] table[col sep=comma] {tikz/decodiert.txt}; + \end{axis} + \end{tikzpicture}}; & + + \node(empfangen) [] { + \begin{tikzpicture} + \begin{axis}[title = {\Large {Empfangen}}] + \addplot[green] table[col sep=comma] {tikz/empfangen.txt}; + \end{axis} + \end{tikzpicture}};\\ + + \node(syndrom) [] { + \begin{tikzpicture} + \begin{axis}[title = {\Large {Syndrom}}] + \addplot[black] table[col sep=comma] {tikz/syndrom.txt}; + \end{axis} + \end{tikzpicture}}; & + + \node(locator) [] { + \begin{tikzpicture} + \begin{axis}[title = {\Large {Locator}}] + \addplot[gray] table[col sep=comma] {tikz/locator.txt}; + \end{axis} + \end{tikzpicture}};\\ + }; + %------------------------------------------------------------- + %FFT & IFFT deskription + + \draw[thin,gray,dashed] (0,9) to (0,-9); + \node(IFFT) [scale=0.9] at (0,9.3) {IFFT}; + \draw[stealth-](IFFT.south west)--(IFFT.south east); + \node(FFT) [scale=0.9, above of=IFFT] {FFT}; + \draw[-stealth](FFT.north west)--(FFT.north east); + + \draw[thick, ->,] (codiert)++(-1,0) +(0.05,0.5) -- +(-0.1,-0.1) -- +(0.1,0.1) -- +(0,-0.5); + %Arrows + \draw[thick, ->] (signal.east) to (codiert.west); + \draw[thick, ->] (codiert.south) to (empfangen.north); + \draw[thick, ->] (empfangen.west) to (decodiert.east); + \draw[thick, ->] (syndrom.east) to (locator.west); + \draw[thick](decodiert.south east)++(-1.8,1) ellipse (1.3cm and 0.8cm) ++(-1.3,0) coordinate(zoom) ; + \draw[thick, ->] (zoom) to[out=180, in=90] (syndrom.north); + + %item + \node[circle, draw, fill =lightgray] at (signal.north west) {1}; + \node[circle, draw, fill =lightgray] at (codiert.north west) {2+3}; + \node[circle, draw, fill =lightgray] at (empfangen.north west) {4}; + \node[circle, draw, fill =lightgray] at (decodiert.north west) {5}; + \node[circle, draw, fill =lightgray] at (syndrom.north west) {6}; + \node[circle, draw, fill =lightgray] at (locator.north west) {7}; +\end{tikzpicture} +\end{document}
\ No newline at end of file diff --git a/buch/papers/reedsolomon/tikz/plotfftraw.tex b/buch/papers/reedsolomon/tikz/plotfftraw.tex new file mode 100644 index 0000000..141d2ce --- /dev/null +++ b/buch/papers/reedsolomon/tikz/plotfftraw.tex @@ -0,0 +1,80 @@ +\begin{tikzpicture}[] + + %--------------------------------------------------------------- + %Knote + \matrix(m) [draw = none, column sep=25mm, row sep=2mm]{ + + \node(signal) [] { + \begin{tikzpicture} + \begin{axis} + [title = {\Large {Signal}}, + xtick={0,20,40,64,80,98}] + \addplot[blue] table[col sep=comma] {tikz/signal.txt}; + \end{axis} + \end{tikzpicture}}; & + + \node(codiert) [] { + \begin{tikzpicture}[] + \begin{axis}[ title = {\Large {Codiert \space + \space Fehler}}, + xtick={0,40,60,100}, axis y line*=left] + \addplot[green] table[col sep=comma] {tikz/codiert.txt}; + \end{axis} + \begin{axis}[xtick={7,21,75}, axis y line*=right] + \addplot[red] table[col sep=comma] {tikz/fehler.txt}; + \end{axis} + \end{tikzpicture}}; \\ + + \node(decodiert) [] { + \begin{tikzpicture} + \begin{axis}[title = {\Large {Decodiert}}] + \addplot[blue] table[col sep=comma] {tikz/decodiert.txt}; + \end{axis} + \end{tikzpicture}}; & + + \node(empfangen) [] { + \begin{tikzpicture} + \begin{axis}[title = {\Large {Empfangen}}] + \addplot[green] table[col sep=comma] {tikz/empfangen.txt}; + \end{axis} + \end{tikzpicture}};\\ + + \node(syndrom) [] { + \begin{tikzpicture} + \begin{axis}[title = {\Large {Syndrom}}] + \addplot[black] table[col sep=comma] {tikz/syndrom.txt}; + \end{axis} + \end{tikzpicture}}; & + + \node(locator) [] { + \begin{tikzpicture} + \begin{axis}[title = {\Large {Locator}}] + \addplot[gray] table[col sep=comma] {tikz/locator.txt}; + \end{axis} + \end{tikzpicture}};\\ + }; + %------------------------------------------------------------- + %FFT & IFFT deskription + + \draw[thin,gray,dashed] (0,9) to (0,-9); + \node(IFFT) [scale=0.9] at (0,9.3) {IFFT}; + \draw[stealth-](IFFT.south west)--(IFFT.south east); + \node(FFT) [scale=0.9, above of=IFFT] {FFT}; + \draw[-stealth](FFT.north west)--(FFT.north east); + + \draw[thick, ->,] (codiert)++(-1,0) +(0.05,0.5) -- +(-0.1,-0.1) -- +(0.1,0.1) -- +(0,-0.5); + %Arrows + \draw[thick, ->] (signal.east) to (codiert.west); + \draw[thick, ->] (codiert.south) to (empfangen.north); + \draw[thick, ->] (empfangen.west) to (decodiert.east); + \draw[thick, ->] (syndrom.east) to (locator.west); + \draw[thick](decodiert.south east)++(-1.8,1) ellipse (1.3cm and 0.8cm) ++(-1.3,0) coordinate(zoom) ; + \draw[thick, ->] (zoom) to[out=180, in=90] (syndrom.north); + + %item + \node[circle, draw, fill =lightgray] at (signal.north west) {1}; + \node[circle, draw, fill =lightgray] at (codiert.north west) {2+3}; + \node[circle, draw, fill =lightgray] at (empfangen.north west) {4}; + \node[circle, draw, fill =lightgray] at (decodiert.north west) {5}; + \node[circle, draw, fill =lightgray] at (syndrom.north west) {6}; + \node[circle, draw, fill =lightgray] at (locator.north west) {7}; +\end{tikzpicture}
\ No newline at end of file diff --git a/buch/papers/reedsolomon/tikz/polynom2.tex b/buch/papers/reedsolomon/tikz/polynom2.tex new file mode 100644 index 0000000..80557fb --- /dev/null +++ b/buch/papers/reedsolomon/tikz/polynom2.tex @@ -0,0 +1,60 @@ +% polynome +%------------------- + +\documentclass[tikz]{standalone} +\usepackage{amsmath} +\usepackage{times} +\usepackage{pgfplots} + + +\begin{document} +% Teiler für das Skalieren der Grafik /40 +\newcommand{\teiler}{40} + + +%////////////////////////////////////// + +\begin{tikzpicture}[>=latex,thick,] + \draw[color=blue, line width=1.4pt] + plot[domain=0:8, samples=100] + ({\x},{(2*\x^2+1*\x+5)/\teiler}); + + \draw[->] (-0.2,0) -- (8,0) coordinate[label={$x$}]; + \draw[->] (0,-0.2) -- (0,150/\teiler) coordinate[label={right:$p(x)$}]; + + \def\punkt#1{ + \fill[color=green] #1 circle[radius=0.08]; + \draw #1 circle[radius=0.07]; + } + + \def\hellpunkt#1{ + \fill[color=lightgray] #1 circle[radius=0.08]; + \draw[gray] #1 circle[ radius=0.07]; + } + + \draw[color=gray,line width=1pt,dashed] + plot[domain=0.5:7, samples=100] + ({\x},{(7.832*\x^2-51.5*\x+121.668)/\teiler}); + + + \punkt{(1,8/\teiler)} + \hellpunkt{(2,15/\teiler)} + \hellpunkt{(3,26/\teiler)} + \punkt{(4,41/\teiler)} + \punkt{(5,60/\teiler)} + \punkt{(6,83/\teiler)} + \punkt{(7,110/\teiler)} + + + + \def\erpunkt#1{ + \fill[color=red] #1 circle[radius=0.08]; + \draw #1 circle[radius=0.07]; + } + \erpunkt{(2,50/\teiler)} + \erpunkt{(3,37.66/\teiler)} + + \draw(0,100/\teiler) -- (-0.1,100/\teiler) coordinate[label={left:$100$}]; + \draw(1,0) -- (1,-0.1) coordinate[label={below:$1$}]; +\end{tikzpicture} +\end{document} diff --git a/buch/papers/reedsolomon/images/polynom2.tex b/buch/papers/reedsolomon/tikz/polynomraw.tex index 288b51c..02968fd 100644 --- a/buch/papers/reedsolomon/images/polynom2.tex +++ b/buch/papers/reedsolomon/tikz/polynomraw.tex @@ -1,12 +1,11 @@ -% polynome -%------------------- -% Teiler für das Skalieren der Grafik /40 +% polynomraw + \newcommand{\teiler}{40} %////////////////////////////////////// -\begin{tikzpicture}[>=latex,thick] +\begin{tikzpicture}[>=latex,thick,] \draw[color=blue, line width=1.4pt] plot[domain=0:8, samples=100] ({\x},{(2*\x^2+1*\x+5)/\teiler}); @@ -21,9 +20,14 @@ \def\hellpunkt#1{ \fill[color=lightgray] #1 circle[radius=0.08]; - \draw #1 circle[radius=0.07]; + \draw[gray] #1 circle[ radius=0.07]; } + \draw[color=gray,line width=1pt,dashed] + plot[domain=0.5:7, samples=100] + ({\x},{(7.832*\x^2-51.5*\x+121.668)/\teiler}); + + \punkt{(1,8/\teiler)} \hellpunkt{(2,15/\teiler)} \hellpunkt{(3,26/\teiler)} @@ -32,9 +36,7 @@ \punkt{(6,83/\teiler)} \punkt{(7,110/\teiler)} - \draw[color=gray,line width=1pt,dashed] - plot[domain=0.5:7, samples=100] - ({\x},{(7.832*\x^2-51.5*\x+121.668)/\teiler}); + \def\erpunkt#1{ \fill[color=red] #1 circle[radius=0.08]; @@ -45,5 +47,4 @@ \draw(0,100/\teiler) -- (-0.1,100/\teiler) coordinate[label={left:$100$}]; \draw(1,0) -- (1,-0.1) coordinate[label={below:$1$}]; -\end{tikzpicture} -%\end{document} +\end{tikzpicture}
