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Research & Publications
- Articles
- Articles
- Working Papers
"On the Geometry and Refined Rate of Primal-dual Hybrid Gradient for Linear Programming."
Lu, Haihao and Jinwen Yang. Mathematical Programming. Forthcoming."A Field Guide for Pacing Budget and ROS Constraints."
Balseiro, Santiago R., Kshipra Bhawalkar, Zhe Feng, Haihao Lu, Vahab Mirrokni, Balasubramanian Sivan, and Di Wang. Proceedings of the 41st International Conference on Machine Learning Vol. 235, (2024): 2607-2638."A J-symmetric Quasi-newton Method for Minimax Problems."
Asl, Azam, Haihao Lu, and Jinwen Yang. Mathematical Programming Vol. 204, No. 1-2 (2024): 207-254."Infeasibility Detection with Primal-dual Hybrid Gradient for Large-scale Linear Programming."
Applegate, David, Mateo Díaz, Haihao Lu, and Miles Lubin. SIAM Journal on Optimization Vol. 34, No. 1 (2024)."On the Linear Convergence of Extra-gradient Methods for Nonconvex-nonconcave Minimax Problems."
Hajizadeh, Saeed, Haihao Lu, and Benjamin Grimmer. INFORMS Journal on Optimization Vol. 6, No. 1 (2024): 19-31. arXiv Preprint."Online Ad Procurement in Non-stationary Autobidding Worlds."
Liang, Jason Cheuk Nam, Haihao Lu, and Baoyu Zhou. Proceedings of the 37th Conference on Neural Information Processing Systems (2023): 1-24."Faster First-order Primal-dual Methods for Linear Programming Using Restarts and Sharpness."
Applegate, David, Oliver Hinder, Haihao Lu, and Miles Lubin. Mathematical Programming Vol. 201, No. 1-2 (2023): 133-184."The Landscape of the Proximal Point Method for Nonconvex–nonconcave Minimax Optimization."
Grimmer, Benjamin, Haihao Lu, Pratik Worah, and Vahab Mirrokni. Mathematical Programming Vol. 201, No. 1-2 (2023): 373-407."The Best of Many Worlds: Dual Mirror Descent for Online Allocation Problems."
Balseiro, Santiago R., Haihao Lu, and Vahab Mirrokni. Operations Research Vol. 71, No. 1 (2023): 101-119."An O(sr)-Resolution ODE Framework for Discrete-time Optimization Algorithms and Applications to the Linear Convergence of Minimax Problems."
Lu, Haihao. Mathematical Programming Vol. 194, No. 1-2 (2022): 1061-1112."Limiting Behaviors of Nonconvex-nonconcave Minimax Optimization via Continuous-time Systems."
Grimmer, Benjamin, Haihao Lu, Pratik Worah, and Vahab Mirrokni. Proceedings of The 33rd International Conference on Algorithmic Learning Theory Vol. 167, (2022): 465-487."Frank-Wolfe Methods with an Unbounded Feasible Region and Applications to Structured Learning."
Wang, Haoyue, Haihao Lu, and Rahul Mazumder. SIAM Journal on Optimization Vol. 32, No. 4 (2022): 2938-2968."Practical Large-Scale Linear Programming using Primal-dual Hybrid Gradient."
Applegate, David, Mateo Diaz, Oliver Hinder, Haihao Lu, Miles Lubin, Brendan O'Donoghue, and Warren Schudy. Proceedings of the 35th Conference on Neural Information Processing Systems (2021): 1-15."Regularized Online Allocation Problems: Fairness and Beyond."
Balseiro, Santiago, Haihao Lu, and Vahab Mirrokni. Proceedings of the 38th International Conference on Machine Learning Vol. 139, (2021): 630-639."Generalized Stochastic Frank–Wolfe Algorithm with Stochastic 'Substitute' Gradient for Structured Convex Optimization."
Lu, Haihao and Robert M. Freund. Mathematical Programming Vol. 187, No. 1-2 (2021): 317-349. arXiv Preprint."Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming."
