16 papers · 1 filter
Tight Nonasymptotic Local Convergence of Sinkhorn-Knopp
Wenzhi Gao, Zhaonan Qu, Yinyu Ye +2
We revisit the Sinkhorn-Knopp (SK) algorithm for the matrix scaling problem. Despite extensive literature on the global convergence of SK and its variants, its local linear converg…
GPU-Accelerated Conic Quadratic Programming with Local Linear Convergence under Strict Complementarity
Hongpei Li, Yicheng Huang, Huikang Liu +2
We present PDHCG-CQP, a GPU-accelerated first-order solver for large-scale conic convex quadratic programming. PDHCG-CQP supports affine constraints and Cartesian products of nonne…
A Curvature-Aware Rank-Adaptive Distributed Augmented-Lagrangian Solver for Large-Scale SDPs
Hongpei Li, Huikang Liu, Dongdong Ge +1
We present CARDAL (Curvature-Aware Rank-Adaptive Distributed Augmented Lagrangian), a distributed multi-GPU solver for large-scale semidefinite programs (SDPs) based on a rank-adap…
The Simple Strategy-Iteration Method is Strongly Polynomial for the Turn-Based Deterministic Forward Game
Sanyou Mei, Chunlin Sun, Yinyu Ye
We study Turn-Based Deterministic Forward Games (TBDFGs), the subclass of turn-based deterministic zero-sum games in which no directed cycle contains actions controlled by both pla…
D-PDLP: Scaling PDLP to Distributed Multi-GPU Systems
Hongpei Li, Yicheng Huang, Huikang Liu +2
We present a distributed framework of the Primal-Dual Hybrid Gradient (PDHG) algorithm for solving massive-scale linear programming (LP) problems. Although PDHG-based solvers demon…
A Practical GPU-Enhanced Matrix-Free Primal-Dual Method for Large-Scale Conic Programs
Zhenwei Lin, Zikai Xiong, Dongdong Ge +1
In this paper, we introduce a practical GPU-enhanced matrix-free first-order method for solving large-scale conic programming problems, which we refer to as PDCS, standing for the…