General Inexact Primal-Dual Hybrid Gradient Methods for Saddle-Point Problems and Convergence Analysis

Zhongming Wu () and Min Li
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Zhongming Wu: Research Center of Risk Management and Emergency, Decision Making, School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China
Min Li: School of Management and Engineering, Nanjing University, Nanjing 210093, P. R. China

Asia-Pacific Journal of Operational Research (APJOR), 2022, vol. 39, issue 05, 1-27

Abstract: In this paper, we focus on the primal-dual hybrid gradient (PDHG) method, which is being widely used to solve a broad spectrum of saddle-point problems. Despite of its wide applications in different areas, the study of inexact versions of PDHG still seems to be in its infancy. We investigate how to design implementable inexactness criteria for solving the subproblems in PDHG scheme so that the convergence of an inexact PDHG can be guaranteed. We propose two specific inexactness criteria and accordingly some inexact PDHG methods for saddle-point problems. The convergence of both inexact PDHG methods is rigorously proved, and their convergence rates are estimated under different scenarios. Moreover, some numerical results on image restoration problems are reported to illustrate the efficiency of the proposed methods.

Keywords: Saddle-point problems; convex programming; primal-dual hybrid gradient method; inexactness criterion; splitting methods; convergence rate (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0217595921500445

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