1 citations · 1 across the 26 of their papers we have counts for
27 papers
Stochastic Gradient Methods with Online Scaling
Wanyu Zhang, Wenzhi Gao, Yinyu Ye +1
This paper introduces Stochastic Online Scaled Gradient Methods (SOSGM), a generalization of the recently developed adaptive preconditioning framework in arXiv:2505.23081 and arXiv…
Tight Nonasymptotic Local Convergence of Sinkhorn-Knopp
Wenzhi Gao, Zhaonan Qu, Yinyu Ye +1
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…
OR-Space: A Full-Lifecycle Workspace Benchmark for Industrial Optimization Agents
Chenyu Zhou, Xinyun Lu, Jiangyue Zhao +3
Large language model (LLM) agents are increasingly used to assist with operations research (OR) modeling, yet existing OR-oriented benchmarks often reduce evaluation to one-shot tr…
OSDN: Improving Delta Rule with Provable Online Preconditioning in Linear Attention
Chenyu Zhou, Hongpei Li, Yuerou Liu +3
Linear attention and state-space models offer constant-memory alternatives to softmax attention, but often struggle with in-context associative recall. The Delta Rule mitigates thi…
A Technical Note on the Implementation and Use of PDCS
Zhenwei Lin, Zikai Xiong, Dongdong Ge +1
This technical note documents the implementation and use of the Primal-Dual Conic Programming Solver (PDCS), a first-order solver for large-scale conic optimization problems introd…
A Single-Sample Polylogarithmic Regret Bound for Nonstationary Online Linear Programming
Haoran Xu, Owen Shen, Peter Glynn +2
We study nonstationary Online Linear Programming (OLP), where orders arrive sequentially with reward-resource consumption pairs that form a sequence of independent, but not nec…
PDHCG-II: An Enhanced Version of PDHCG for Large-Scale Convex QP
Hongpei Li, Yicheng Huang, Huikang Liu +2
Quadratic programming (QP) is a fundamental optimization model with wide-ranging applications in decision-making and machine learning, yet efficiently solving large-scale instances…
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…
OptPipe: Memory- and Scheduling-Optimized Pipeline Parallelism for LLM Training
Hongpei Li, Han Zhang, Huikang Liu +2
Pipeline parallelism (PP) has become a standard technique for scaling large language model (LLM) training across multiple devices. However, despite recent progress in reducing memo…
FMIP: Joint Continuous-Integer Flow For Mixed-Integer Linear Programming
Hongpei Li, Hui Yuan, Han Zhang +4
Mixed-Integer Linear Programming (MILP) is a foundational tool for complex decision-making problems. However, the NP-hard nature of MILP presents a significant computational challe…
PDHCG: A Scalable First-Order Method for Large-Scale Competitive Market Equilibrium Computation
Huikang Liu, Yicheng Huang, Hongpei Li +2
Large-scale competitive market equilibrium problems arise in a wide range of important applications, including economic decision-making and intelligent manufacturing. Traditional s…
Solver-Informed RL: Grounding Large Language Models for Authentic Optimization Modeling
Yitian Chen, Jingfan Xia, Siyu Shao +2
Optimization modeling is fundamental to decision-making across diverse domains. Despite progress in automating optimization formulation from natural language descriptions, Large La…
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…
Beyond Regret: Decoupling Learning and Decision-making in Online Linear Programming
Wenzhi Gao, Dongdong Ge, Chenyu Xue +2
Online linear programming plays an important role in both revenue management and resource allocation, and recent research has focused on developing efficient first-order online lea…
A Robust Anchor-based Method for Multi-Camera Pedestrian Localization
Wanyu Zhang, Jiaqi Zhang, Dongdong Ge +4
This paper addresses the problem of vision-based pedestrian localization, which estimates a pedestrian's location using images and camera parameters. In practice, however, calibrat…
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 Single-Loop Robust Policy Gradient Method for Robust Markov Decision Processes
Zhenwei Lin, Chenyu Xue, Qi Deng +1
Robust Markov Decision Processes (RMDPs) have recently been recognized as a valuable and promising approach to discovering a policy with creditable performance, particularly in the…
Restarted Primal-Dual Hybrid Conjugate Gradient Method for Large-Scale Quadratic Programming
Yicheng Huang, Wanyu Zhang, Hongpei Li +3
Convex quadratic programming (QP) is an essential class of optimization problems with broad applications across various fields. Traditional QP solvers, typically based on simplex o…
A Low-Rank ADMM Splitting Approach for Semidefinite Programming
Qiushi Han, Chenxi Li, Zhenwei Lin +5
We introduce a new first-order method for solving general semidefinite programming problems, based on the alternating direction method of multipliers (ADMM) and a matrix-splitting…
Decoupling Learning and Decision-Making: Breaking the Barrier in Online Resource Allocation with First-Order Methods
Wenzhi Gao, Chunlin Sun, Chenyu Xue +2
Online linear programming plays an important role in both revenue management and resource allocation, and recent research has focused on developing efficient first-order online lea…
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…