Hybrid Block Successive Approximation for One-Sided Non-Convex Min-Max Problems: Algorithms and Applications

DOI10.1109/TSP.2020.2986363MaRDI QIDQ5102911

Yongxin Chen, Mingyi Hong, Ioannis Tsaknakis, Songtao Lu

Publication date: 23 September 2022

Published in: IEEE Transactions on Signal Processing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1902.08294

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

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Weakly-convex–concave min–max optimization: provable algorithms and applications in machine learning ⋮ A stochastic primal-dual method for a class of nonconvex constrained optimization ⋮ Zeroth-order single-loop algorithms for nonconvex-linear minimax problems ⋮ Zeroth-order algorithms for nonconvex-strongly-concave minimax problems with improved complexities ⋮ Minimax Problems with Coupled Linear Constraints: Computational Complexity and Duality ⋮ A unified single-loop alternating gradient projection algorithm for nonconvex-concave and convex-nonconcave minimax problems ⋮ Decentralized Gradient Descent Maximization Method for Composite Nonconvex Strongly-Concave Minimax Problems ⋮ Optimality Conditions for Nonsmooth Nonconvex-Nonconcave Min-Max Problems and Generative Adversarial Networks ⋮ Alternating Proximal-Gradient Steps for (Stochastic) Nonconvex-Concave Minimax Problems ⋮ Utility/privacy trade-off as regularized optimal transport ⋮ New approximation algorithms for weighted maximin dispersion problem with box or ball constraints

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