New Primal-Dual Algorithm for Convex Problems

Abstract

Primal-dual algorithm (PDA) is a classic and popular scheme for convex-concave saddle point problems. It is universally acknowledged that the proximal terms in the subproblems about the primal and dual variables are crucial to the convergence theory and numerical performance of primal-dual algorithms. By taking advantage of the information from the current and previous iterative points, we exploit two new proximal terms for the subproblems about the primal and dual variables. Based on two new proximal terms, we present a new primal-dual algorithm for convex-concave saddle point problems with bilinear coupling terms and establish its global convergence and O(1/N ) ergodic convergence rate. When either the primal function or the dual function is strongly convex, we accelerate the above proposed algorithm and show that the corresponding algorithm can achieve O(1/N^2) convergence rate. Since the conditions for the stepsizes of the proposed algorithm are related directly to the spectral norm of the linear transform, which is difficult to obtain in some applications, we also introduce a linesearch strategy for the above proposed primal-dual algorithm and establish its global convergence and O(1/N ) ergodic convergence rate . Some numerical experiments are conducted on matrix game and LASSO problems by comparing with other state-of-the-art algorithms, which demonstrate the effectiveness of the proposed three primal-dual algorithms.