Four Operator Splitting via a Forward-Backward-Half-Forward Algorithm with Line Search

In this article we provide a splitting method for solving monotone inclusions in a real Hilbert space involving four operators: a maximally monotone, a monotone-Lipschitzian, a cocoercive, and a monotone-continuous operator. The proposed method takes advantage of the intrinsic properties of each operator, generalizing the forward-back-half forward splitting and the Tseng's algorithm with line-search. At each iteration, our algorithm defines the step-size by using a line search in which the monotone-Lipschitzian and the cocoercive operators need only one activation. We also derive a method for solving non-linearly constrained composite convex optimization problems in real Hilbert spaces. Finally, we implement our algorithm in a non-linearly constrained least-square problem, and we compare its performance with available methods in the literature.Comment: 17 page

A Perturbation Framework for Convex Minimization and Monotone Inclusion Problems with Nonlinear Compositions

We introduce a framework based on Rockafellar's perturbation theory to analyze and solve general nonsmooth convex minimization and monotone inclusion problems involving nonlinearly composed functions as well as linear compositions. Such problems have been investigated only from a primal perspective and only for nonlinear compositions of smooth functions in finite-dimensional spaces in the absence of linear compositions. In the context of Banach spaces, the proposed perturbation analysis leads to the construction of a dual problem and of a maximally monotone Kuhn--Tucker operator which is decomposable as the sum of simpler monotone operators. In the Hilbertian setting, this decomposition leads to block-iterative primal-dual proximal algorithms that fully split all the components of the problem and capture state-of-the-art existing methods as special cases