import numpy as np
from PEPit.function import Function
[docs]class ConvexIndicatorFunction(Function):
"""
The :class:`ConvexIndicatorFunction` class overwrites the `add_class_constraints` method of :class:`Function`,
implementing interpolation constraints for the class of closed convex indicator functions.
Attributes:
D (float): upper bound on the diameter of the feasible set, possibly set to np.inf
Convex indicator functions are characterized by a parameter `D`, hence can be instantiated as
Example:
>>> from PEPit import PEP
>>> from PEPit.functions import ConvexIndicatorFunction
>>> problem = PEP()
>>> func = problem.declare_function(function_class=ConvexIndicatorFunction, D=1)
References:
`[1] A. Taylor, J. Hendrickx, F. Glineur (2017).
Exact worst-case performance of first-order methods for composite convex optimization.
SIAM Journal on Optimization, 27(3):1283–1313.
<https://arxiv.org/pdf/1512.07516.pdf>`_
"""
def __init__(self,
D=np.inf,
is_leaf=True,
decomposition_dict=None,
reuse_gradient=False):
"""
Args:
D (float): Diameter of the support of self.
is_leaf (bool): True if self is defined from scratch.
False if self is defined as linear combination of leaf.
decomposition_dict (dict): Decomposition of self as linear combination of leaf :class:`Function` objects.
Keys are :class:`Function` objects and values are their associated coefficients.
reuse_gradient (bool): If True, the same subgradient is returned
when one requires it several times on the same :class:`Point`.
If False, a new subgradient is computed each time one is required.
"""
super().__init__(is_leaf=is_leaf,
decomposition_dict=decomposition_dict,
reuse_gradient=reuse_gradient)
# Store the diameter D in an attribute
self.D = D
[docs] def add_class_constraints(self):
"""
Formulates the list of interpolation constraints for self (closed convex indicator function),
see [1, Theorem 3.6].
"""
for i, point_i in enumerate(self.list_of_points):
xi, gi, fi = point_i
for j, point_j in enumerate(self.list_of_points):
xj, gj, fj = point_j
if point_i == point_j:
self.list_of_class_constraints.append(fi == 0)
else:
self.list_of_class_constraints.append(gi * (xj - xi) <= 0)
if self.D != np.inf:
self.list_of_class_constraints.append((xi - xj) ** 2 <= self.D ** 2)