Revisiting Stochastic Extragradient

Konstantin Mishchenko, Dmitry Kovalev, Egor Shulgin, Peter Richtarik, Yura Malitsky
; Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:4573-4582, 2020.

Abstract

We fix a fundamental issue in the stochastic extragradient method by providing a new sampling strategy that is motivated by approximating implicit updates. Since the existing stochastic extragradient algorithm, called Mirror-Prox, of (Juditsky, 2011) diverges on a simple bilinear problem when the domain is not bounded, we prove guarantees for solving variational inequality that go beyond existing settings. Furthermore, we illustrate numerically that the proposed variant converges faster than many other methods on several convex-concave saddle-point problems. We also discuss how extragradient can be applied to training Generative Adversarial Networks (GANs) and how it compares to other methods. Our experiments on GANs demonstrate that the introduced approach may make the training faster in terms of data passes, while its higher iteration complexity makes the advantage smaller.

Cite this Paper


BibTeX
@InProceedings{pmlr-v108-mishchenko20a, title = {Revisiting Stochastic Extragradient}, author = {Mishchenko, Konstantin and Kovalev, Dmitry and Shulgin, Egor and Richtarik, Peter and Malitsky, Yura}, booktitle = {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics}, pages = {4573--4582}, year = {2020}, editor = {Silvia Chiappa and Roberto Calandra}, volume = {108}, series = {Proceedings of Machine Learning Research}, address = {Online}, month = {26--28 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v108/mishchenko20a/mishchenko20a.pdf}, url = {http://proceedings.mlr.press/v108/mishchenko20a.html}, abstract = {We fix a fundamental issue in the stochastic extragradient method by providing a new sampling strategy that is motivated by approximating implicit updates. Since the existing stochastic extragradient algorithm, called Mirror-Prox, of (Juditsky, 2011) diverges on a simple bilinear problem when the domain is not bounded, we prove guarantees for solving variational inequality that go beyond existing settings. Furthermore, we illustrate numerically that the proposed variant converges faster than many other methods on several convex-concave saddle-point problems. We also discuss how extragradient can be applied to training Generative Adversarial Networks (GANs) and how it compares to other methods. Our experiments on GANs demonstrate that the introduced approach may make the training faster in terms of data passes, while its higher iteration complexity makes the advantage smaller.} }
Endnote
%0 Conference Paper %T Revisiting Stochastic Extragradient %A Konstantin Mishchenko %A Dmitry Kovalev %A Egor Shulgin %A Peter Richtarik %A Yura Malitsky %B Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2020 %E Silvia Chiappa %E Roberto Calandra %F pmlr-v108-mishchenko20a %I PMLR %J Proceedings of Machine Learning Research %P 4573--4582 %U http://proceedings.mlr.press %V 108 %W PMLR %X We fix a fundamental issue in the stochastic extragradient method by providing a new sampling strategy that is motivated by approximating implicit updates. Since the existing stochastic extragradient algorithm, called Mirror-Prox, of (Juditsky, 2011) diverges on a simple bilinear problem when the domain is not bounded, we prove guarantees for solving variational inequality that go beyond existing settings. Furthermore, we illustrate numerically that the proposed variant converges faster than many other methods on several convex-concave saddle-point problems. We also discuss how extragradient can be applied to training Generative Adversarial Networks (GANs) and how it compares to other methods. Our experiments on GANs demonstrate that the introduced approach may make the training faster in terms of data passes, while its higher iteration complexity makes the advantage smaller.
APA
Mishchenko, K., Kovalev, D., Shulgin, E., Richtarik, P. & Malitsky, Y.. (2020). Revisiting Stochastic Extragradient. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, in PMLR 108:4573-4582

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