Source code for cvxpy.expressions.variable

Copyright 2013 Steven Diamond

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
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from __future__ import annotations

from typing import Any, Iterable

import scipy.sparse as sp

import cvxpy.lin_ops.lin_utils as lu
from cvxpy import settings as s
from cvxpy.expressions.leaf import Leaf

def upper_tri_to_full(n: int) -> sp.csc_matrix:
    """Returns a coefficient matrix to create a symmetric matrix.

    n : int
        The width/height of the matrix.

    SciPy CSC matrix
        The coefficient matrix.
    entries = n*(n+1)//2

    val_arr = []
    row_arr = []
    col_arr = []
    count = 0
    for i in range(n):
        for j in range(i, n):
            # Index in the original matrix.
            # Index in the filled matrix.
            row_arr.append(j*n + i)
            if i != j:
                # Index in the original matrix.
                # Index in the filled matrix.
                row_arr.append(i*n + j)
            count += 1

    return sp.csc_matrix((val_arr, (row_arr, col_arr)),
                         (n*n, entries))

[docs]class Variable(Leaf): """The optimization variables in a problem. """ def __init__( self, shape: int | Iterable[int, ...] = (), name: str | None = None, var_id: int | None = None, **kwargs: Any ): if var_id is None: = lu.get_id() else: = var_id if name is None: self._name = "%s%d" % (s.VAR_PREFIX, elif isinstance(name, str): self._name = name else: raise TypeError("Variable name %s must be a string." % name) self._variable_with_attributes: Variable | None = None self._value = None = None self.gradient = None super(Variable, self).__init__(shape, **kwargs)
[docs] def name(self) -> str: """str : The name of the variable.""" return self._name
def is_constant(self) -> bool: return False @property def grad(self) -> dict[Variable, sp.csc_matrix]: """Gives the (sub/super)gradient of the expression w.r.t. each variable. Matrix expressions are vectorized, so the gradient is a matrix. Returns: A map of variable to SciPy CSC sparse matrix or None. """ # TODO(akshayka): Do not assume shape is 2D. return {self: sp.eye(self.size).tocsc()} def variables(self) -> list[Variable]: """Returns itself as a variable. """ return [self] def canonicalize(self): """Returns the graph implementation of the object. Returns: A tuple of (affine expression, [constraints]). """ obj = lu.create_var(self.shape, return (obj, []) def attributes_were_lowered(self) -> bool: """True iff variable generated when lowering a variable with attributes. """ return self._variable_with_attributes is not None def set_variable_of_provenance(self, variable: Variable) -> None: assert variable.attributes self._variable_with_attributes = variable def variable_of_provenance(self) -> Variable | None: """Returns a variable with attributes from which this variable was generated.""" return self._variable_with_attributes def __repr__(self) -> str: """String to recreate the object. """ attr_str = self._get_attr_str() return f"Variable({self.shape}, {self.__str__()}{attr_str})"