5 papers
The Boosted Double-Proximal Subgradient Algorithm for Nonconvex Optimization
Francisco J. Aragón-Artacho, Pedro Pérez-Aros, David Torregrosa-Belén
In this paper we introduce the Boosted Double-proximal Subgradient Algorithm (BDSA), a novel splitting algorithm designed to address general structured nonsmooth and nonconvex math…
Galerkin-like Method for Integro-Differential Inclusions with applications to Volterra Sweeping processes
Pedro Pérez-Aros, Manuel Torres-Valdebenito, Emilio Vilches
In this paper, we develop the Galerkin-like method to address first-order integro-differential inclusions. Under compactness or monotonicity conditions, we obtain new results for t…
Probability functions generated by set-valued mappings: a study of first order information
Wim van Ackooij, Pedro Pérez-Aros, Claudia Soto
Probability functions appear in constraints of many optimization problems in practice and have become quite popular. Understanding their first-order properties has proven useful, n…
Inner Moreau envelope of nonsmooth conic chance constrained optimization problems
Wim van Ackooij, Pedro Pérez-Aros, Claudia Soto +1
Optimization problems with uncertainty in the constraints occur in many applications. Particularly, probability functions present a natural form to deal with this situation. Nevert…
Coderivative-Based Semi-Newton Method in Nonsmooth Difference Programming
Francisco J. Aragón-Artacho, Boris S. Mordukhovich, Pedro Pérez-Aros
This paper addresses the study of a new class of nonsmooth optimization problems, where the objective is represented as a difference of two generally nonconvex functions. We propos…