Haihao Lu.
Menu

Publications

Journal articles, conference papers, and papers under review.
You can also find my work on Google Scholar .

2026

  1. A New Crossover Algorithm for LP Inspired by the Spiral Dynamic of PDHG

    Tianhao Liu, Haihao Lu

    INFORMS Journal on Computing

    Paper arXiv

  2. A Practical and Optimal First-Order Method for Large-Scale Convex Quadratic Programming

    Haihao Lu, Jinwen Yang

    Mathematical Programming

    Paper

  3. On the Sparsity of Optimal Linear Decision Rules for a Class of Robust Optimization Problems with Box Uncertainty Sets

    Haihao Lu, Bradley Sturt

    Operations Research

    Paper

2025

  1. cuPDLP.jl: A GPU Implementation of Restarted Primal-Dual Hybrid Gradient for Linear Programming in Julia

    Haihao Lu, Jinwen Yang

    Operations Research

    Paper arXiv

  2. Optimizing Scalable Targeted Marketing Policies with Constraints

    Haihao Lu, Duncan Simester, Yuting Zhu

    Marketing Science

    Paper SSRN

  3. Regularized Online Allocation Problems: Fairness and Beyond

    Santiago Balseiro, Haihao Lu, Vahab Mirrokni

    Manufacturing and Service Operations Management

    Paper arXiv Supplemental Material Conference Paper

  4. On the Geometry and Refined Rate of Primal-dual Hybrid Gradient for Linear Programming

    Haihao Lu, Jinwen Yang

    Mathematical Programming

    Paper

  5. cuPDLPx: A Further Enhanced GPU-Based First-Order Solver for Linear Programming

    Haihao Lu, Zedong Peng, Jinwen Yang

    Under review

    arXiv

  6. An Overview of GPU-based First-Order Methods for Linear Programming and Extensions

    Haihao Lu, Jinwen Yang

    Under review

    arXiv

  7. New Understandings and Computation on Augmented Lagrangian Methods for Low-Rank Semidefinite Programming

    Lijun Ding, Haihao Lu, Jinwen Yang

    Under review

    arXiv

  8. PDLP: A Practical First-Order Method for Large-Scale Linear Programming

    David Applegate, Mateo Díaz, Oliver Hinder, Haihao Lu, Miles Lubin, Brendan O'Donoghue, Warren Schudy

    Under review

    arXiv

2024

  1. Auto-bidding and Auctions in Online Advertising: A Survey

    Gagan Aggarwal, Ashwinkumar Badanidiyuru, Santiago R. Balseiro, et al.

    ACM SIGecom Exchanges

    Paper

  2. A Field Guide for Pacing Budget and ROS Constraints

    Santiago R. Balseiro, Kshipra Bhawalkar, Zhe Feng, Haihao Lu, Vahab Mirrokni, Balasubramanian Sivan, Di Wang

