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The Rate of Convergence of Bregman Proximal Methods: Local Geometry Versus Regularity Versus Sharpness., , , и . SIAM J. Optim., 34 (3): 2440-2471 (2024)The Last-Iterate Convergence Rate of Optimistic Mirror Descent in Stochastic Variational Inequalities., , , и . COLT, том 134 из Proceedings of Machine Learning Research, стр. 326-358. PMLR, (2021)Accelerating Smooth Games by Manipulating Spectral Shapes., , , , и . AISTATS, том 108 из Proceedings of Machine Learning Research, стр. 1705-1715. PMLR, (2020)A Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Games, , , и . (2019)cite arxiv:1906.05945.A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Differentiable Games., , , и . AISTATS, том 108 из Proceedings of Machine Learning Research, стр. 2863-2873. PMLR, (2020)Expressive Power of Invariant and Equivariant Graph Neural Networks., и . ICLR, OpenReview.net, (2021)Linear Lower Bounds and Conditioning of Differentiable Games., , , и . ICML, том 119 из Proceedings of Machine Learning Research, стр. 4583-4593. PMLR, (2020)Almost sure convergence rates of stochastic gradient methods under gradient domination., , , и . CoRR, (2024)A Tight and Unified Analysis of Extragradient for a Whole Spectrum of Differentiable Games, , , и . (2019)cite arxiv:1906.05945.Lower Bounds and Conditioning of Differentiable Games, , , и . (2019)cite arxiv:1906.07300Comment: Submitted to NeurIPS 2019.