9 papers
GPU-Accelerated Preprocessing for Large-Scale Linear Programming
Kaihuang Chen, Jiawei Lu, Defeng Sun +4
GPU-accelerated first-order methods are increasingly effective for solving large-scale linear programs (LPs), yet LP preprocessing remains predominantly CPU-based and can become co…
ADMM Fails to Achieve an Ergodic KKT Residual Bound
Kaihuang Chen, Defeng Sun, Yancheng Yuan +2
The Karush--Kuhn--Tucker (KKT) residual is a fundamental measure of first-order optimality and, under an error bound condition, is comparable to the distance to the KKT solution se…
dHPR: A Distributed Halpern Peaceman--Rachford Method for Non-smooth Distributed Optimization Problems
Zhangcheng Feng, Defeng Sun, Yancheng Yuan +1
This paper introduces the distributed Halpern Peaceman--Rachford (dHPR) method, an efficient algorithm for solving distributed convex composite optimization problems with non-smoot…
On the Relationships among GPU-Accelerated First-Order Methods for Solving Linear Programming
Kaihuang Chen, Defeng Sun, Yancheng Yuan +2
This paper aims to understand the relationships among recently developed GPU-accelerated first-order methods (FOMs) for linear programming (LP), with particular emphasis on HPR-LP…
HPR-QP: A dual Halpern Peaceman-Rachford method for solving large-scale convex composite quadratic programming
Kaihuang Chen, Defeng Sun, Yancheng Yuan +2
In this paper, we introduce HPR-QP, a dual Halpern Peaceman-Rachford (HPR) method designed for solving large-scale convex composite quadratic programming. One distinctive feature o…
Peaceman-Rachford Splitting Method Converges Ergodically for Solving Convex Optimization Problems
Kaihuang Chen, Defeng Sun, Yancheng Yuan +2
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…
HPR-LP: An implementation of an HPR method for solving linear programming
Kaihuang Chen, Defeng Sun, Yancheng Yuan +2
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 me…
HOT: An Efficient Halpern Accelerating Algorithm for Optimal Transport Problems
Guojun Zhang, Zhexuan Gu, Yancheng Yuan +1
This paper proposes an efficient HOT algorithm for solving the optimal transport (OT) problems with finite supports. We particularly focus on an efficient implementation of the HOT…
Accelerating preconditioned ADMM via degenerate proximal point mappings
Defeng Sun, Yancheng Yuan, Guojun Zhang +1
In this paper, we aim to accelerate a preconditioned alternating direction method of multipliers (pADMM), whose proximal terms are convex quadratic functions, for solving linearly…