Huchette, Joey, Haihao Lu, Hossein Esfandiari, and Vahab Mirrokni. Proceedings of the 34th Conference on Neural Information Processing Systems (2020): 1-11."Accelerating Gradient Boosting Machines."
Lu, Haihao, Sai Praneeth Karimireddy, Natalia Ponomareva, and Vahab Mirrokni. Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics Vol. 108, (2020): 516-526."Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk Minimization."
Kawaguchi, Kenji and Haihao Lu. Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics Vol. 108, (2020): 669-679."Dual Mirror Descent for Online Allocation Problems."
Balseiro, Santiago, Haihao Lu, and Vahab Mirrokni. Proceedings of the 37th International Conference on Machine Learning Vol. 119, (2020): 613-628."Randomized Gradient Boosting Machine."
Lu, Haihao, and Rahul Mazumder. SIAM Journal on Optimization Vol. 30, No. 4 (2020): 2780-2808."'Relative Continuity' for Non-Lipschitz Nonsmooth Convex Optimization Using Stochastic (or Deterministic) Mirror Descent."
Lu, Haihao. INFORMS Journal on Optimization Vol. 1, No. 4 (2019): 288-303."New Computational Guarantees for Solving Convex Optimization Problems with First Order Methods, Via a Function Growth Condition Measure."
Freund, Robert M. and Haihao Lu. Mathematical Programming Vol. 170, No. 1-2 (2018): 445-477."Accelerating Greedy Coordinate Descent Methods."
Lu, Haihao, Robert M. Freund, and Vahab Mirrokni. Proceedings of the 35th International Conference on Machine Learning Vol. 80, (2018): 3257-3266."Approximate Leave-one-out for Fast Parameter Tuning in High Dimensions."
Wang, Shuaiwen, Wenda Zhou, Haihao Lu, Arian Maleki, and Vahab Mirrokni. Proceedings of the 35th International Conference on Machine Learning Vol. 80, (2018): 5228-5237."Relatively Smooth Convex Optimization by First-order Methods, and Applications."
Lu, Haihao, Robert M. Freund, and Yurii Nesterov. SIAM Journal on Optimization Vol. 28, No. 1 (2018): 333-354. Working Paper."Stochastic Linearization of Turbulent Dynamics of Dispersive Waves in Equilibrium and Non-equilibrium State."
Jiang, Shixiao W., Haihao Lu, Douglas Zhou, and David Cai. New Journal of Physics Vol. 18, No. 8 (2016): 083028."Renormalized Dispersion Relations of 𝛽-Fermi-Pasta-Ulam Chains in Equilibrium and Nonequilibrium States."
Jiang, Shi-xiao W., Haihao Lu, Douglas Zhou, and David Cai. Physical Review E Vol. 90, No. 3 (2014): 032925.
"A New Crossover Algorithm for LP Inspired by the Spiral Dynamic of PDHG."
Liu, Tianhao and Haihao Lu, MIT Sloan Working Paper 7153-24. Cambridge, MA: MIT Sloan School of Management, September 2024. arXiv Preprint."Auto-bidding and Auctions in Online Advertising: A Survey."
Aggarwal, Gagan, Ashwinkumar Badanidiyuru, Santiago R. Balseiro, Kshipra Bhawalkar, Yuan Deng, Zhe Feng, Gagan Goel, Christopher Liaw, Haihao Lu, Mohammad Mahdian, Jieming Mao, Aranyak Mehta, Vahab Mirrokni, Renato Paes Leme, Andres Perlroth, Georgios Piliouras, Jon Schneider, Ariel Schvartzman, Balasubramanian Sivan, Kelly Spendlove, Yifeng Teng, Di Wang, Hanrui Zhang, Mingfei Zhao, Wennan Zhu, and Song Zuo, MIT Sloan Working Paper 7154-24. Cambridge, MA: MIT Sloan School of Management, August 2024. arXiv Preprint."PDOT: a Practical Primal-Dual Algorithm and a GPU-Based Solver for Optimal Transport."
