Primal-dual extrapolation methods for monotone inclusions under local Lipschitz continuity with applications to variational inequality, conic constrained saddle point, and convex conic optimization problems

Abstract

In this paper we consider a class of structured monotone inclusion (MI) problems that consist of finding a zero in the sum of two monotone operators, in which one is maximal monotone while another is locally Lipschitz continuous. In particular, we first propose a primal-dual extrapolation (PDE) method for solving a structured strongly MI problem by modifying the classical forward-backward splitting method by using a point and operator extrapolation technique, in which the parameters are adaptively updated by a backtracking line search scheme. The proposed PDE method is almost parameterfree, equipped with a verifiable termination criterion, and enjoys an operation complexity of O log ε −1 , measured by the amount of fundamental operations consisting only of evaluations of one operator and resolvent of another operator, for finding an ε-residual solution of the structured strongly MI problem. We then propose another PDE method for solving a structured non-strongly MI problem by applying the above PDE method to approximately solve a sequence of structured strongly MI problems. The resulting PDE method is parameter-free, equipped with a verifiable termination criterion, and enjoys an operation complexity of O ε −1 log ε −1 for finding an ε-residual solution of the structured non-strongly MI problem. As a consequence, we apply the latter PDE method to convex conic optimization, conic constrained saddle point, and variational inequality problems, and obtain complexity results for finding an ε-KKT or ε-residual solution of them under local Lipschitz continuity. To the best of our knowledge, no prior studies were conducted to investigate methods with complexity guarantees for solving the aforementioned problems under local Lipschitz continuity. All the complexity results obtained in this paper are entirely new.