English

Primal-dual extrapolation methods for monotone inclusions under local Lipschitz continuity

Optimization and Control 2024-09-04 v3 Machine Learning Numerical Analysis Numerical Analysis Machine Learning

Abstract

In this paper we consider a class of monotone inclusion (MI) problems of finding a zero of the sum of two monotone operators, in which one operator is maximal monotone while the other is {\it locally Lipschitz} continuous. We propose primal-dual extrapolation methods to solve them using a point and operator extrapolation technique, whose parameters are chosen by a backtracking line search scheme. The proposed methods enjoy an operation complexity of O(logϵ1){\cal O}(\log \epsilon^{-1}) and O(ϵ1logϵ1){\cal O}(\epsilon^{-1}\log \epsilon^{-1}), measured by the number of fundamental operations consisting only of evaluations of one operator and resolvent of the other operator, for finding an ε\varepsilon-residual solution of strongly and non-strongly MI problems, respectively. The latter complexity significantly improves the previously best operation complexity O(ε2){\cal O}(\varepsilon^{-2}). As a byproduct, complexity results of the primal-dual extrapolation methods are also obtained for finding an ε\varepsilon-KKT or ε\varepsilon-residual solution of convex conic optimization, conic constrained saddle point, and variational inequality problems under {\it local Lipschitz} continuity. We provide preliminary numerical results to demonstrate the performance of the proposed methods.

Keywords

Cite

@article{arxiv.2206.00973,
  title  = {Primal-dual extrapolation methods for monotone inclusions under local Lipschitz continuity},
  author = {Zhaosong Lu and Sanyou Mei},
  journal= {arXiv preprint arXiv:2206.00973},
  year   = {2024}
}

Comments

To appear in Mathematics of Operations Research

R2 v1 2026-06-24T11:37:02.854Z