We propose a geometric framework to describe and analyse a wide array of operator
splitting methods for solving monotone inclusion problems. The initial inclusion problem …

In this paper, the problem of computing the projection, and therefore the minimum distance,
from a point onto a Minkowski sum of general convex sets is studied. Our approach is based …

We briefly review the majorization–minimization (MM) principle and elaborate on the closely
related notion of proximal distance algorithms, a generic approach for solving constrained …

This paper addresses the task of estimating a covariance matrix under a patternless sparsity
assumption. In contrast to existing approaches based on thresholding or shrinkage …

The majorization-minimization (MM) principle is an extremely general framework for deriving
optimization algorithms. It includes the expectation-maximization (EM) algorithm, proximal …

The Hausdorff distance between two closed sets has important theoretical and practical
applications. Yet apart from finite point clouds, there appear to be no generic algorithms for …

We briefly review the majorization–minimization (MM) principle and elaborate on the closely
related notion of proximal distance algorithms, a generic approach for solving constrained …

Modeling dynamical systems combining prior physical knowledge and machinelearning
(ML) is promising in scientific problems when the underlying processesare not fully …

Clustering, a fundamental activity in unsupervised learning, is notoriously difficult when the
feature space is high-dimensional. Fortunately, in many realistic scenarios, only a handful of …

The current paper proposes and tests algorithms for finding the diameter of a compact
convex set and the farthest point in the set to another point. For these two nonconvex …