Peaceman-Rachford Splitting Method Converges Ergodically for Solving Convex Optimization Problems

2025/01/14 by Chen, Kaihuang, Sun, Defeng, Yuan, Yancheng +2
#90C05 #90C06 #90C25 #FOS: Mathematics #Optimization and Control (math.OC)

paper · doi:10.48550/arxiv.2501.07807

Abstract

In this paper, we prove that the ergodic sequence generated by the Peaceman-Rachford (PR) splitting method with semi-proximal terms converges for convex optimization problems (COPs). Numerical experiments on the linear programming benchmark dataset further demonstrate that, with a restart strategy, the ergodic sequence of the PR splitting method with semi-proximal terms consistently outperforms both the point-wise and ergodic sequences of the Douglas-Rachford (DR) splitting method. These findings indicate that the restarted ergodic PR splitting method is a more effective choice for tackling large-scale COPs compared to its DR counterparts.

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