\ No newline at end of file diff --git a/buch/papers/reedsolomon/experiments/signal.txt b/buch/papers/reedsolomon/tikz/signal.txt index c4fa5f8..c4fa5f8 100644 --- a/buch/papers/reedsolomon/experiments/signal.txt +++ b/buch/papers/reedsolomon/tikz/signal.txt diff --git a/buch/papers/reedsolomon/experiments/syndrom.txt b/buch/papers/reedsolomon/tikz/syndrom.txt index 8ca9eed..8ca9eed 100644 --- a/buch/papers/reedsolomon/experiments/syndrom.txt +++ b/buch/papers/reedsolomon/tikz/syndrom.txt diff --git a/buch/papers/spannung/Einleitung.tex b/buch/papers/spannung/Einleitung.tex index b1588ff..8e0d36d 100644 --- a/buch/papers/spannung/Einleitung.tex +++ b/buch/papers/spannung/Einleitung.tex @@ -1,17 +1,18 @@ \section{Einleitung\label{spannung:section:Einleitung}} \rhead{Einleitung} Das Hook'sche Gesetz beschreibt die Beziehung von Spannung und Dehnung von linear-elastischen Materialien im Eindimensionalen. -In diesem Kapitel geht es darum das Hook'sche Gesetz im Dreidimensionalen zu beschreiben. +In diesem Kapitel geht es darum, das Hook'sche Gesetz im Dreidimensionalen zu beschreiben. Durch variable Krafteinwirkungen entstehen in jedem Punkt des Materials eine Vielzahl an unterschiedlichen Spannungen. In jedem erdenklichen Punkt im Dreidimensionalen herrscht daher ein entsprechender individueller Spannungszustand. Um das Hook'sche Gesetz für den 3D Spannungszustand formulieren zu können, reichen Skalare nicht aus. -Darum werden Vektoren, Matrizen und Tensoren zur Hilfe gezogen. +Darum werden Vektoren, Matrizen und Tensoren zu Hilfe gezogen. Mit diesen lässt sich eine Spannungsformel für den 3D Spannungszustand bilden. Diese Spannungsformel ist Grundlage für Computerprogramme und geotechnische Versuche, wie der Oedometer-Versuch. -Um die mathematische Untersuchung vorzunehmen, beschäftigt man sich zuerst mit den spezifischen Gegebenheiten und Voraussetzungen. -Ebenfalls gilt es ein paar wichtige Begriffe und deren mathematischen Zeichen einzuführen. -In diesem Kapitel gehen wir auch auf die Zusammenhänge von Spannung, Dehnungen und Verformungen an elastischen Materialien ein, +Um die mathematischen und physikalischen Berechnungen anwenden zu können, +müssen vorerst ein paar spezifische Bedingungen vorausgesetzt und Annahmen getroffen werden. +Ebenfalls gilt es, ein paar wichtige Begriffe und deren mathematischen Zeichen einzuführen. +In diesem Kapitel gehen wir auch auf die Zusammenhänge von Spannungen, Dehnungen und Verformungen an elastischen Materialien ein, wie sie in gängigen Lehrbüchern der Mechanik oder der Geotechnik behandelt werden, z.~B.~\cite{spannung:Grundlagen-der-Geotechnik}. \section{Spannungsausbreitung\label{spannung:section:Spannungsausbreitung}} @@ -29,7 +30,7 @@ Belastet man den Boden mit einer Spannung so wird diese in den Boden geleitet und von diesem kompensiert. Im Boden entstehen unterschiedlich hohe Zusatzspannungen. Diese Zusatzspannung breitet sich räumlich im Boden aus. -Im Falle einer konstanten Flächenlast $\sigma$ siehe Abbildung~\ref{spannung:Bild4} breitet sich die Zusatzspannung zwiebelartig aus. +Im Falle einer konstanten Flächenlast $\sigma$ siehe Abbildung~\ref{fig:Bild4} breitet sich die Zusatzspannung zwiebelartig aus. \begin{figure} \centering @@ -38,11 +39,11 @@ Im Falle einer konstanten Flächenlast $\sigma$ siehe Abbildung~\ref{spannung:Bi \label{fig:Bild4} \end{figure} -Mit der Tiefe $t$ nimmt diese permanent ab (siehe Abbildung~\ref{spannung:Bild5}). -Wie diese Geometrie der Ausbreitung ist, kann durch viele Modelle und Ansätze näherungsweise beschrieben werden. +Mit der Tiefe $t$ nimmt diese permanent ab (siehe Abbildung~\ref{fig:Bild5}). +Wie diese Geometrie der Ausbreitung aussieht, kann durch viele Modelle und Ansätze näherungsweise beschrieben werden. Diese Zusatzspannung $\sigma$ ist im Wesentlichen abhängig von $(x,y,t)$. Je nach Modell werden noch andere Parameter berücksichtigt. -Das können beispielsweise jenste Bodenkennwerte oder auch der Wassergehalt sein. +Das können beispielsweise verschiedene Bodenkennwerte oder auch der Wassergehalt sein. \begin{figure} \centering @@ -72,18 +73,18 @@ berechnet werden mit: t &= \text{Tiefe [\si{\meter}]} \\ s &= \text{Setzung, Absenkung [m].} \end{align*} -Diese Zusammenhänge sind wie erwähnt unter anderem im Lehrbuch [\cite{spannung:Grundlagen-der-Geotechnik}] beschrieben. +Diese Zusammenhänge sind wie erwähnt unter anderem im Lehrbuch \cite{spannung:Grundlagen-der-Geotechnik} beschrieben. In der praktischen Geotechnik wird man allerdings weitaus schwierigere Situationen antreffen. -Ein Beispiel wäre eine Baugrube mit einem Baugrubenabschluss, wo ein Teil des Bodens abgetragen ist (siehe Abbildung~\ref{spannung:Bild3}). +Ein Beispiel wäre eine Baugrube mit einem Baugrubenabschluss, wo ein Teil des Bodens abgetragen ist (siehe Abbildung~\ref{fig:Bild3}). Die Ausbreitung der Zusatzspannung $\sigma(x,y,t)$ würde hier deutlich komplizierter ausfallen. Dies bedeutet auch eine komplexere Setzung der Bodenoberfläche infolge einer Flächenlast $\sigma$. Aus allen zusätzlichen Spannungen müssen die adäquaten Dehnungen mit Hilfe einer Spannungsgleichung berechnet werden. Diese beruht auf Annahmen nach Hooke auf einem linear-elastischen Boden. -Generell wird im Ingenieurwesen versucht Phänomene möglichst nach dem Hook'schen Gesetz abbilden zu können. +Generell wird im Bauingenieurwesen oder auch im Maschinenbau versucht, manche Phänomene möglichst nach dem Hook'schen Gesetz abbilden zu können. \begin{figure} \centering \includegraphics[width=0.45\linewidth,keepaspectratio]{papers/spannung/Grafiken/Bild3.png} - \caption{Beispiel eines Lastauftrags auf den Boden bei einer komplexeren Situation, welches kompliziertere Spannungsausbreitung zur Folge hat} + \caption{Beispiel eines Lastauftrags auf den Boden bei einer komplexeren Situation, welche kompliziertere Spannungsausbreitung zur Folge hat} \label{fig:Bild3} \end{figure} diff --git a/buch/papers/spannung/main.tex b/buch/papers/spannung/main.tex index bbdf730..d2aeda9 100644 --- a/buch/papers/spannung/main.tex +++ b/buch/papers/spannung/main.tex @@ -3,7 +3,7 @@ % % (c) 2020 Hochschule Rapperswil % -\chapter{Thema\label{chapter:spannung}} +\chapter{Dreidimensionaler Spannungszustand\label{chapter:spannung}} \lhead{Dreiachsiger Spannungszustand} \begin{refsection} \chapterauthor{Adrian Schuler und Thomas Reichlin} diff --git a/buch/papers/spannung/teil0.tex b/buch/papers/spannung/teil0.tex index 7647252..089c28e 100644 --- a/buch/papers/spannung/teil0.tex +++ b/buch/papers/spannung/teil0.tex @@ -1,9 +1,10 @@ \section{Der Spannungszustand\label{spannung:section:Der Spannungsustand}} \rhead{Der Spannungszustand} -Ein Spannungszustand ist durch alle Spannungen, welche in einem beliebigen Punkt im Körper wirken, definiert (siehe Abbildung~\ref{spannung:Bild2}). +Ein Spannungszustand ist durch alle Spannungen, welche in einem beliebigen Punkt im Körper wirken, definiert (siehe Abbildung~\ref{fig:Bild2}). Änderungen der äusseren Kräfte verändern die inneren Spannungszustände im Material. -Um alle Spannungen eines Punktes darstellen zu können, wird ein infinitesimales Bodenelement in Form eines Würfels modellhaft vorgestellt. -Man spricht auch von einem Elementarwürfel, da dieser elementar klein ist. +Um alle Spannungen eines Punktes darstellen zu können, +stellt man sich modellhaft ein infinitesimales Bodenelement in Form eines Würfels vor. +Man spricht auch von einem Elementarwürfel. \begin{figure} \centering @@ -15,19 +16,19 @@ Man spricht auch von einem Elementarwürfel, da dieser elementar klein ist. Es werden jeweils drei Seiten dieses Würfels betrachtet, wobei die drei gegenüberliegenden Seiten im Betrag die selben Spannungen aufweisen, sodass der Elementarwürfel im Gleichgewicht ist. Wäre dieses Gleichgewicht nicht vorhanden, käme es zu Verschiebungen und Drehungen. -Das infinitesimale Bodenteilchen hat die Koordinaten $1$, $2$, $3$. +Das infinitesimale Bodenteilchen hat die Koordinatenachsen $1$, $2$, $3$. Veränderungen der Normalspannungen können durch Schubspannungen kompensiert werden und umgekehrt. -So sind insgesamt neun verschiedene Spannungen möglich, wobei drei Normal- und sechs Schubspannungen sind. +So sind insgesamt neun verschiedene Spannungen möglich, konkret sind dies drei Normal- und sechs Schubspannungen. Normalspannungen wirken normal (mit rechtem Winkel) zur angreifenden Fläche und Schubspannungen parallel zur angreifenden Fläche. Alle Beträge dieser neun Spannungen am Elementarwürfel bilden den Spannungszustand. -Daraus können die äquivalenten Dehnungen $\varepsilon$ mit Hilfe des Hook'schen Gesetz berechnet werden. +Daraus können die äquivalenten Dehnungen $\varepsilon$ mit Hilfe des Hook'schen Gesetzes berechnet werden. Daher gibt es auch den entsprechenden Dehnungszustand. \section{Spannungszustand\label{spannung:section:Spannungsustand}} \rhead{Spannungszustand} -Im einachsigen Spannungszustand herrscht nur die Normalspannung $\sigma_{11}$ (siehe Abbildung~\ref{spannung:Bild1}). +Im einachsigen Spannungszustand herrscht nur die Normalspannung $\sigma_{11}$ (siehe Abbildung~\ref{fig:Bild1}). Das Hook'sche Gesetz beschreibt genau diesen 1D Spannungszustand. Nach Hooke gilt: \[ @@ -59,7 +60,7 @@ mit A &= \text{Fläche [\si{\meter\squared}].