Source code for cvxpy.transforms.indicator

Copyright 2017 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 typing import List, Tuple

import numpy as np

import cvxpy.utilities.performance_utils as perf
from cvxpy.constraints.constraint import Constraint
from cvxpy.expressions.expression import Expression

[docs]class indicator(Expression): """An expression representing the convex function I(constraints) = 0 if constraints hold, +infty otherwise. Parameters ---------- constraints : list A list of constraint objects. err_tol: A numeric tolerance for determining whether the constraints hold. """ def __init__(self, constraints: List[Constraint], err_tol: float = 1e-3) -> None: self.args = constraints self.err_tol = err_tol super(indicator, self).__init__() @perf.compute_once def is_constant(self) -> bool: """The Indicator is constant if all constraints have constant args. """ all_args = sum([c.args for c in self.args], []) return all([arg.is_constant() for arg in all_args]) def is_convex(self) -> bool: """Is the expression convex? """ return True def is_concave(self) -> bool: """Is the expression concave? """ return False def is_log_log_convex(self) -> bool: return False def is_log_log_concave(self) -> bool: return False def is_nonneg(self) -> bool: """Is the expression positive? """ return True def is_nonpos(self) -> bool: """Is the expression negative? """ return False def is_imag(self) -> bool: """Is the Leaf imaginary? """ return False def is_complex(self) -> bool: """Is the Leaf complex valued? """ return False def get_data(self) -> List[float]: """Returns info needed to reconstruct the expression besides the args. """ return [self.err_tol] @property def shape(self) -> Tuple[int, ...]: """Returns the (row, col) dimensions of the expression. """ return () def name(self) -> str: """Returns the string representation of the expression. """ return f"Indicator({self.args})" def domain(self) -> List[Constraint]: """A list of constraints describing the closure of the region where the expression is finite. """ return self.args @property def value(self) -> float: """Returns the numeric value of the expression. Returns: A numpy matrix or a scalar. """ if all(cons.value() for cons in self.args): return 0.0 else: return np.infty @property def grad(self): """Gives the (sub/super)gradient of the expression w.r.t. each variable. Matrix expressions are vectorized, so the gradient is a matrix. None indicates variable values unknown or outside domain. Returns: A map of variable to SciPy CSC sparse matrix or None. """ # TODO raise NotImplementedError()