Revisiting Inexact Fixed-Point Iterations for Min-Max Problems: Stochasticity and Structured Nonconvexity

arXiv:2402.05071 · math.OC, cs.LG, stat.ML · Submitted 2024-02-07 · Read on arXiv

math.OC, cs.LG, stat.ML

Submitted: 2024-02-07

Updated: 2026-09-26

Journal ref: Proceedings of the International Conference on Machine Learning (ICML) 2024

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 9× in this paper · explained in 1 episode
• Reinforcement Learning — 5× in this paper · explained in 4 episodes
• Hilbert Space — 4× in this paper · explained in 2 episodes
• Modularity — 2× in this paper · explained in 9 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Convergence Guarantees — 1× in this paper · explained in 3 episodes
• Generative Adversarial Networks (GANs) — 1× in this paper · explained in 3 episodes
• Geometric Distribution — 1× in this paper · explained in 2 episodes
• Limit Cycles — 1× in this paper · explained in 2 episodes
• Minty Variational Inequality — 1× in this paper
• Regularization — 1× in this paper · explained in 3 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes

Sources

• Stochastic Halpern iteration in normed spaces and applications to reinforcement learning
• Accelerated Algorithms for Constrained Nonconvex-Nonconcave Min-Max Optimization and Comonotone Inclusion
• Near-Optimal Algorithms for Making the Gradient Small in Stochastic Minimax Optimization
• An Inexact Halpern Iteration with Application to Distributionally Robust Optimization
• Sublinear Convergence Rates of Extragradient-Type Methods: A Survey on Classical and Recent Developments
• Randomized Block-Coordinate Optimistic Gradient Algorithms for Root-Finding Problems
• Accelerated Minimax Algorithms Flock Together

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