} \end{align*} Diese Beziehung gilt bei linear-elastischen Materialien, welche reversible Verformungen zulassen. -Es ist praktisch die relative Dehnung $\varepsilon$ anzugeben und nicht eine absolute Längenänderung $\Delta l$. +Es ist praktisch, die relative Dehnung $\varepsilon$ anzugeben und nicht eine absolute Längenänderung $\Delta l$. \begin{figure} \centering \includegraphics[width=0.35\linewidth,keepaspectratio]{papers/spannung/Grafiken/Bild1.png} @@ -73,10 +74,10 @@ Mithilfe vom Elastizitätsmodul $E$ als Proportionalitätskonstante lässt sich E\cdot\varepsilon \] beschreiben. -Im Falle, dass $E$ nicht konstant ist, kann dieser näherungsweise durch +Im Falle, dass $E$ nicht konstant ist, wird dieser durch \[ E = -\frac{\Delta\sigma}{\Delta\varepsilon} +\frac{\text{d}\sigma}{\text{d}\varepsilon} \] -ausgedrückt werden.
\ No newline at end of file +ausgedrückt.
\ No newline at end of file diff --git a/buch/papers/spannung/teil1.tex b/buch/papers/spannung/teil1.tex index 74516c1..647b452 100644 --- a/buch/papers/spannung/teil1.tex +++ b/buch/papers/spannung/teil1.tex @@ -1,8 +1,8 @@ \section{Skalare, Vektoren, Matrizen und Tensoren\label{spannung:section:Skalare,_Vektoren,_Matrizen_und_Tensoren}} \rhead{Skalare, Vektoren, Matrizen und Tensoren} -Der Begriff Tensor kann als Überbegriff, der mathematischen Objekte Skalar, Vektor und Matrix, betrachtet werden. +Der Begriff Tensor kann als Überbegriff der mathematischen Objekte Skalar, Vektor und Matrix, betrachtet werden. Allerdings sind noch höhere Stufen dieser Objekte beinhaltet. -Ein Skalar, ein Vektor oder eine Matrix ist daher auch ein Tensor. +Skalare, Vektoren oder Matrizen sind daher auch Tensoren. Ein Skalar ist ein Tensor 0. Stufe. Mit einem Vektor können mehrere Skalare auf einmal beschrieben werden. Ein Vektor hat daher die Stufe 1 und ist höherstufig als ein Skalar. @@ -14,11 +14,10 @@ Jede Stufe von Tensoren verlangt andere Rechenregeln. So zeigt sich auch der Nachteil von Tensoren mit Stufen höher als 2. Man ist also bestrebt höherstufige Tensoren mit Skalaren, Vektoren oder Matrizen zu beschreiben. -Der Begriff Tensor wurde 1840 von Rowan Hamilton in die Mathematik eingeführt. +In den 40er Jahren vom 19. Jahrhundert wurde der Begriff Tensor von Rowan Hamilton in die Mathematik eingeführt. James Clerk Maxwell hat bereits mit Tensoren operiert, ohne den Begriff Tensor gekannt zu haben. Erst Woldemar Voigt hat den Begriff in die moderne Bedeutung von Skalar, Matrix und Vektor verallgemeinert. Er hat in der Elastizitätstheorie als erstes Tensoren eingesetzt und beschrieben. Auch Albert Einstein hat solche Tensoren eingesetzt, um in der Relativitätstheorie die Änderung der 4D Raumzeit beschreiben zu können. \cite{spannung:Tensor} -\cite{spannung:Voigtsche-Notation} diff --git a/buch/papers/spannung/teil2.tex b/buch/papers/spannung/teil2.tex index 6326eab..8620afe 100644 --- a/buch/papers/spannung/teil2.tex +++ b/buch/papers/spannung/teil2.tex @@ -3,7 +3,7 @@ Durch komplexe Spannungsausbreitungen im Boden entstehen im 3D Spannungszustand unterschiedliche Normal- und Schubspannungen. \begin{figure} \centering - \includegraphics[width=0.4\linewidth,keepaspectratio]{papers/spannung/Grafiken/infinitesimalerWuerfel.png} + \includegraphics[width=0.30\linewidth,keepaspectratio]{papers/spannung/Grafiken/infinitesimalerWuerfel.png} \caption{Beispiel eines Spannungszustandes; Vergrösserung eines infinitesimalen Bodenteilchen} \label{fig:infinitesimalerWuerfel} \end{figure} @@ -49,7 +49,7 @@ Der Dehnungstensor ist ebenfalls ein Tensor 2. Stufe und kann somit auch als $3\ dargestellt werden und beschreibt den gesamten Dehnungszustand. Der Spannungs- und Dehnungstensor 2. Stufe kann je in einen Tensor 1. Stufe überführt werden, welches ein Spaltenvektor ist. -Gemäss der Hadamard-Algebra dürfen Zeile um Zeile in eine Spalte notiert werden, sodass es einen Spaltenvektor ergibt. +Man darf Zeile um Zeile in eine Spalte notieren, sodass es einen Spaltenvektor ergibt. So ergibt sich der Spannungsvektor \[ @@ -79,7 +79,7 @@ So ergibt sich der Spannungsvektor \sigma_{33} \end{pmatrix} \] -und Dehnungsvektor +und der Dehnungsvektor \[ \overline{\varepsilon} = @@ -140,14 +140,6 @@ C_{3311} & C_{3312} & C_{3313} & C_{3321} & C_{3322} & C_{3323} & C_{3331} & C_{ \end{pmatrix} \] geschrieben werden kann. -Dieser Elastizitätstensor muss für isotrope Materialien zwingend symmetrisch sein. -Folglich gilt: -\[ -\overline{\overline{C}} -= -\overline{\overline{C}}~^{T} -. -\] Die allgemeine Spannungsgleichung lautet nun: \[ \vec\sigma @@ -155,8 +147,7 @@ Die allgemeine Spannungsgleichung lautet nun: \overline{\overline{C}}\cdot\vec{\varepsilon} . \] - -Als Indexnotation +Sie kann ebenfalls als Indexnotation \[ \sigma_{ij} = @@ -164,7 +155,15 @@ Als Indexnotation \sum_{l=1}^3 C_{ijkl}\cdot\varepsilon_{kl} \] -kann dies ebenfalls geschrieben werden. +geschrieben werden. +Der Elastizitätstensor muss für isotrope Materialien zwingend symmetrisch sein. +Folglich gilt: +\[ +\overline{\overline{C}} += +\overline{\overline{C}}~^{T} +. +\] Die Konstanten $C$ werden nun nach dem Hook'schen Gesetz mit Hilfe des Elastizitätsmoduls $E$ definiert. Da dieser Modul durch die eindimensionale Betrachtung definiert ist, @@ -221,7 +220,7 @@ definiert ist. Trägt man die Konstanten in die Matrix ein, ergibt sich \end{pmatrix} . \] -Die Normalspannung $\sigma_{22}$ lässt sich exemplarisch als +Die Normalspannung $\sigma_{22}$ lässt sich zum Beispiel als \[ \sigma_{22} = @@ -229,11 +228,13 @@ Die Normalspannung $\sigma_{22}$ lässt sich exemplarisch als \] berechnen. +Reduzierte Spannungs- und Dehnungsgleichungen + Man betrachte nun die Eigenschaften des Elastizitätstensors. Dieser ist quadratisch und symmetrisch, die verschiedenen Einträge wechseln sich aber miteinander ab. Es ergeben sich keine Blöcke mit einheitlichen Einträgen. -Allerdings weiss man, dass im isotropen Boden der Spannungs-, Dehnungs- und daher auch Elastizitätstensor symmetrisch sind. +Allerdings weiss man, dass im isotropen Boden der Spannungs-, Dehnungs- und daher auch der Elastizitätstensor symmetrisch sind. Wäre dem nicht so, würde sich das Material je nach Richtung unterschiedlich elastisch verhalten. Diese Symmetrie setzt daher voraus, dass \[ @@ -399,7 +400,7 @@ Somit lässt sich die reduzierte allgemeine Spannungsgleichung mit \] beschreiben. Die Konstanten $C$ werden wieder nach dem Hook'schen Gesetz definiert. -Dies ergibt die Spannungsformel, welche weit möglichst vereinfacht ist: +Dies ergibt die Spannungsgleichung, welche weit möglichst vereinfacht ist: \begin{equation} \begin{pmatrix} \sigma_{11}\\ @@ -433,7 +434,7 @@ Dies ergibt die Spannungsformel, welche weit möglichst vereinfacht ist: Im Elastizitätstensor fallen zwei $3\times3$ Blöcke auf, welche nur Einträge mit $0$ haben. Der Tensor besagt also, dass diese jeweiligen Dehnungen keinen Einfluss auf unsere Spannung haben. -Man sieht nun auch ganz gut, dass sich im Vergleich zu der allgemeinen Spannungsgleichung, die Einträge verschoben haben. +Man sieht nun auch ganz gut, dass sich im Vergleich zu der allgemeinen Spannungsgleichung die Einträge verschoben haben. Da nach Voigt zuerst die Normalspannungen und anschliessend die Schubspannungen notiert worden sind, ergeben sich die $3\times3$ Blöcke. Man betrachte als Beispiel