On a fixed-point continuation method for a convex optimization problem

Jean-Baptiste Fest, Tommi Heikkilä, Ignace Loris, Ségolène Martin, Luca Ratti, Simone Rebegoldi, Gesa Sarnighausen

Published 2022-12-23Version 1

We consider a variation of the classical proximal-gradient algorithm for the iterative minimization of a cost function consisting of a sum of two terms, one smooth and the other prox-simple, and whose relative weight is determined by a penalty parameter. This so-called fixed-point continuation method allows one to approximate the problem's trade-off curve, i.e. to compute the minimizers of the cost function for a whole range of values of the penalty parameter at once. The algorithm is shown to converge, and a rate of convergence of the cost function is also derived. Furthermore, it is shown that this method is related to iterative algorithms constructed on the basis of the $\epsilon$-subdifferential of the prox-simple term. Some numerical examples are provided.