Enhanced adaptive gradient algorithms for nonconvex-PL minimax optimization
F Huang - arXiv preprint arXiv:2303.03984, 2023 - arxiv.org
In the paper, we study a class of nonconvex nonconcave minimax optimization problems (ie,
$\min_x\max_y f (x, y) $), where $ f (x, y) $ is possible nonconvex in $ x $, and it is …
$\min_x\max_y f (x, y) $), where $ f (x, y) $ is possible nonconvex in $ x $, and it is …
Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization
We provide a unified analysis of two-timescale gradient descent ascent (TTGDA) for solving
structured nonconvex minimax optimization problems in the form of $\min_\textbf {x}\max …
structured nonconvex minimax optimization problems in the form of $\min_\textbf {x}\max …
Achieving Near-Optimal Convergence for Distributed Minimax Optimization with Adaptive Stepsizes
In this paper, we show that applying adaptive methods directly to distributed minimax
problems can result in non-convergence due to inconsistency in locally computed adaptive …
problems can result in non-convergence due to inconsistency in locally computed adaptive …
AGDA+: Proximal Alternating Gradient Descent Ascent Method With a Nonmonotone Adaptive Step-Size Search For Nonconvex Minimax Problems
We consider double-regularized nonconvex-strongly concave (NCSC) minimax problems of
the form $(P):\min_ {x\in\mathcal {X}}\max_ {y\in\mathcal {Y}} g (x)+ f (x, y)-h (y) $, where $ g …
the form $(P):\min_ {x\in\mathcal {X}}\max_ {y\in\mathcal {Y}} g (x)+ f (x, y)-h (y) $, where $ g …
Two Completely Parameter-Free Alternating Gradient Projection Algorithms for Nonconvex-(strongly) Concave Minimax Problems
J Yang, H Zhang, Z Xu - arXiv preprint arXiv:2407.21372, 2024 - arxiv.org
Due to their importance in various emerging applications, efficient algorithms for solving
minimax problems have recently received increasing attention. However, many existing …
minimax problems have recently received increasing attention. However, many existing …
Double-Step Alternating Extragradient with Increasing Timescale Separation for Finding Local Minimax Points: Provable Improvements
K Kim, D Kim - Forty-first International Conference on Machine … - openreview.net
In nonconvex-nonconcave minimax optimization, two-timescale gradient methods have
shown their potential to find local minimax (optimal) points, provided that the timescale …
shown their potential to find local minimax (optimal) points, provided that the timescale …
High-probability complexity bounds for stochastic non-convex minimax optimization
Stochastic smooth nonconvex minimax problems are prevalent in machine learning, eg,
GAN training, fair classification, and distributionally robust learning. Stochastic gradient …
GAN training, fair classification, and distributionally robust learning. Stochastic gradient …
Online Min-max Problems with Non-convexity and Non-stationarity
Online min-max optimization has recently gained considerable interest due to its rich
applications to game theory, multi-agent reinforcement learning, online robust learning, etc …
applications to game theory, multi-agent reinforcement learning, online robust learning, etc …