die Berechnung von $\sigma_{33}$. @@ -441,8 +442,8 @@ Es ist ersichtlich, dass die Schubdehnungen keinen Einfluss auf $\sigma_{33}$ ha Der Einfluss der zu $\sigma_{33}$ äquivalenten Dehnung $\varepsilon_{33}$ hat den grössten Einfluss. Die anderen Normalspannungen $\sigma_{11}$ und $\sigma_{22}$ haben einen unter anderem mit $\nu$ korrigierten Einfluss. -Von $\overline{\overline{C}}$ bildet man noch die inverse Matrix $\overline{\overline{C}}\mathstrut^{-1}$ um die Gleichung umstellen zu können. -Dadurch erhält man die Dehnungsgleichung: +Von $\overline{\overline{C}}$ bildet man die inverse Matrix $\overline{\overline{C}}\mathstrut^{-1}$, mithilfe des Gauss - Jordan Algorithmus, um die Gleichung umstellen zu können. +Durch einige Berechnungsschritte erhält man die Dehnungsgleichung: \[ \vec{\varepsilon} diff --git a/buch/papers/spannung/teil3.tex b/buch/papers/spannung/teil3.tex index 3e456c3..a9080ea 100644 --- a/buch/papers/spannung/teil3.tex +++ b/buch/papers/spannung/teil3.tex @@ -30,7 +30,7 @@ q \label{spannung:Invariante_q} . \end{equation} -Diese Zusammenhänge werden im Skript [\cite{spannung:Stoffgesetze-und-numerische-Modellierung-in-der-Geotechnik}] aufgezeigt. +Diese Zusammenhänge werden im Skript \cite{spannung:Stoffgesetze-und-numerische-Modellierung-in-der-Geotechnik} aufgezeigt. Die hydrostatische Spannung $p$ kann gemäss Gleichung \eqref{spannung:Invariante_p} als \[ p @@ -38,28 +38,28 @@ p \frac{\sigma_{11}+2\sigma_{33}}{3} \] vereinfacht werden. -Die deviatorische Spannung $q$ wird gemäss Gleichung \eqref{spannung:Invariante_q}als +Die deviatorische Spannung $q$ wird gemäss Gleichung \eqref{spannung:Invariante_q} als \[ q = \sigma_{11}-\sigma_{33} \] -vereinfacht. Man kann $p$ als Isotrop und $q$ als Schub betrachten. +vereinfacht. Man kann $p$ als Druck und $q$ als Schub betrachten. -Die Invarianten können mit der Spannungsformel \eqref{spannung:Spannungsgleichung} berechnet werden. +Die Invarianten $p$ und $q$ können mit der Spannungsgleichung \eqref{spannung:Spannungsgleichung} berechnet werden. Durch geschickte Umformung dieser Gleichung, lassen sich die Module als Faktor separieren. Dabei entstehen spezielle Faktoren mit den Dehnungskomponenten. So ergibt sich \[ -\overbrace{\frac{\sigma_{11}+2\sigma_{33}}{3}}^{p} +\overbrace{\frac{\sigma_{11}+2\sigma_{33}}{3}}^{\displaystyle{p}} = -\frac{E}{3(1-2\nu)} \overbrace{(\varepsilon_{11} - 2\varepsilon_{33})}^{\varepsilon_{v}} +\frac{E}{3(1-2\nu)} \overbrace{(\varepsilon_{11} - 2\varepsilon_{33})}^{\displaystyle{{\varepsilon_{v}}}} \] und \[ -\overbrace{\sigma_{11}-\sigma_{33}}^{q} +\overbrace{\sigma_{11}-\sigma_{33}}^{\displaystyle{q}} = -\frac{3E}{2(1+\nu)} \overbrace{\frac{2}{3}(\varepsilon_{11} - \varepsilon_{33})}^{\varepsilon_{s}} +\frac{3E}{2(1+\nu)} \overbrace{\frac{2}{3}(\varepsilon_{11} - \varepsilon_{33})}^{\displaystyle{\varepsilon_{s}}} . \] Die Faktoren mit den Dehnungskomponenten können so mit @@ -79,8 +79,8 @@ eingeführt werden, mit \varepsilon_{v} &= \text{Hydrostatische Dehnung [-]} \\ \varepsilon_{s} &= \text{Deviatorische Dehnung [-].} \end{align*} -Die hydrostatische Dehnung $\varepsilon_{v}$ kann mit einer Kompression verglichen werden. -Die deviatorische Dehnung $\varepsilon_{s}$ kann mit einer Verzerrung verglichen werden. +Die hydrostatische Dehnung $\varepsilon_{v}$ kann mit einer Kompression und +die deviatorische Dehnung $\varepsilon_{s}$ mit einer Verzerrung verglichen werden. Diese zwei Gleichungen kann man durch die Matrixschreibweise \begin{equation} @@ -90,8 +90,8 @@ Diese zwei Gleichungen kann man durch die Matrixschreibweise \end{pmatrix} = \begin{pmatrix} - \frac{3E}{2(1+\nu)} & 0 \\ - 0 & \frac{E}{3(1-2\nu)} + \displaystyle{\frac{3E}{2(1+\nu)}} & 0 \\ + 0 & \displaystyle{\frac{E}{3(1-2\nu)}} \end{pmatrix} \begin{pmatrix} \varepsilon_{s}\\ @@ -100,9 +100,11 @@ Diese zwei Gleichungen kann man durch die Matrixschreibweise \label{spannung:Matrixschreibweise} \end{equation} vereinfachen. -Man hat so eine Matrix multipliziert mit einem Vektor und erhält einen Vektor. -Änderungen des Spannungszustandes können mit dieser Gleichung vollumfänglich erfasst werden. +Änderungen des Spannungszustandes können mit diesen Gleichungen vollumfänglich erfasst werden. +Diese Spannungsgleichung mit den zwei Einträgen ($p$ und $q$) ist gleichwertig +wie die ursprüngliche Spannungsgleichung mit den neun Einträgen +($\sigma_{11}$, $\sigma_{12}$, $\sigma_{13}$, $\sigma_{21}$, $\sigma_{22}$, $\sigma_{23}$, $\sigma_{31}$, $\sigma_{32}$, $\sigma_{33}$). Mit dieser Formel \eqref{spannung:Matrixschreibweise} lassen sich verschieden Ergebnisse von Versuchen analysieren und berechnen. -Ein solcher Versuch, den oft in der Geotechnik durchgeführt wird, ist der Oedometer-Versuch. +Ein solcher Versuch, der oft in der Geotechnik durchgeführt wird, ist der Oedometer-Versuch. Im nächsten Kapitel wird die Anwendung der Matrix an diesem Versuch beschrieben. diff --git a/buch/papers/spannung/teil4.tex b/buch/papers/spannung/teil4.tex index 2f2e4ce..00b2d4f 100644 --- a/buch/papers/spannung/teil4.tex +++ b/buch/papers/spannung/teil4.tex @@ -1,6 +1,6 @@ -\section{Oedometer-Versuch\label{spannung:section:Oedometer-Versuch}} -\rhead{Oedometer-Versuch} -Mit dem Oedometer-Versuch kann der oedometrische Elastizitätsmodul $E_{OED}$ bestimmt werden. +\section{Oedometrischer Elastizitätsmodul\label{spannung:section:Oedometrischer Elastizitätsmodul}} +\rhead{Oedometrischer Elastizitätsmodul} +Mit dem Oedometer-Versuch kann der oedometrische Elastizitätsmodul $E_{\text{OED}}$ bestimmt werden. Dieser beschreibt ebenfalls das Verhältnis zwischen Spannung und Dehnung, allerdings unter anderen Bedingungen. Diese Bedingung ist das Verhindern der seitlichen Verformung, sprich der Dehnung in Richtung $1$ und $2$. Es wird ein Probeelement mit immer grösseren Gewichten belastet, welche gleichmässig auf das Material drücken. @@ -43,8 +43,8 @@ Diese lautet nun: \end{pmatrix} = \begin{pmatrix} - \frac{E_{OED}}{(1+\nu)} & 0 \\ - 0 & \frac{E_{OED}}{3(1-2\nu)} + \displaystyle{\frac{E_{\text{OED}}}{(1+\nu)}} & 0 \\ + 0 & \displaystyle{\frac{E_{\text{OED}}}{3(1-2\nu)}} \end{pmatrix} \begin{pmatrix} \varepsilon_{11}\\ @@ -52,28 +52,28 @@ Diese lautet nun: \end{pmatrix} . \] -Daraus lässt sich bei jedem Setzungsgrad der oedometrische Elastitzitätsmodul $E_{OED}$ und die seitlichen Spannungen $\sigma_{33}$ mit den 2 Gleichungen +Daraus lässt sich bei jedem Setzungsgrad der oedometrische Elastitzitätsmodul $E_{\text{OED}}$ und die seitlichen Spannungen $\sigma_{33}$ mit den zwei Gleichungen \[ \sigma_{11}-\sigma_{33} = -\frac{E_{OED}}{(1+\nu)}\cdot\varepsilon_{11} +\frac{E_{\text{OED}}}{(1+\nu)}\cdot\varepsilon_{11} \] und \[ \sigma_{11}+2\sigma_{33} = -\frac{E_{OED}}{3(1-2\nu)}\cdot\varepsilon_{11} +\frac{E_{\text{OED}}}{3(1-2\nu)}\cdot\varepsilon_{11} \] berechnen. -Mit diesen Gleichungen hat man das Gleichungssystem um $E_{OED}$ und $\sigma_{33}$ zu berechnen. +Mit diesen Gleichungen hat man das Gleichungssystem um $E_{\text{OED}}$ und $\sigma_{33}$ zu berechnen. Die Poisson-Zahl muss als Kennwert gemäss der Bodenklasse gewählt werden. -Den Versuch kann man auf einem $\sigma$-$\varepsilon$-Diagramm abtragen (siehe Abbildung~\ref{spannung:DiagrammOedometer-Versuch}). +Den Versuch kann man auf einem $\sigma$-$\varepsilon$-Diagramm abtragen (siehe Abbildung~\ref{fig:DiagrammOedometer-Versuch}). Durch die Komprimierung nimmt der Boden mehr Spannung auf, und verformt sich zugleich weniger stark. -Mit diesem ermittelten $E_{OED}$ kann man nun weitere Berechnungen für die Geotechnik durchführen. +Mit diesem ermittelten $E_{\text{OED}}$ kann man nun weitere Berechnungen für die Geotechnik durchführen. \begin{figure} \centering - \includegraphics[width=0.5\linewidth,keepaspectratio]{papers/spannung/Grafiken/DiagrammOedometer-Versuch.png} + \includegraphics[width=0.45\linewidth,keepaspectratio]{papers/spannung/Grafiken/DiagrammOedometer-Versuch.png} \caption{Diagramm Charakteristik verschiedener Elastizitätsmodule bei gleichem Material} \label{fig:DiagrammOedometer-Versuch} \end{figure}
