activity
20162020
collaborators

7 papers

math.OC2020

Strengthened Splitting Methods for Computing Resolvents

Francisco J. Aragón Artacho, Rubén Campoy, Matthew K. Tam

In this work, we develop a systematic framework for computing the resolvent of the sum of two or more monotone operators which only activates each operator in the sum individually.…

math.OC2019

The Douglas-Rachford Algorithm for Convex and Nonconvex Feasibility Problems

Francisco J. Aragón Artacho, Rubén Campoy, Matthew K. Tam

The Douglas-Rachford method, a projection algorithm designed to solve continuous optimization problems, forms the basis of a useful heuristic for solving combinatorial optimization…

math.OC2018

Computing the resolvent of the sum of maximally monotone operators with the averaged alternating modified reflections algorithm

F. J. Aragón Artacho, R. Campoy

The averaged alternating modified reflections algorithm is a projection method for finding the closest point in the intersection of closed convex sets to a given point in a Hilbert…

math.OC2018

The Cyclic Douglas-Rachford Algorithm with r-sets-Douglas-Rachford Operators

Francisco J. Aragón Artacho, Yair Censor, Aviv Gibali

The Douglas-Rachford (DR) algorithm is an iterative procedure that uses sequential reflections onto convex sets and which has become popular for convex feasibility problems. In thi…

math.OC2017

Optimal rates of linear convergence of the averaged alternating modified reflections method for two subspaces

Francisco J. Aragón Artacho, Rubén Campoy

The averaged alternating modified reflections (AAMR) method is a projection algorithm for finding the closest point in the intersection of convex sets to any arbitrary point in a H…

math.CO2017

A feasibility approach for constructing combinatorial designs of circulant type

Francisco J. Aragón Artacho, Rubén Campoy, Ilias Kotsireas +1

In this work, we propose an optimization approach for constructing various classes of circulant combinatorial designs that can be defined in terms of autocorrelations. The problem…