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-rw-r--r--sumofsquares/expression.py137
1 files changed, 0 insertions, 137 deletions
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})"