most citedFirst-Order Methods for Linear Programming

1 citations · 3 across the 6 of their papers we have counts for

collaborators

6 papers

cs.GT20241 cited

Auto-bidding and Auctions in Online Advertising: A Survey

Gagan Aggarwal, Ashwinkumar Badanidiyuru, Santiago R. Balseiro +23

In this survey, we summarize recent developments in research fueled by the growing adoption of automated bidding strategies in online advertising. We explore the challenges and opp…

math.OC2024

PDOT: a Practical Primal-Dual Algorithm and a GPU-Based Solver for Optimal Transport

Haihao Lu, Jinwen Yang

In this paper, we propose a practical primal-dual algorithm with theoretical guarantees and develop a GPU-based solver, which we dub PDOT, for solving large-scale optimal transport…

math.OC20241 cited

First-Order Methods for Linear Programming

Haihao Lu

Linear programming is the seminal optimization problem that has spawned and grown into today's rich and diverse optimization modeling and algorithmic landscape. This article provid…

math.OC20241 cited

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…

math.OC2023

On the Convergence of L-shaped Algorithms for Two-Stage Stochastic Programming

John R. Birge, Haihao Lu, Baoyu Zhou

In this paper, we design, analyze, and implement a variant of the two-loop L-shaped algorithms for solving two-stage stochastic programming problems that arise from important appli…

math.OC2023

On a Unified and Simplified Proof for the Ergodic Convergence Rates of PPM, PDHG and ADMM

Haihao Lu, Jinwen Yang

We present a unified viewpoint of proximal point method (PPM), primal-dual hybrid gradient (PDHG) and alternating direction method of multipliers (ADMM) for solving convex-concave…