Stochastic variance reduction for variational inequality methods
A Alacaoglu, Y Malitsky - Conference on Learning Theory, 2022 - proceedings.mlr.press
We propose stochastic variance reduced algorithms for solving convex-concave saddle
point problems, monotone variational inequalities, and monotone inclusions. Our framework …
point problems, monotone variational inequalities, and monotone inclusions. Our framework …
[HTML][HTML] Convergence of sequences: A survey
B Franci, S Grammatico - Annual Reviews in Control, 2022 - Elsevier
Convergent sequences of real numbers play a fundamental role in many different problems
in system theory, eg, in Lyapunov stability analysis, as well as in optimization theory and …
in system theory, eg, in Lyapunov stability analysis, as well as in optimization theory and …
Solving stochastic weak minty variational inequalities without increasing batch size
This paper introduces a family of stochastic extragradient-type algorithms for a class of
nonconvex-nonconcave problems characterized by the weak Minty variational inequality …
nonconvex-nonconcave problems characterized by the weak Minty variational inequality …
A stochastic primal-dual algorithm for composite constrained optimization
This paper studies the decentralized stochastic optimization problem over an undirected
network, where each agent owns its local private functions made up of two non-smooth …
network, where each agent owns its local private functions made up of two non-smooth …
No-regret learning in games with noisy feedback: Faster rates and adaptivity via learning rate separation
YG Hsieh, K Antonakopoulos… - Advances in …, 2022 - proceedings.neurips.cc
We examine the problem of regret minimization when the learner is involved in a continuous
game with other optimizing agents: in this case, if all players follow a no-regret algorithm, it is …
game with other optimizing agents: in this case, if all players follow a no-regret algorithm, it is …
Two Steps at a Time---Taking GAN Training in Stride with Tseng's Method
Motivated by the training of generative adversarial networks (GANs), we study methods for
solving minimax problems with additional nonsmooth regularizers. We do so by employing …
solving minimax problems with additional nonsmooth regularizers. We do so by employing …
Stochastic generalized Nash equilibrium-seeking in merely monotone games
B Franci, S Grammatico - IEEE Transactions on Automatic …, 2021 - ieeexplore.ieee.org
We solve the stochastic generalized Nash equilibrium (SGNE) problem in merely monotone
games with expected value cost functions. Specifically, we present the first distributed SGNE …
games with expected value cost functions. Specifically, we present the first distributed SGNE …
Variable sample-size optimistic mirror descent algorithm for stochastic mixed variational inequalities
ZP Yang, Y Zhao, GH Lin - Journal of Global Optimization, 2024 - Springer
In this paper, we propose a variable sample-size optimistic mirror descent algorithm under
the Bregman distance for a class of stochastic mixed variational inequalities. Different from …
the Bregman distance for a class of stochastic mixed variational inequalities. Different from …
Stochastic variance-reduced forward-reflected methods for root-finding problems
Q Tran-Dinh - arXiv preprint arXiv:2406.00937, 2024 - arxiv.org
We develop two novel stochastic variance-reduction methods to approximate a solution of
root-finding problems applicable to both equations and inclusions. Our algorithms leverage …
root-finding problems applicable to both equations and inclusions. Our algorithms leverage …
A fast stochastic approximation-based subgradient extragradient algorithm with variance reduction for solving stochastic variational inequality problems
XJ Long, YH He - Journal of Computational and Applied Mathematics, 2023 - Elsevier
In this paper, we propose a fast stochastic approximation-based subgradient extragradient
algorithm with variance reduction for solving the stochastic variational inequality, where the …
algorithm with variance reduction for solving the stochastic variational inequality, where the …