Haihao Lu
Orcid: 0000-0002-5217-1894
According to our database1,
Haihao Lu
authored at least 35 papers
between 2017 and 2024.
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Bibliography
2024
Infeasibility Detection with Primal-Dual Hybrid Gradient for Large-Scale Linear Programming.
SIAM J. Optim., March, 2024
On the Linear Convergence of Extragradient Methods for Nonconvex-Nonconcave Minimax Problems.
INFORMS J. Optim., January, 2024
2023
Oper. Res., January, 2023
The landscape of the proximal point method for nonconvex-nonconcave minimax optimization.
Math. Program., 2023
Faster first-order primal-dual methods for linear programming using restarts and sharpness.
Math. Program., 2023
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
2022
Frank-Wolfe Methods with an Unbounded Feasible Region and Applications to Structured Learning.
SIAM J. Optim., December, 2022
An O(s<sup>r)</sup>-resolution ODE framework for understanding discrete-time algorithms and applications to the linear convergence of minimax problems.
Math. Program., 2022
CoRR, 2022
Limiting Behaviors of Nonconvex-Nonconcave Minimax Optimization via Continuous-Time Systems.
Proceedings of the International Conference on Algorithmic Learning Theory, 29 March, 2022
2021
Generalized stochastic Frank-Wolfe algorithm with stochastic "substitute" gradient for structured convex optimization.
Math. Program., 2021
Linear Convergence of Stochastic Primal Dual Methods for Linear Programming Using Variance Reduction and Restarts.
CoRR, 2021
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
Proceedings of the 38th International Conference on Machine Learning, 2021
2020
An O(s<sup>r</sup>)-Resolution ODE Framework for Discrete-Time Optimization Algorithms and Applications to Convex-Concave Saddle-Point Problems.
CoRR, 2020
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
Proceedings of the 37th International Conference on Machine Learning, 2020
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020
Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk Minimization.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020
2019
"Relative Continuity" for Non-Lipschitz Nonsmooth Convex Optimization Using Stochastic (or Deterministic) Mirror Descent.
INFORMS J. Optim., October, 2019
2018
SIAM J. Optim., 2018
New computational guarantees for solving convex optimization problems with first order methods, via a function growth condition measure.
Math. Program., 2018
CoRR, 2018
CoRR, 2018
Proceedings of the 35th International Conference on Machine Learning, 2018
Proceedings of the 35th International Conference on Machine Learning, 2018
2017