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Added the support of convex indicator for feasible sets bounded by a radius #103

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Merged
bgoujaud merged 2 commits into from
Aug 7, 2025
Merged

Added the support of convex indicator for feasible sets bounded by a radius #103

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3500791
Added the support of convex indicator for feasible set bounded by a r…
Oct 30, 2024
c6bc146
Updated convex indicator to handle radius constraints with arbitrary …
Nov 4, 2024
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34 changes: 28 additions & 6 deletions PEPit/functions/convex_indicator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,23 +2,27 @@

from PEPit.function import Function


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
R (float): upper bound on the radius of the feasible set, possibly set to np.inf
center (Point): Center of the feasible set spanned by the radius constraint, possibly set to None.
Convex indicator functions are characterized by a parameter `D` (or `R`), hence can be instantiated as

Example:
>>> from PEPit import PEP
>>> from PEPit import Point
>>> from PEPit.functions import ConvexIndicatorFunction
>>> problem = PEP()
>>> func = problem.declare_function(function_class=ConvexIndicatorFunction, D=1)

>>> func1 = problem.declare_function(function_class=ConvexIndicatorFunction, D=1)
>>> func2 = problem.declare_function(function_class=ConvexIndicatorFunction, R=1)
>>> omega = Point()
>>> func3 = problem.declare_function(function_class=ConvexIndicatorFunction, R=1, center=omega)

References:

`[1] A. Taylor, J. Hendrickx, F. Glineur (2017).
Expand All @@ -30,6 +34,8 @@ class ConvexIndicatorFunction(Function):

def __init__(self,
D=np.inf,
R=np.inf,
center=None,
is_leaf=True,
decomposition_dict=None,
reuse_gradient=False,
Expand All @@ -38,6 +44,9 @@ def __init__(self,

Args:
D (float): Diameter of the support of self. Default value set to infinity.
R (float): Radius of the support of self. Default value set to infinity.
center: Center of the feasible set spanned by the radius constraint of self. Default value set to None.
If the value is None, the feasible set is centered on the origin.
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.
Expand All @@ -56,6 +65,10 @@ def __init__(self,

# Store the diameter D in an attribute
self.D = D
# Store the radius R in an attribute
self.R = R
# Store the center in an attribute
self.center = center

@staticmethod
def set_value_constraint_i(xi, gi, fi):
Expand Down Expand Up @@ -91,6 +104,15 @@ def set_diameter_constraint_i_j(self,
# Diameter constraint
constraint = ((xi - xj) ** 2 <= self.D ** 2)

# Radius constraint
if self.R < np.inf:
# No self.center provided centers the ball on the origin
if self.center is None:
constraint = ((xi)**2 <= self.R ** 2)
# Centering the ball on self.center
else:
constraint = ((self.center - xi)**2 <= self.R ** 2)

return constraint

def add_class_constraints(self):
Expand All @@ -108,7 +130,7 @@ def add_class_constraints(self):
constraint_name="convexity",
set_class_constraint_i_j=self.set_convexity_constraint_i_j,
)
if self.D != np.inf:
if (self.D != np.inf) or (self.R != np.inf):
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="diameter",
Expand Down