Lu, Haihao and Jinwen Yang, MIT Sloan Working Paper 7124-24. Cambridge, MA: MIT Sloan School of Management, July 2024. arXiv Preprint."Restarted Halpern PDHG for Linear Programming."
Lu, Haihao and Jinwen Yang, MIT Sloan Working Paper 7125-24. Cambridge, MA: MIT Sloan School of Management, July 2024. arXiv Preprint."A Practical and Optimal First-order Method for Large-scale Convex Quadratic Programming."
Lu, Haihao and Jinwen Yang, MIT Sloan Working Paper 7103-23. Cambridge, MA: MIT Sloan School of Management, May 2024. arXiv Preprint."Optimizing Scalable Targeted Marketing Policies with Constraints."
Lu, Haihao, Duncan Simester, and Yuting Zhu, MIT Sloan Working Paper 7101-23. Cambridge, MA: MIT Sloan School of Management, January 2024. arXiv Preprint."Nearly Optimal Linear Convergence of Stochastic Primal-Dual Methods for Linear Programming."
Lu, Haihao and Jinwen Yang, MIT Sloan Working Paper 7127-21. Cambridge, MA: MIT Sloan School of Management, December 2023. arXiv Preprint."cuPDLP.jl: A GPU Implementation of Restarted Primal-Dual Hybrid Gradient for Linear Programming in Julia."
Lu, Haihao and Jinwen Yang, MIT Sloan Working Paper 7102-23. Cambridge, MA: MIT Sloan School of Management, December 2023. arXiv Preprint."Analysis of Dual-based PID Controllers Through Convolutional Mirror Descent."
Balseiro, Santiago R., Haihao Lu, Vahab Mirrokni, and Balasubramanian Sivan, MIT Sloan Working Paper 7110-22. Cambridge, MA: MIT Sloan School of Management, December 2023. arXiv Preprint."On the Sparsity of Optimal Linear Decision Rules in Robust Inventory Management."
Lu, Haihao and Brad Sturt, MIT Sloan Working Paper 7109-22. Cambridge, MA: MIT Sloan School of Management, December 2023. arXiv Preprint."Achieving Fairness and Accuracy in Regressive Property Taxation."
Candogan, Ozan, Feiyu Han, and Haihao Lu, MIT Sloan Working Paper 7126-23. Cambridge, MA: MIT Sloan School of Management, December 2023. arXiv Preprint."On the Convergence of L-shaped Algorithms for Two-stage Stochastic Programming."
Birge, John R., Haihao Lu, and Baoyu Zhou, MIT Sloan Working Paper 7104-23. Cambridge, MA: MIT Sloan School of Management, September 2023. arXiv Preprint."On a Unified and Simplified Proof for the Ergodic Convergence Rates of PPM, PDHG and ADMM."
Lu, Haihao and Jinwen Yang, MIT Sloan Working Paper 7106-23. Cambridge, MA: MIT Sloan School of Management, May 2023. arXiv Preprint."On the Infimal Sub-differential Size of Primal-dual Hybrid Gradient Method and Beyond."
Lu, Haihao and Jinwen Yang, MIT Sloan Working Paper 7108-22. Cambridge, MA: MIT Sloan School of Management, March 2023. arXiv Preprint."Regularized Online Allocation Problems: Fairness and Beyond."
Balseiro, Santiago R., Haihao Lu, Vahab Mirrokni, MIT Sloan Working Paper 7111-20. Cambridge, MA: MIT Sloan School of Management, November 2021. arXiv Preprint."Approximate Leave-One-Out for High-Dimensional Non-Differentiable Learning Problems."
Wang, Shuaiwen, Wenda Zhou, Arian Maleki, Haihao Lu, and Vahab Mirrokni, MIT Sloan Working Paper 7128-18. Cambridge, MA: MIT Sloan School of Management, October 2018. arXiv Preprint.