2 citations · 2 across the 10 of their papers we have counts for
11 papers · 1 filter
A proximal subgradient method for nonconvex stochastic optimization under the Kurdyka-Łojasiewicz condition
Felipe Atenas, Alejandro Jofré, Pedro Pérez-Aros +1
This work introduces a proximal stochastic subgradient method for minimizing the sum of an expected cost, whose integrand is potentially nonsmooth and nonconvex, and a lower semico…
Nonmonotone subgradient methods based on a local descent lemma
Francisco J. Aragón-Artacho, Rubén Campoy, Pedro Pérez-Aros +1
In this paper we present a nonmonotone line search subgradient algorithm tailored to upper- functions. This is a family of nonsmooth and nonconvex functions that sat…
Optimal Diagonal Preconditioning Beyond Worst-Case Conditioning: Theory and Practice of Omega Scaling
Saeed Ghadimi, Woosuk L. Jung, Arnesh Sujanani +2
We study optimal diagonal preconditioning using the classical worst-case -condition number and the averaging-based -condition number. For the -optimal preconditioning prob…
Randomized block proximal method with locally Lipschitz continuous gradient
Pedro Pérez-Aros, David Torregrosa-Belén
Block-coordinate algorithms are recognized to furnish efficient iterative schemes for addressing large-scale problems, especially when the computation of full derivatives entails s…
Projection, Degeneracy, and Singularity Degree for Spectrahedra
Haesol Im, Woosuk L. Jung, David Torregrosa-Belén +1
Facial reduction, FR, is a regularization technique for convex programs where the strict feasibility constraint qualification, CQ, fails.Though this CQ holds generically, failure i…
Approaches to iterative algorithms for solving nonlinear equations with an application in tomographic absorption spectroscopy
F. J. Aragón-Artacho, W. Cai, Y. Censor +3
In this paper we propose an approach for solving systems of nonlinear equations without computing function derivatives. Motivated by the application area of tomographic absorption…
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…
The superiorization method with restarted perturbations for split minimization problems with an application to radiotherapy treatment planning
Francisco J. Aragón-Artacho, Yair Censor, Aviv Gibali +1
In this paper we study the split minimization problem that consists of two constrained minimization problems in two separate spaces that are connected via a linear operator that ma…
A primal-dual splitting algorithm for composite monotone inclusions with minimal lifting
Francisco J. Aragón-Artacho, Radu I. Boţ, David Torregrosa-Belén
In this work, we study resolvent splitting algorithms for solving composite monotone inclusion problems. The objective of these general problems is finding a zero in the sum of max…
Distributed Forward-Backward Methods for Ring Networks
Francisco J. Aragón-Artacho, Yura Malitsky, Matthew K. Tam +1
In this work, we propose and analyse forward-backward-type algorithms for finding a zero of the sum of finitely many monotone operators, which are not based on reduction to a two o…
A direct proof of convergence of Davis-Yin splitting algorithm allowing larger stepsizes
Francisco J. Aragón-Artacho, David Torregrosa-Belén
This note is devoted to the splitting algorithm proposed by Davis and Yin in 2017 for computing a zero of the sum of three maximally monotone operators, with one of them being coco…