    Proceedings of the 41st International Conference on Machine Learning

    Paper

  3. A J-symmetric Quasi-newton Method for Minimax Problems

    Azam Asl, Haihao Lu, Jinwen Yang

    Mathematical Programming

    Paper

  4. Infeasibility Detection with Primal-dual Hybrid Gradient for Large-scale Linear Programming

    David Applegate, Mateo Díaz, Haihao Lu, Miles Lubin

    SIAM Journal on Optimization

    Paper

  5. On the Linear Convergence of Extra-gradient Methods for Nonconvex-nonconcave Minimax Problems

    Saeed Hajizadeh, Haihao Lu, Benjamin Grimmer

    INFORMS Journal on Optimization

    Paper arXiv

  6. MPAX: Mathematical Programming in JAX

    Haihao Lu, Zedong Peng, Jinwen Yang

    Under review

    arXiv

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

    Haihao Lu, Jinwen Yang

    Under review

    arXiv

  8. Restarted Halpern PDHG for Linear Programming

    Haihao Lu, Jinwen Yang

    Under review

    arXiv

2023

  1. Online Ad Procurement in Non-stationary Autobidding Worlds

    Jason Cheuk Nam Liang, Haihao Lu, Baoyu Zhou

    Proceedings of the 37th Conference on Neural Information Processing Systems

    Paper

  2. Faster First-order Primal-dual Methods for Linear Programming Using Restarts and Sharpness

    David Applegate, Oliver Hinder, Haihao Lu, Miles Lubin

    Mathematical Programming

    Paper

  3. The Landscape of the Proximal Point Method for Nonconvex–nonconcave Minimax Optimization

    Benjamin Grimmer, Haihao Lu, Pratik Worah, Vahab Mirrokni

    Mathematical Programming

    Paper

  4. The Best of Many Worlds: Dual Mirror Descent for Online Allocation Problems

    Santiago R. Balseiro, Haihao Lu, Vahab Mirrokni

    Operations Research

    Paper

  5. Nearly Optimal Linear Convergence of Stochastic Primal-Dual Methods for Linear Programming

    Haihao Lu, Jinwen Yang

    Under review

    arXiv

  6. Analysis of Dual-based PID Controllers Through Convolutional Mirror Descent

    Santiago R. Balseiro, Haihao Lu, Vahab Mirrokni, Balasubramanian Sivan

    Under review

    arXiv

  7. Achieving Fairness and Accuracy in Regressive Property Taxation

    Ozan Candogan, Feiyu Han, Haihao Lu

    Under review

    arXiv

  8. On the Convergence of L-shaped Algorithms for Two-stage Stochastic Programming

    John R. Birge, Haihao Lu, Baoyu Zhou

    Under review

    arXiv

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

    Haihao Lu, Jinwen Yang

    Under review

    arXiv

  10. On the Infimal Sub-differential Size of Primal-dual Hybrid Gradient Method and Beyond

    Haihao Lu, Jinwen Yang

    Under review

    arXiv

2022

  1. An O(sʳ)-Resolution ODE Framework for Discrete-time Optimization Algorithms and Applications to the Linear Convergence of Minimax Problems

    Haihao Lu

    Mathematical Programming

    Paper

  2. Limiting Behaviors of Nonconvex-nonconcave Minimax Optimization via Continuous-time Systems

    Benjamin Grimmer, Haihao Lu, Pratik Worah, Vahab Mirrokni

    Proceedings of The 33rd International Conference on Algorithmic Learning Theory

    Paper

  3. Frank-Wolfe Methods with an Unbounded Feasible Region and Applications to Structured Learning

    Haoyue Wang, Haihao Lu, Rahul Mazumder

    SIAM Journal on Optimization

    Paper

2021

  1. Practical Large-Scale Linear Programming using Primal-dual Hybrid Gradient

    David Applegate, Mateo Diaz, Oliver Hinder, Haihao Lu, Miles Lubin, Brendan O'Donoghue, Warren Schudy

    Proceedings of the 35th Conference on Neural Information Processing Systems

    Paper

  2. Generalized Stochastic Frank–Wolfe Algorithm with Stochastic 'Substitute' Gradient for Structured Convex Optimization

    Haihao Lu, Robert M. Freund

    Mathematical Programming

    Paper arXiv

2020

  1. Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming

    Joey Huchette, Haihao Lu, Hossein Esfandiari, Vahab Mirrokni

    Proceedings of the 34th Conference on Neural Information Processing Systems

    Paper

  2. Accelerating Gradient Boosting Machines

    Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva, Vahab Mirrokni

    Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics

    Paper

  3. Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk Minimization

    Kenji Kawaguchi, Haihao Lu

    Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics

    Paper

  4. Dual Mirror Descent for Online Allocation Problems

    Santiago Balseiro, Haihao Lu, Vahab Mirrokni

    Proceedings of the 37th International Conference on Machine Learning

    Paper

  5. Randomized Gradient Boosting Machine

    Haihao Lu, Rahul Mazumder

    SIAM Journal on Optimization

    Paper

2019

  1. 'Relative Continuity' for Non-Lipschitz Nonsmooth Convex Optimization Using Stochastic (or Deterministic) Mirror Descent

    Haihao Lu

    INFORMS Journal on Optimization

    Paper

2018

  1. New Computational Guarantees for Solving Convex Optimization Problems with First Order Methods, Via a Function Growth Condition Measure

    Robert M. Freund, Haihao Lu

    Mathematical Programming

    Paper

  2. Accelerating Greedy Coordinate Descent Methods

    Haihao Lu, Robert M. Freund, Vahab Mirrokni

    Proceedings of the 35th International Conference on Machine Learning

    Paper

  3. Approximate Leave-one-out for Fast Parameter Tuning in High Dimensions

    Shuaiwen Wang, Wenda Zhou, Haihao Lu, Arian Maleki, Vahab Mirrokni

    Proceedings of the 35th International Conference on Machine Learning

    Paper

  4. Relatively Smooth Convex Optimization by First-order Methods, and Applications

    Haihao Lu, Robert M. Freund, Yurii Nesterov

    SIAM Journal on Optimization

    Paper Preprint PDF

  5. Approximate Leave-One-Out for High-Dimensional Non-Differentiable Learning Problems

    Shuaiwen Wang, Wenda Zhou, Arian Maleki, Haihao Lu, Vahab Mirrokni

    Under review

    arXiv

2017

  1. Depth Creates No Bad Local Minima

    Haihao Lu, Kenji Kawaguchi

    Under review

    arXiv

2016

  1. Stochastic Linearization of Turbulent Dynamics of Dispersive Waves in Equilibrium and Non-equilibrium State

    Shixiao W. Jiang, Haihao Lu, Douglas Zhou, David Cai

    New Journal of Physics

    Paper

2014

  1. Renormalized Dispersion Relations of 𝛽-Fermi-Pasta-Ulam Chains in Equilibrium and Nonequilibrium States

    Shi-xiao W. Jiang, Haihao Lu, Douglas Zhou, David Cai

    Physical Review E

    Paper