import numpy as np
from PEPit.function import Function
[docs]
class SmoothStronglyConvexFunction(Function):
"""
The :class:`SmoothStronglyConvexFunction` class overwrites the `add_class_constraints` method of :class:`Function`,
by implementing interpolation constraints of the class of smooth strongly convex functions.
Attributes:
mu (float): strong convexity parameter
L (float): smoothness parameter
Smooth strongly convex functions are characterized by parameters :math:`\\mu` and :math:`L`,
hence can be instantiated as
Example:
>>> from PEPit import PEP
>>> from PEPit.functions import SmoothStronglyConvexFunction
>>> problem = PEP()
>>> func = problem.declare_function(function_class=SmoothStronglyConvexFunction, mu=.1, L=1.)
References:
`[1] A. Taylor, J. Hendrickx, F. Glineur (2017).
Smooth strongly convex interpolation and exact worst-case performance of first-order methods.
Mathematical Programming, 161(1-2), 307-345.
<https://arxiv.org/pdf/1502.05666.pdf>`_
"""
def __init__(self,
mu,
L,
is_leaf=True,
decomposition_dict=None,
reuse_gradient=True,
name=None):
"""
Args:
mu (float): The strong convexity parameter.
L (float): The smoothness parameter.
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.
name (str): name of the object. None by default. Can be updated later through the method `set_name`.
Note:
Smooth strongly convex functions are necessarily differentiable, hence `reuse_gradient` is set to True.
"""
super().__init__(is_leaf=is_leaf,
decomposition_dict=decomposition_dict,
reuse_gradient=True,
name=name,
)
# Store mu and L
self.mu = mu
self.L = L
if self.L == np.inf:
print("\033[96m(PEPit) Smooth strongly convex functions are necessarily differentiable. To instantiate\n"
"a strongly convex function, please avoid using the class SmoothStronglyConvexFunction with\n"
"L == np.inf. Instead, please use the class StronglyConvexFunction (which accounts for the fact\n"
"that there might be several sub-gradients at the same point).\033[0m")
[docs]
def set_smoothness_strong_convexity_constraint_i_j(self,
xi, gi, fi,
xj, gj, fj,
):
"""
Formulates the list of interpolation constraints for self (smooth strongly convex function).
"""
# Interpolation conditions of smooth strongly convex functions class
constraint = (fi - fj >=
gj * (xi - xj)
+ 1 / (2 * self.L) * (gi - gj) ** 2
+ self.mu / (2 * (1 - self.mu / self.L)) * (
xi - xj - 1 / self.L * (gi - gj)) ** 2)
return constraint
[docs]
def add_class_constraints(self):
"""
Add class constraints.
"""
self.add_constraints_from_two_lists_of_points(list_of_points_1=self.list_of_points,
list_of_points_2=self.list_of_points,
constraint_name="smoothness_strong_convexity",
set_class_constraint_i_j=
self.set_smoothness_strong_convexity_constraint_i_j,
)