A randomized intertial primal-dual fixed point algorithm for monotone inclusions

Abstract

In this paper, we propose a randomized intertial block-coordinate primaldual fixed point algorithm to solve a wide array of monotone inclusion problems base on the modification of the heavy ball method of Nesterov. These methods rely on a sweep of blocks of variables which are activated at each iteration according to a random rule. To this end we formulate the inertial version of the Krasnosel'skii-Mann algorithm for approximating the set of fixed points of a quasinonexpansive operator, for which we also provide an exhaustive convergence analysis. As a by-product, we can obtain some intertial block-coordinate operator splitting methods for solving composite monotone inclusion and convex minimization problems.

References in corpus (5)

• An inertial forward-backward algorithm for monotone inclusions
• Generalized Forward-Backward Splitting
• A Douglas-Rachford Splitting Method for Solving Equilibrium Problems
• Stochastic Quasi-Fejér Block-Coordinate Fixed Point Iterations with Random Sweeping
• A Class of Randomized Primal-Dual Algorithms for Distributed Optimization