Zeroth-Order primal-dual Alternating Projection Gradient Algorithms for Nonconvex Minimax Problems with Coupled linear Constraints

no code implementations • 26 Jan 2024 • Huiling Zhang, Zi Xu, Yuhong Dai

nonconvex-concave) minimax problems with coupled linear constraints under deterministic settings and $\tilde{\mathcal{O}}(\varepsilon ^{-3})$ (resp.

An accelerated first-order regularized momentum descent ascent algorithm for stochastic nonconvex-concave minimax problems

no code implementations • 24 Oct 2023 • Huiling Zhang, Zi Xu

Stochastic nonconvex minimax problems have attracted wide attention in machine learning, signal processing and many other fields in recent years.

Primal Dual Alternating Proximal Gradient Algorithms for Nonsmooth Nonconvex Minimax Problems with Coupled Linear Constraints

no code implementations • 9 Dec 2022 • Huiling Zhang, Junlin Wang, Zi Xu, Yu-Hong Dai

$\mathcal{O}\left( \varepsilon ^{-4} \right)$) under nonconvex-strongly concave (resp.

A Unified Single-loop Alternating Gradient Projection Algorithm for Nonconvex-Concave and Convex-Nonconcave Minimax Problems

no code implementations • 3 Jun 2020 • Zi Xu, Huiling Zhang, Yang Xu, Guanghui Lan

Moreover, its gradient complexity to obtain an $\varepsilon$-stationary point of the objective function is bounded by $\mathcal{O}\left( \varepsilon ^{-2} \right)$ (resp., $\mathcal{O}\left( \varepsilon ^{-4} \right)$) under the strongly convex-nonconcave (resp., convex-nonconcave) setting.