HPR-LP: An implementation of an HPR method for solving linear programming

In this paper, we introduce an HPR-LP solver, an implementation of a Halpern Peaceman-Rachford (HPR) method with semi-proximal terms for solving linear programming (LP). The HPR method enjoys the iteration complexity of \(O(1/k)\) in terms of the Karush-Kuhn-Tucker residual and the objective error....

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Chen, Kaihuang, Sun, Defeng, Yancheng Yuan, Zhang, Guojun, Zhao, Xinyuan
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 22.08.2024
Subjects
Benchmarks
Complexity
Datasets
Design parameters
Linear programming
Parameter robustness
Solvers
Online AccessGet full text

Cover

More Information
Summary:In this paper, we introduce an HPR-LP solver, an implementation of a Halpern Peaceman-Rachford (HPR) method with semi-proximal terms for solving linear programming (LP). The HPR method enjoys the iteration complexity of \(O(1/k)\) in terms of the Karush-Kuhn-Tucker residual and the objective error. Based on the complexity results, we design an adaptive strategy of restart and penalty parameter update to improve the efficiency and robustness of the HPR method. We conduct extensive numerical experiments on different LP benchmark datasets using NVIDIA A100-SXM4-80GB GPU in different stopping tolerances. Our solver's Julia version achieves a \(\textbf{2.39x}\) to \(\textbf{5.70x}\) speedup measured by SGM10 on benchmark datasets with presolve (\(\textbf{2.03x}\) to \(\textbf{4.06x}\) without presolve) over the award-winning solver PDLP with the tolerance of \(10^{-8}\).
ISSN:2331-8422