\ No newline at end of file diff --git a/buch/papers/verkehr/main.tex b/buch/papers/verkehr/main.tex index 6348993..98d0581 100644 --- a/buch/papers/verkehr/main.tex +++ b/buch/papers/verkehr/main.tex @@ -3,8 +3,7 @@ % % (c) 2020 Hochschule Rapperswil % -\chapter{Thema\label{chapter:verkehr}} -\lhead{Verkehrsfluss und Verkehrsnetze} +\chapter{Verkehrsfluss und Verkehrsnetze\label{chapter:verkehr}} \begin{refsection} \chapterauthor{Pascal Andreas Schmid und Robine Luchsinger} diff --git a/buch/papers/verkehr/section1.tex b/buch/papers/verkehr/section1.tex index d96d450..6ac86ad 100644 --- a/buch/papers/verkehr/section1.tex +++ b/buch/papers/verkehr/section1.tex @@ -1,99 +1,75 @@ -\section{Einführung} \label{section:verkehr/einfuehrung} -\subsection{Verkehrsnetze} Das Verkehrsnetz besteht aus allen Anlagen, auf oder unter der Erdoberfläche, auf denen eine räumliche Fortbewegung von Personen oder auch Gütern stattfindet. Verkehrsnetze sind ein Bestandteil der Verkehrsinfrastruktur, die auf topografischen Karten festgehalten werden. Sie umfassen den Schienenverkehr, alle Strassen und Wege, wie auch Flugplätze und alle dazugehörigen Bauwerke. Aus verkehrsgeografischer Sicht besteht das Verkehrsnetz aus Kanten, Knotenpunkten und dem Hinterland. Die Knotenpunkte werden auch hier durch die Kanten verbunden, die den Verkehrsstrom aufnehmen, wobei das Hinterland durch einzelne Knoten versorgt wird. Die Aufteilung in Kanten und Knotenpunkte ermöglicht eine Vereinfachung komplexer Verkehrsnetze, damit sie mittels der Graphentheorie untersucht werden können. Grundsätzlich können kurze Wege zwischen den Knotenpunkten das Ziel beim Aufbau eines Verkehrsnetzes sein. Es kann aber auch versucht werden, die Bau- und Unterhaltskosten des Verkehrsnetzes in einem gewissen Rahmen zu halten. Aus diesen Vorgaben ergibt sich dann, je nach dem was gewünscht wird, eine grob- oder feinmaschige Struktur des Netzes. Ziel ist aber ein möglichst wirtschaftliches und optimales Verkehrsnetz. -\subsection{Suchalgorithmen} +\section{Suchalgorithmen} +Inbesondere bei Graphen in Form von Verkehrsnetzen ist das Finden eines kürzesten Weges von Interesse. Mathematisch betrachtet handelt es sich hierbei um ein Optimierungsproblem, bei dem die Summe der Kantengewichte zwischen zwei Knoten minimiert werden soll. Zu diesem Zweck existieren verschiedene Suchalgorithmen. In den folgenden Abschnitten wird auf eine Auswahl davon eingegangen. Zuvor ist es jedoch notwendig, einige Begriffe und Eigenschaften von Suchalgorithmen zu definieren. -\subsubsection{Dijkstra-Algorithmus} -Der Algorithmus von Dijkstra ist benannt nach seinem Erfinder dem Mathematik- und Infomratikprofessor Edsger Dijkstra. Den Algorithmus hat er im Jahr 1959 erfunden. -Der Algorithmus von Dijkstra ist ein Greedy-Algorithmus (gieriger Algorithmus), der schrittweise einen Folgezustand auswählt, damit beim Zeitpunkt der Wahl der grösste Gewinn bzw. das beste Ergebnis erzielt werden kann. -Trotz der Schnelligkeit der Greedy-Algorithmen, können viele Probleme nicht optimal gelöst werden. -Vereinfacht wird beim Dijkstra-Algorithmus, ausgehend von einem Startknoten so lange dem kürzesten Pfad gefolgt, bis der Zielknoten erreicht wird. Dabei muss für jeden besuchten Knoten die Kostenfunktion als auch der Pfad dahin (vorheriger Knoten) gespeichert werden. -Dadurch wird hingegen garantiert, dass, wenn der Zielknoten erreicht wird, auch der kürzeste Pfad gefunden wurde. -Grundlegende Voraussetzung für den Dijkstra-Algorithmus ist die strikte Positivität der Kantengewichte. Andernfalls würde ein wiederholtes Ablaufen einer Kante mit negativem Gewicht zu einer stetigen Reduktion der Kostenfunktion führen, was zu einer unendlichen Schlaufe führen würde. +Einerseits wird zwischen optimalen und nicht-optimalen Algorithmen unterschieden. Ein Suchalgorithmus gilt als optimal, falls er einen günstigsten Pfad zwischen zwei Knoten findet. Es gilt zu beachten, dass im Falle des Vorhandenseins von mehrerern Pfaden mit identischer, minimaler Summe der Kantengewichte zwischen zwei Knoten, mindestens einer dieser Pfade gefunden wird. -Gegeben sei ein Netzwerk mit $n$ Knoten und dem Startknoten $a$. -Alle Kanten sind mit $k(i, j)$ bewertet. -Gesucht wird der kürzeste Pfad zwischen dem Startknoten und allen übrigen Knoten im Netz. -$D(i)$ ist die kürzeste Distanz vom Startknoten $a$ zum Knoten $i, V(i)$ ist der unmittelbare Vorgängerknoten vom Knoten $i$ auf dem kürzesten Weg vom Startknoten $a$ zum Konten $i$ und die Menge $M$ ist die Menge einer bestimmten Auswahl an Knoten. +Weiter wird zwischen informierten und uninformierten Algorithmen differenziert. Während uninformierte Suchalgorithmen den Suchraum schematisch auf Basis der Eigenschaften des Graphen absuchen, bis eine günstigste Lösung gefunden wurde, verwenden informierte Suchalgorithmen eine Heuristik zur Abschätzung der Suchrichtung. Oftmals wird bei informierten Algorithmen ein Verlust der Optimalität zugunsten einer verbesserten Rechenzeit in Kauf genommen. Es exisitieren jedoch auch Heurstiken, die eine optimale Lösung gewährleisten. -Dabei gilt -\begin{equation}M={a}\end{equation} -\begin{equation}D(a)=0\end{equation} wobei -\begin{equation}D(i)=\infty\end{equation} und -\begin{equation}i \neq a \end{equation} -Ausserdem gilt \begin{equation}V(i)=(-) \text{für alle Knoten $i$}\end{equation}\\ +Eine besondere Art von Suchalgorithmen stellen die sogenannten Greedy-Algorithmen, zu deutsch gierige Algorithmen, dar. Sie zeichnen sich dadurch aus, dass sie stets den zurzeit günstigsten Folgezustand auswählen. Dadurch sind sie in der Regel äusserst effizient, garantieren bei vielen Problemstellungen jedoch keine optimale Lösung. -%THEORIE... -Iteration +\subsection{Dijkstra-Algorithmus} +Der Algorithmus von Dijkstra ist benannt nach seinem Erfinder dem Mathematik- und Informatikprofessor Edsger Dijkstra. Er gehört zur Klasse der uninformierten Greedy-Algorithmen. Zudem ist die Optimalität bei strikt positiven Kantengewichten gewährleistet. +Vorteilhaft ist die einfache Implementierung. Abhängig von der Programmiersprache sind zwischen 30 und 40 Zeilen an Code ausreichend, damit er den kürzesten Pfad zwischen einem Startknoten $a$ und Zielknoten $b$ finden kann. -1. Auswahl eines Knotens \begin{equation} K\in M \text{mit} D(K)=D(i);i\in M\end{equation} +Die für dieses Paper verwendete programmierte Funktion (MATLAB) verwendet eine abgewandelte Form der gewichteten Adjazenz-Matrix $A$, für welche gilt: +Der Matrix-Eintrag $A_{i,j}$ enthält das Kantengewicht der Kante von Knoten $j$ nach $i$ auf. Falls keine Kante zwischen $j$ und $i$ vorhanden ist, beträgt der Eintrag $\infty$. Dies vereinfacht die Implementierung zur Bestimmung des nächst-günstigsten Pfades. +Zudem werden zwei Hilfs-Vektoren $\vec{d}$ und $\vec{b}$ der Länge $n$ eingeführt, wobei $n$ die Anzahl Knoten des Graphen ist. Im Vektoreintrag $\vec{d}(i)$ wird das kummulierte Kantengewicht zur Erreichung von Knoten $i$ vom Startknoten $a$ gespeichert. Der Eintrag $\vec{d}(a)$ beträgt somit $0$. Im Vektor $\vec{b}$ wird zudem vermerkt, falls ein Knoten bereits als Ziel eines kürzesten Pfads gefunden wurde und somit für die weitere Suche nicht mehr berücksichtigt werden muss ($\vec{b}(i)=1$, sonst $\vec{b}(i)=0$). -2. Für alle Nachfolger $N(j)$ vom Knoten $K$ gilt: -\begin{equation}D(K) + k_Kj < D(j)\end{equation} dann wird \begin{equation}D(j) = D(K) + k_Kj, V(j) = K\end{equation} gesetzt und somit wird der Knoten $j$ in die Menge $M$ aufgenommen. +Ausgehend vom Startknoten $a$ wird nun anhand der Matrix $A$ in der Spalte $a$ nach dem kleinsten Eintrag gesucht. Somit wird der Folgeknoten $c$ gefunden. Dieser Vorgang wird nun wiederholt, wobei jedoch sämtliche von Knoten $a$ und $c$ erreichbaren Knoten berücksichtigt werden, die noch nicht besucht wurden. In anderen Worten alle nicht verschwindenden Einträge $i$ der Spalten $a$ und $c$ der Matrix $A$, für welche gilt $\vec{b}(i)=0$. Ausschlaggebend für die folgende Auswahl ist die Summe der kummulierten Kantengewichte und des Kantengewichts des nächsten Knotens. Als Beispiel zur Erreichung von Knoten $k$ über Knoten $j$: +\begin{equation} +\vec{d}(k)=\vec{d}(j)+A(k,j) +\end{equation} +Diese Iteration wird solange durchgeführt, bis der Folgeknoten dem Zielknoten entspricht. -3. Der ausgewählte Knoten \begin{equation}K\in M\text{wird aus der Menge herausgelöscht}\end{equation}\\ -Diese drei Schritte werden so lange wiederholt bis gilt -\begin{equation}M=\{\}\end{equation} +\subsection{A*-Algorithmus} +Der A*-Algorithmus basiert auf dem Dijkstra-Algorithmus, verwendet jedoch eine Heuristik zur Abschätzung der günstigsten Suchrichtung. Somit handelt es sich um einen informierten Greedy-Algorithmus, der abhängig von der verwendeten Heuristik auch optimal sein kann. Er wurde von Peter Hart, Nils Nilsson und Bertram Raphael entwickelt. -\subsubsection{A*-Algorithmus} -Suchalgorithmen werden nach einfachen (uninformierte) und heuristischen (informierten) Algorithmen unterschieden. Während einfache Algorithmen den Suchraum intuitiv durchsuchen, beziehen heuristische Algorithmen Wissen über den Suchraum mit ein. -Der A*-Algorithmus geht auf seine Erfinder Peter Hart, Nils Nilsson und Bertram Raphael zurück, die den Algorithmus erstmals im Jahr 1968 beschrieben. -Der A*-Algorithmus ist ein heuristischer Suchalgorithmus, der den kürzesten Pfad zwischen zwei Knoten in einem Graphen mit positiven Kantengewichten berechnet. -Im Gegensatz zu einfachen Suchalgorithmen, wird beim A*-Algorithmus eine Schätzfunktion, die sogenannte Heuristik, verwendet. Dies ermöglicht ein zielgerichtetes Suchen und gleichzeitig wird die Laufzeit verringert. -Ausserdem findet der A*-Algorithmus immer eine optimale Lösung, sofern eine vorhanden ist. -Der A*-Algorithmus wird als Verallgemeinerung gehandhabt und gilt als Erweiterung des Dijkstra-Algorithmus. +\subsection{Anwendung A*-Algorithmus} +Wie oben erwähnt basiert der A*-Algorithmus auf dem Shortest-Path-Algorithmus von Dijkstra. Gemäss dem Algorihtmus von Dijkstra werden von einem Startknoten aus die jeweiligen Nachbarknoten, die Nachbarknoten der Nachbarknoten usw. verarbeitet. Die Kantengewichte werden dabei aufsummiert und die Priorität wird auf die Kante gelegt, die das geringste Gewicht aufweist. Mit diesem Verfahren wird sichergestellt, dass die erste gefundene Lösung auch eine optimale Lösung darstellt.\\ -\subsubsection{Anwendung A*-Algorithmus} -Wie oben erwähnt basiert der A*-Algorithmus auf dem Shortest-Path-Algorithmus von Dijkstra. Gemäss dem Algorihtmus von Dijkstra werden von einem Startknoten aus die jeweiligen Nachbarknoten, die Nachbarknoten der Nachbarknoten usw. verarbeitet. Die Kantengewichte werden dabei aufsummiert und die Priorität wird auf die Kante gelegt, die das geringste Gewicht aufweist. Mit diesem Verfahren wird sichergestellt, dass die erste gefundene Lösung auch die optimalste Lösung darstellt.\\ +Der A*-Algorithmus unterscheidet sich vom Dijkstra-Algorithmus dahingehend, dass bei der Auswahl des Folgeknotens, nicht nur die Summe der Kantengewichte $\vec{d}(j)+A(k,j)$, sondern zusätzlich die für jeden Knoten definierte Abschätzfunktion $f(k)$ hinzuaddiert wird. Dies passiert jedoch nur bei der \emph{Auswahl} des Folgeknotens. Der Wert von $f(k)$ wird nicht im Eintrag $\vec{d}(k)$ gespeichert. Somit wird gewährleistet, dass der gefundene Pfad, der Summe der Kantengewichte entspricht. Ein Beispiel dafür, wie eine Abschätzfunktion gebildet werden kann findet sich in Abschnitt \ref{sec:verkehr/euklidische} -Die Kantengewichte werden für jeden Knoten in Form einer Funktion dargestellt -\begin{equation}f(n)=g(n)\end{equation} mit -\begin{equation}g(n)=\text{Summe aller Kantengewichte vom Startknoten bis n}\end{equation}\\ -Der A*-Algorithmus erweitert die Vorgehensweise des Algorithmus von Dijkstra um die Heuristik $h(n)$, die für jeden Knoten $n$ die geschätzte Entfernung zum Zielknoten beschreibt. -Somit gilt: -\begin{equation}f(n)=g(n)+h(n)\end{equation}\\ -Wie auch der Algorithmus von Dijkstra findet der A*-Algorithmus die optimalste Lösung. +\subsection{Euklidische Heuristik} +\label{sec:verkehr/euklidische} +Bei Verkehrsnetzen ist die euklidische Distanz eine gängige und zuverlässige Heurstik. Dabei wird zu den effektiven Reisekosten zum aktuellen Knoten die euklidische Distanz bis zum Zielknoten hinzuaddiert. Dadurch wird die Kostenfunktion konsequent nie überschätzt. Dies stellt eine Voraussetzung an eine zulässige Heuristik dar. Unter Verwendung dieser Heuristik gilt der A*-Algorithmus als optimal. -\subsubsection{Floyd-Warshall-Algorithmus} -Der Floyd-Warshall-Algorithmus, auch Tripel-Algorithmus genannt, wurde erstmals im Jahr 1962 von seinen Namensgebern Robert Floyd und Stephen Warshall vorgestellt. -Der Floyd-Warshall-Algorithmus sucht kürzeste Wege innerhalb eines Graphen. Er ermittelt aber nicht nur die Distanz zwischen zwei Knoten, sondern berechnet die kürzesten Wege zwischen allen Knotenpaaren eines gewichteten Graphen. Somit werden die kürzesten , beziehungsweise die optimalsten Wege zwischen allen Paaren von Knoten berechnet. Der Floyd-Warhshall-Algrithmus kann ausserdem mit negativen Kantengewichten umgehen, sofern der Graph aber keinen negativen Kreis (Zyklus) aufweist. Ist dies der Fall, führt der Algorithmus zu einem falschen Ergebnis. -Ein Kreis (Zyklus) in einem Graphen ist ein Weg, bei dem Start- und Endpunkt den gleichen Knoten aufweisen. Dieser wird negativ, wenn die Summe der gewichteten Kanten kleiner als Null wird.\\ -Der Floyd-Warshall-Algorithmus besteht grundsätzlich aus Floyd's Berechnung der kürzesten Distanzen zwischen zwei Knoten und Warshall's Konstruktion der kürzesten Wege. Werden diese beiden Teilgebiete zusammengefügt, ergibt sich der Floyd-Warshall-Algorithmus. +Bei der euklidischen Heuristik wird die Abschätzfunktion $f(k)$ für jeden Knoten $k$ durch euklidische Distanz zum Zielknoten $b$ gebildet. +\begin{equation} +f(k)=\sqrt{(x_k-x_b)^2+(y_k-y_b)^2} +\end{equation} + +Was bei einem physischen Verkehrsnetz einfach zu bewältigen ist, da Koordinaten von Verkehrsnetzen zur Berechnung der Distanz verwendet werden können, ist bei virtuellen Netzwerken (z.B. Servernetzen) entweder nicht möglich, oder nicht relevant. Hier können hingegen andere Eigenschaften des Netzwerks verwendet werden, auf welche in diesem Paper nicht weiter eingegangen wird. -\subsubsection{Anwendung Floyd-Warshall-Algorithmus} +\subsection{Floyd-Warshall-Algorithmus} +Der Floyd-Warshall-Algorithmus, auch Tripel-Algorithmus genannt, wurde erstmals im Jahr 1962 von seinen Namensgebern Robert Floyd und Stephen Warshall vorgestellt. +Der Floyd-Warshall-Algorithmus sucht kürzeste Wege innerhalb eines Graphen. Er ermittelt aber nicht nur die Distanz zwischen zwei Knoten, sondern berechnet die kürzesten Wege zwischen allen Knotenpaaren eines gewichteten Graphen. Somit werden die günstigsten Wege zwischen allen Paaren von Knoten berechnet. Der Floyd-Warhshall-Algrithmus kann ausserdem mit negativen Kantengewichten umgehen, sofern der Graph keinen negativen Kreis (Zyklus) aufweist. Ein Kreis, sprich ein Weg mit identischem Start- und Zielknoten, ist negativ, falls die Summe der Kantengewichte des Weges kleiner als null ist. Ist dies der Fall, führt der Algorithmus zu einem falschen Ergebnis. -Wie oben erwähnt, besteht der Floyd-Warshall-Algorithmus aus dem Teil von Floyd zur Berechnung der kürzesten Pfade und dem Teil von Warshall zur Konstruktion der kürzesten Pfade. +\subsection{Anwendung Floyd-Warshall-Algorithmus} %THEORIE... -Als erstes wird eine Gewichtsmatrix $W$ mit den Matrixeinträgen $W[i, j]$ erstellt. +In einem ersten Schritt wird eine Gewichtsmatrix $W$ mit den Matrixeinträgen $W[i, j]$ erstellt. Der Algorithmus berechnet danach in einer Hauptschleife alle Knoten $k$ von 1 bis $n$. Dabei versucht er in jeder Iteration alle Wege von $i$ nach $j$ durch die Wege $(i, k)$ und $(k, j)$ zu verbessern. -Falls dieser mögliche Umweg zu einer Verbesserung führt, wird der Algorithmus aktualisiert. +Falls dieser mögliche Umweg zu einer Verbesserung führt, wird der entsprechende Eintrag aktualisiert. Die aktuelle Gewichtung der Pfade wird mit -\begin{equation}d[i, j]=min[d[i,j], d[i,k] + d[k,i]]\end{equation} +\begin{equation}d[i, j]=\min[d[i,j], d[i,k] + d[k,i]]\end{equation} ermittelt. -\subsubsection{Euklidische Heuristik} -Bei Verkehrsnetzen ist die euklidische Distanz eine gängige und zuverlässige Heurstik. Dabei wird zu den effektiven Reisekosten zum aktuellen Knoten