8 citations · 17 across the 15 of their papers we have counts for
11 papers · 1 filter
When Does Primal Interior Point Method Beat Primal-dual in Linear Optimization?
Wenzhi Gao, Huikang Liu, Yinyu Ye +1
The primal-dual interior point method (IPM) is widely regarded as the most efficient IPM variant for linear optimization. In this paper, we demonstrate that the improved stability…
Gradient Methods with Online Scaling
Wenzhi Gao, Ya-Chi Chu, Yinyu Ye +1
We introduce a framework to accelerate the convergence of gradient-based methods with online learning. The framework learns to scale the gradient at each iteration through an onlin…
Accelerating Low-Rank Factorization-Based Semidefinite Programming Algorithms on GPU
Qiushi Han, Zhenwei Lin, Hanwen Liu +4
In this paper, we address a long-standing challenge: how to achieve both efficiency and scalability in solving semidefinite programming problems. We propose breakthrough accelerati…
A Tuning-Free Primal-Dual Splitting Algorithm for Large-Scale Semidefinite Programming
Yinjun Wang, Haixiang Lan, Yinyu Ye
This paper proposes and analyzes a tuning-free variant of Primal-Dual Hybrid Gradient (PDHG), and investigates its effectiveness for solving large-scale semidefinite programming (S…
cuPDLP-C: A Strengthened Implementation of cuPDLP for Linear Programming by C language
Haihao Lu, Jinwen Yang, Haodong Hu +6
A recent GPU implementation of the Restarted Primal-Dual Hybrid Gradient Method for Linear Programming was proposed in Lu and Yang (2023). Its computational results demonstrate the…
Blessing of High-Order Dimensionality: from Non-Convex to Convex Optimization for Sensor Network Localization
Mingyu Lei, Jiayu Zhang, Yinyu Ye
This paper investigates the Sensor Network Localization (SNL) problem, which seeks to determine sensor locations based on known anchor locations and partially given anchors-sensors…