From 8440098abb691c4d78925862e27397a428d1cbdd Mon Sep 17 00:00:00 2001
From: Nao Pross <np@0hm.ch>
Date: Sat, 25 May 2024 12:25:33 +0200
Subject: Delete obsolete expression extension module

---
 sumofsquares/expression.py | 137 ---------------------------------------------
 1 file changed, 137 deletions(-)
 delete mode 100644 sumofsquares/expression.py

diff --git a/sumofsquares/expression.py b/sumofsquares/expression.py
deleted file mode 100644
index 28f1794..0000000
--- a/sumofsquares/expression.py
+++ /dev/null
@@ -1,137 +0,0 @@
-from __future__ import annotations
-
-from abc import abstractmethod
-from typing import Iterable
-from typing_extensions import override
-from dataclassabc import dataclassabc
-
-from polymatrix.expression.expression import ExpressionBaseMixin
-from polymatrix.expression.from_ import from_statemonad
-from polymatrix.expressionstate import ExpressionState
-from polymatrix.statemonad import init_state_monad
-from polymatrix.polymatrix.mixins import PolyMatrixMixin
-
-from polymatrix.expression.init import (
-        init_concatenate_expr,
-        init_diag_expr,
-        init_lower_triangular_expr,
-        init_transpose_expr,
-        init_slice_expr)
-
-from .abc import Constraint
-from .constraints import PositiveSemidefinite, ExponentialCone
-from .error import SolverError
-from .variable import init_opt_variable_expr
-
-
-class SOSExpressionBaseMixin(ExpressionBaseMixin):
-    @override
-    def apply(self, state: ExpressionState) -> tuple[ExpressionState, ExpressionBaseMixin]:
-        raise SolverError(f"Expression containing {self.__class__.__qualname__} "
-                           "cannot be used directly, they need to be rewritten "
-                           "into an equivalent form using a canonicalization function.")
-
-    @abstractmethod
-    def recast(self) -> tuple[ExpressionBaseMixin, Iterable[Constraint]]:
-        """
-        Recast the expression into a form that can be directly used for
-        optimization, possibly by introducing new constraints.
-
-        The return values are the new expression to be minimized and the new
-        constraints that have to be added.
-        """
-
-
-class LogDetMixin(SOSExpressionBaseMixin):
-    """ Compute the sum of the logarithm of the eigenvalues. """
-
-    @property
-    @abstractmethod
-    def underlying(self) -> ExpressionBaseMixin:
-        """" Take the logdet of this expression """
-
-    @override
-    def recast(self) -> tuple[ExpressionBaseMixin, Iterable[Constraint]]:
-        # The problem
-        #
-        #   maximize logdet(A)
-        #
-        # is equivalent to solving
-        #
-        #   maximize      t
-        #
-        #   subject to    [ A    Z       ]
-        #                 [ Z.T  diag(Z) ] >= 0
-        #
-        #                 Z lower triangular
-        #                 t <= sum_i log(Z[i,i])
-        #
-        # and the last constraint of the above is equivalent to
-        #
-        #   t <= sum_i u[i]
-        #   u_i <= log(Z[i, i])  for all i
-        #
-        # And finally to get rid of the log the latter constraint one is
-        # equivalent to
-        #
-        #   (Z[i,i], 1, u[i]) in Exponential Cone for all i
-        #
-        # Hence we can replace the original problem with
-        #
-        #   minimize      - sum_i u[i]
-        #
-        #   subject to    [ A    Z       ]
-        #                 [ Z.T  diag(Z) ] >= 0
-        #
-        #                 Z lower triangular
-        #                 (Z[i,i], 1, u[i]) in ExpCone for all i
-
-        A = self.underlying
-
-        # FIXME: get rid of these functions, create ShapeExprMixin in polymatrix?
-        def make_u(state: ExpressionState) -> tuple[ExpressionState, PolyMatrixMixin]:
-            state, pm = A.apply(state)
-            n, m = pm.shape
-            if n != m:
-                raise ValueError(f"Matrix A of logdet(A) must be square, "
-                                 f"but it has shape {pm.shape}")
-
-            # FIXME: should check to avoid name clashes
-            u = init_opt_variable_expr("u_logdet", shape=(n, 1))
-            return u.apply(state)
-
-        def make_z(state: ExpressionState) -> tuple[ExpressionState, PolyMatrixMixin]:
-            state, pm = A.apply(state)
-            n, m = pm.shape
-            if n != m:
-                raise ValueError(f"Matrix A of logdet(A) must be square, "
-                                 f"but it has shape {pm.shape}")
-
-            # FIXME: should check to avoid name clashes
-            Z = init_lower_triangular_expr(init_opt_variable_expr("Z_logdet", shape=(n * (n + 1) // 2, 1)))
-            return Z.apply(state)
-
-        Z = from_statemonad(init_state_monad(make_z))
-        Z_T = init_transpose_expr(Z)
-        Z_diag = init_diag_expr(Z)
-
-        u = from_statemonad(init_state_monad(make_u))
-
-        # we call the new big matrix Q
-        Q = init_concatenate_expr(((A, Z), (Z_T, Z_diag)))
-
-        def make_expcones(state: ExpressionState) -> tuple[ExpressionState, tuple[Constraint]]:
-            pass
-
-
-        constraints = [PositiveSemidefinite(Q)]
-
-        raise NotImplementedError
-
-
-@dataclassabc(froze=True)
-class LogDetImpl(LogDetMixin):
-    underlying: ExpressionBaseMixin
-
-    def __str__(self):
-        return f"logdet({self.underlying})"
-- 
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