die euklidische Distanz bis zum Zielknoten hinzuaddiert. Dadurch wird die Kostenfunktion konsequent nie überschätzt. Dies stellt eine Voraussetzung an eine zulässige Heuristik dar. -Was bei einem physischen Verkehrsnetz einfach zu bewältigen ist, da Koordinaten von Verkehrsnetzen zur Berechnung der Distanz verwendet werden können, ist bei virtuellen Netzwerken (z.B. Servernetzen) entweder nicht möglich, oder nicht relevant. -\subsection{PageRank-Algorithmus} -Der PageRank-Algorithmus wurde von den Gründern von Google, Larry Page und Sergey Brin im Jahr 1996 entwickelt und zum Patent angemeldet. Zwei Jahre später gründeten sie ihr Unternehmen Google Inc.. -Beim PageRank-Algorithmus handelt es sich um den Algorithmus von Google, aus dem die Google-Matrix abgeleitet wird. -Die Google-Matrix ist eine immens grosse Matrix mit Millionen Zeilen und Spalten, die für die schnelle und vor allem exakte Bestimmung der PageRanks (Gewichtung) eine grosse Bedeutung hat. -Der PageRank-Algorithmus analysiert und gewichtet beispielsweise die Verlinkungsstruktur verschiedener Websites des World Wide Web anhand ihrer Struktur. -Der PageRank wird umso höher, je mehr hochwertige Links auf eine Webseite verweisen und je höher die Gewichtung einer Webseite ist, desto grösser ist der Effekt.\\ -Dabei handelt es sich um einen iterativen Prozess. Ausgegangen wird von der Adjazenz-Matrix $A$, für welche gilt. -%THEORIE... -Grundsätzlich setzt sich der PageRank Algorithmus mit der Fragestellung auseinander, wie eine Suchmaschine wie Google Suchresultate bewertet und somit sortieren soll. Öfters aufgerufene Resultate sollen schliesslich höher gewichtet werden. Dabei wird angenommen, dass eine Website populärer ist, je mehr andere Websites darauf verweisen. +\section{PageRank-Algorithmus} +Der PageRank-Algorithmus wurde von den Gründern von Google, Larry Page und Sergey Brin im Jahr 1996 entwickelt und zum Patent angemeldet. Zwei Jahre später gründeten sie ihr Unternehmen Google Inc. +Beim PageRank-Algorithmus handelt es sich nicht um einen Suchalgorithmus, stattdessen werden Knoten aufgrund der Vernetzung des vorliegenden Graphen bewertet. +Verwendet wird er beispielsweise um die Verlinkungsstruktur verschiedener Websites des World Wide Web anhand ihrer Struktur zu bewerten und relevante Suchergebnisse zu ermittteln. Der PageRank wird umso höher, je mehr hochwertige Links auf eine Webseite verweisen und je höher die Gewichtung einer Webseite ist, desto grösser ist der Effekt.\\ +Dabei handelt es sich um einen iterativen Prozess. Ausgegangen wird von der Adjazenz-Matrix $A$, für welche folgendes gilt: \begin{equation} A_{i,j}=\left\{ \begin{matrix} @@ -103,16 +79,20 @@ A_{i,j}=\left\{ \begin{matrix} \label{verkehr:Adja} \end{equation} +%THEORIE... +Grundsätzlich setzt sich der PageRank Algorithmus mit der Fragestellung auseinander, wie eine Suchmaschine wie Google Suchresultate bewertet und somit sortieren soll. Öfters aufgerufene Resultate sollen schliesslich höher gewichtet werden. Dabei wird angenommen, dass eine Website populärer ist, je mehr andere Websites darauf verweisen. + + -Für ungerichtete Graphen mit $n$ Knoten gilt \begin{equation}A_{i,j}=A_{j,i}\end{equation} und weiter \begin{equation}A_{i,i}=0\quad\forall i\in \left\{1...n\right\}\end{equation} +Für ungerichtete Graphen mit $n$ Knoten gilt \begin{equation}A_{i,j}=A_{j,i}\end{equation} und weiter \begin{equation}A_{i,i}=0\quad\forall i\in \left\{1\dots n\right\}\end{equation} Beim PageRank-Algorithmus wird eine abgewandelte Form der Adjazenz-Matrix verwendet. -Dabei werden die Matrix-Einträge spaltenweise durch die jeweilige Spaltensumme geteilt. -\begin{equation} P_{i,j}=\frac{A_{i,j}}{\sum_{i=1}^{n}A_{i,j}} \end{equation} +Dabei werden die Matrix-Einträge spaltenweise durch die jeweilige Spaltensumme geteilt: +\( P_{i,j}=\frac{A_{i,j}}{\sum_{i=1}^{n}A_{i,j}} \) Anschliessend multipliziert man diese Matrix $P$ mit einem Spaltenvektor $\Vec{r_0}$ mit $n$ Einträgen, für welchen gilt: -\begin{equation} \Vec{r_0}(i) = \frac{1}{n} \quad\forall i\in \left\{1...n\right\} \end{equation} +\( \Vec{r_0}(i) = \frac{1}{n} \quad\forall i\in \left\{1\dots n\right\} \) Dieser Vektor stellt ein neutrales Ranking dar. Alle Knoten werden gleich gewichtet. -Dadurch erhält man wiederum einen $n$-zeiligen Spaltenvektor $\Vec{r_1}$, der das "erste" Ranking darstellt. Durch Multiplikation der ursprünglichen Matrix $P$ mit dem 1. Ranking-Vektor $\Vec{r_1}$ wird auf Basis des ersten Rankings ein zweites erstellt. -\begin{equation} \Vec{r_2} = P\cdot\Vec{r_1} = P\cdot(P\cdot\Vec{r_0}) = P^2\cdot\Vec{r_0}\end{equation} -somit -\begin{equation} \Vec{r_i} = P^i\cdot\Vec{r_0}\end{equation} -Der Vektor $\Vec{r_i}$ konvergiert zu einem Eigenvektor von $P$ und stellt das abschliessende Ranking dar. +Dadurch erhält man wiederum einen $n$-zeiligen Spaltenvektor $\Vec{r_1}$, der das ``erste'' Ranking darstellt. Durch Multiplikation der ursprünglichen Matrix $P$ mit dem 1. Ranking-Vektor $\Vec{r_1}$ wird auf Basis des ersten Rankings ein zweites erstellt: +\( \Vec{r_2} = P\cdot\Vec{r_1} = P\cdot(P\cdot\Vec{r_0}) = P^2\cdot\Vec{r_0}\) +und somit allgemein: +\( \Vec{r_i} = P^i\cdot\Vec{r_0}\) +Der Vektor $\Vec{r_i}$ konvergiert zu einem Eigenvektor von $P$ der das abschliessende Ranking darstellt. diff --git a/buch/papers/verkehr/section2.tex b/buch/papers/verkehr/section2.tex index 638d9dd..527885e 100644 --- a/buch/papers/verkehr/section2.tex +++ b/buch/papers/verkehr/section2.tex @@ -1,12 +1,12 @@ \section{Versuchsreihe} \label{section:verkehr/versuchsreihe} -Um zwei der vorgestellten Suchalgorithmen zu vergleichen, wurden zwei Versuchsreihen erstellt. Dazu wurden in einem ersten Schritt zufällige Netzwerke generiert und anschliessend der \emph{Dijkstra}-, sowie der \emph{$A^*$}-Algorithmus auf das Netzwerk angewandt. -Dieser Vorgang wurde für die zufällig generierten Netzwerke mit einer Knotenzahl von 10, 20 50, 100, 200, 500 und 1000 je zehnmal repetiert. -Die Anzahl der Knoten im abgesuchten Netzwerk wirkt sich direkt auf die Rechenzeit aus. Der \emph{Dijkstra}-Algorithmus weist eine Zeitkomplexität von $\mathcal{O}(E\log{}V)$ auf, wobei $E$ die Anzahl Kanten (engl. \emph{edges}) und $V$ die Anzahl Knoten (engl. \emph{vertices}) darstellt. -Für den \emph{A*}-Algorithmus ist die Zeitkomplexität einerseits abhängig von der verwendeten Heuristik, andererseits aber auch vom vorliegenden Netzwerk selbst. Aus diesem Grund lässt sich keine defintive Angabe zu $\mathcal{O}$ machen. +Um zwei der vorgestellten Suchalgorithmen zu vergleichen, wurden zwei Versuchsreihen erstellt. Dazu wurden in einem ersten Schritt zufällige Netzwerke generiert und anschliessend der Dijkstra- und der A*-Algorithmus auf das Netzwerk angewandt. +Dieser Vorgang wurde für die zufällig generierten Netzwerke mit einer Knotenzahl von 10, 20 50, 100, 200, 500 und 1000 je zehnmal wiederholt. +Die Anzahl der Knoten im abgesuchten Netzwerk wirkt sich direkt auf die Rechenzeit aus. Der \emph{Dijkstra}-Algorithmus weist eine Zeitkomplexität von $\mathcal{O}(|E|\log{}|V|)$ auf, wobei $E$ die Menge der Kanten (engl. \emph{edges}) und $V$ die Menge der Knoten (engl. \emph{vertices}) des Graphen $G$ darstellt. +Für den A*-Algorithmus ist die Zeitkomplexität einerseits abhängig von der verwendeten Heuristik, andererseits aber auch vom vorliegenden Netzwerk selbst. Aus diesem Grund lässt sich keine definitive Angabe zur Zeitkomplexität machen. -Die beiden Versuchsreihen unterscheiden sich zudem dahingehend, dass der Start- und Zielknoten bei der ersten Versuchsreihe im Netzwerk diametral gegenüber liegen. Dadurch gehen viele Knoten verloren, welcher \emph{Dijkstra} als uninformierter Suchalgorithmus absuchen würde. In der zweiten Veruschsreihe werden hingegen Start- un Zielpunkt zufällig im Netzwerk ausgewählt. Es wird deshalb erwwartet, dass die Unterschiede in der Rechenzeit der beiden Algorithmen in der zweiten Versuchsreihe deutlich ausgeprägter sind. +Die beiden Versuchsreihen unterscheiden sich zudem dahingehend, dass der Start- und Zielknoten bei der ersten Versuchsreihe im Netzwerk diametral gegenüber liegen. Dadurch gehen viele Knoten verloren, welcher \emph{Dijkstra} als uninformierter Suchalgorithmus absuchen würde. In der zweiten Veruschsreihe werden hingegen Start- un Zielpunkt zufällig im Netzwerk ausgewählt. Es wird deshalb erwartet, dass die Unterschiede in der Rechenzeit der beiden Algorithmen in der zweiten Versuchsreihe deutlich ausgeprägter sind. \subsection{Einfluss der Knotenzahl auf die Rechenzeit} \label{verkehr:Knotenzahl} @@ -19,9 +19,9 @@ Die beiden Versuchsreihen unterscheiden sich zudem dahingehend, dass der Start- \label{verkehr:Vr1} \end{figure} -In \ref{verkehr:Vr1} ist ersichtlich, dass der Unterschied in der Rechenzeit zwischen \emph{Dijkstra} und \emph{A*} erst aber einer Knotenzahl von ca. $n=500$ merklich ansteigt. Dieses etwas überraschende Resultat ist darauf zurückzuführen, dass bei steigender Knotenzahl die Abweichung des effektiven kürzesten Pfades von der Distanz der Luftlinie abnimmt. +In \ref{verkehr:Vr1} ist ersichtlich, dass der Unterschied in der Rechenzeit zwischen Dijkstra und A* erst ab einer Knotenzahl von ca. $n=500$ merklich ansteigt. Dieses etwas überraschende Resultat ist darauf zurückzuführen, dass bei steigender Knotenzahl die Abweichung des effektiven kürzesten Pfades von der Distanz der Luftlinie abnimmt. Die Effektivität von \emph{A*} mit euklidischer Heuristik ist wiederum grösser, wenn die Abweichung des kürzesten Pfads von der Luftlinie minimal ist. -Bei Betrachtung von \ref{verkehr:pathDifference} wird dies ersichtlich, wobei die relative Abweichung erstaunlicherweise bei einer Knotenzahl von $n=100$ maximal ist und nach $n=500$ nur noch marginal abnimmt. +Abbildung \ref{verkehr:pathDifference} illustriert dies, wobei die relative Abweichung erstaunlicherweise bei einer Knotenzahl von $n=100$ maximal ist und nach $n=500$ nur noch marginal abnimmt. \begin{figure} \centering @@ -36,13 +36,13 @@ Bei Betrachtung von \ref{verkehr:pathDifference} wird dies ersichtlich, wobei di \begin{figure} \centering -\includegraphics[width=12cm]{papers/verkehr/figures/chart_Vr2.png}\\ +\includegraphics[width=12cm]{papers/verkehr/figures/chart_Vr2.png} \caption{Gemessene Rechenzeiten der zweiten Versuchsreihe in Abhängigkeit der Knotenzahl.} \label{verkehr:Vr2} \end{figure} -Zum Vergleich der Resultate in \ref{verkehr:Knotenzahl} zeigt \ref{verkehr:Vr2} die Rechenzeiten der zweiten Versuchsreihe, in welcher die Start- und Zielknoten zufällig im Netzwerk ausgewählt wurden. Einerseits ist eine reduzierte durchschnittliche Rechenzeit festzustellen, was schlicht daran liegt, dass die zufällige Wahl der Knoten dazu führt, dass diese tendenziell weniger weit auseinander liegen.\\ -Des weiteren ist festzustellen, dass sich die Unterschiede der Rechenzeiten zwischen \emph{Dijkstra} und \emph{A*} deutlich früher abzeichnen. Dieses Phänomen lässt sich leicht durch die zielgerichtete Suche des \emph{A*}-Algorithmus erklären. +Zum Vergleich der Resultate in Abschnitt \ref{verkehr:Knotenzahl} zeigt Abbildung \ref{verkehr:Vr2} die Rechenzeiten der zweiten Versuchsreihe, in welcher die Start- und Zielknoten zufällig im Netzwerk ausgewählt wurden. Einerseits ist eine reduzierte durchschnittliche Rechenzeit festzustellen, was daran liegt, dass die zufällige Wahl der Knoten dazu führt, dass diese tendenziell weniger weit auseinander liegen. +Des weiteren ist festzustellen, dass sich die Unterschiede der Rechenzeiten zwischen Dijkstra und A* deutlich früher abzeichnen. Dieses Phänomen lässt sich leicht durch die zielgerichtete Suche des A*-Algorithmus erklären. \begin{figure} \centering @@ -52,4 +52,4 @@ Des weiteren ist festzustellen, dass sich die Unterschiede der Rechenzeiten zwis \label{verkehr:Comparison} \end{figure} -In \ref{verkehr:Comparison} ist ersichtlich, dass bei einem im Netzwerk liegenden Startknoten die zielgerichtete Suche von \emph{A*} deutlich ausgeprägter zum Zuge kommt, als wenn dieser am Rand des Netzwerks liegen würde. +In Abbildung \ref{verkehr:Comparison} ist ersichtlich, dass bei einem im Netzwerk liegenden Startknoten die zielgerichtete Suche von \emph{A*} deutlich ausgeprägter zum Zuge kommt, als wenn dieser am Rand des Netzwerks liegen würde. diff --git a/buch/papers/verkehr/section3.tex b/buch/papers/verkehr/section3.tex index 99a0d92..9aa8ae4 100644 --- a/buch/papers/verkehr/section3.tex +++ b/buch/papers/verkehr/section3.tex @@ -1,8 +1,9 @@ \section{Ausblick} \subsection{Optimierungsprobleme bei Graphen} -Das Finden eines kürzesten Pfades, sprich die Minimierung der Summe der Kantengewichte, ist nur eines der Optimierungsprobleme, die sich im Bereich von Grafen aufstellen lassen. Verschiedene, ähnliche Problemstellungen lassen sich teilweise mit denselben Algorithmen lösen.\\ -Im Bereich vom Computernetzwerken könnte zum Beispiel die Minimierung der Knotenzahl zur Datenübbertragung von Interesse sein. Dabei lässt sich dieses Problem einfach dadurch lösen, dass dem \emph{Dijkstra}, oder dem \emph{A*}-Algorithmus anstelle der Graph-Matrix (mit Kantengewichten als Einträgen) die Adjazenz-Matrix als Argument übergeben wird. Der gefundene kürzeste Pfad enstpricht der Anzahl benutzter Kanten, bzw. der Anzahl besuchter Knoten. +Das Finden eines kürzesten Pfades, sprich die Minimierung der Summe der Kantengewichte, ist nur eines der Optimierungsprobleme, die sich im Bereich von Graphen aufstellen lassen. Verschiedene, ähnliche Problemstellungen lassen sich teilweise mit denselben Algorithmen lösen. + +Im Bereich vom Computernetzwerken könnte zum Beispiel die Minimierung der Knotenzahl zur Datenübbertragung von Interesse sein. Dabei lässt sich dieses Problem einfach dadurch lösen, dass dem Dijkstra- oder dem A*-Algorithmus anstelle der gewichteten Adjazenz-Matrix (mit Kantengewichten als Einträgen) die ungewichtet Adjazenz-Matrix als Argument übergeben wird. Der gefundene kürzeste Pfad enstpricht der Anzahl benutzter Kanten, bzw. der Anzahl besuchter Knoten. \subsection{Wahl der Heuristik} -Ein grundlegendes Problem bei der Anwendung des \emph{A*} oder ähnlicher informierter Suchalgorithmen ist die Wahl der Heurstik. Bei einem physischen Verkehrsnetz kann bspw. die euklidische Distanz problems ermittelt werde. Bei einem regionalen Netzwerk ist die Annahme eines orthogonalen X-Y-Koordinatenetzes absolut ausreichend. Dies gilt z.B. auch für das Vernessungsnetz der Schweiz\footnote{Die aktuelle Schweizer Referenzsystem LV95 benutzt ein E/N-Koordinatennetz, wobei aufgrund zunehmender Abweichung vom Referenzellipsoid bei grosser Entfernung vom Nullpunkt ein Korrekturfaktor für die Höhe angebracht werden muss.} Bei überregionalen Netzwerken (Beispiel: Flugverbindungen) ist hingegen eine Berechnung im dreidimensionalen Raum, oder vereinfacht als Projektion auf das Geoid notwendig. Anonsten ist der Ablauf bei der Ausführung des Algorithmus allerdings identisch.\\ +Ein grundlegendes Problem bei der Anwendung des A* oder ähnlicher informierter Suchalgorithmen ist die Wahl der Heurstik. Bei einem physischen Verkehrsnetz kann bspw. die euklidische Distanz problems ermittelt werde. Bei einem regionalen Netzwerk ist die Annahme eines orthogonalen X-Y-Koordinatenetzes absolut ausreichend. Dies gilt z.B. auch für das Vernessungsnetz der Schweiz\footnote{Die aktuelle Schweizer Referenzsystem LV95 benutzt ein E/N-Koordinatennetz, wobei aufgrund zunehmender Abweichung vom Referenzellipsoid bei grosser Entfernung vom Nullpunkt ein Korrekturfaktor für die Höhe angebracht werden muss.} Bei überregionalen Netzwerken (Beispiel: Flugverbindungen) ist hingegen eine Berechnung im dreidimensionalen Raum, oder vereinfacht als Projektion auf das Geoid notwendig. Anonsten ist der Ablauf bei der Ausführung des Algorithmus allerdings identisch. In nicht-physischen Netzwerken stellt sich jedoch eine zweite Problematik. Da eine physische Distanz entweder nicht ermittelt werden kann, oder aber nicht ausschlaggebend ist, sind andere Netzwerk-Eigenschaften zur Beurteilung beizuziehen. Die Zuverlässigkeit ist dabei aber in den meisten Fällen nicht vergleichbar hoch, wie bei der euklidischen Heuristik. Oftmals werden deshalb bei derartigen Problem auch Algorithmen angewendet, die eine deutlich optimierte Zeitkomplexität aufweisen, dafür aber nicht mit Sicherheit den effizienstesten Pfad finden. |
