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Stochastic Gradient Descent-Ascent (SGDA) is one of the most prominent algorithms for solving min-max optimization and variational inequalities problems (VIP) appearing in various machine learning tasks.
Distributed learning with compressed gradient differences
Mishchenko, K., Gorbunov, E., Takáč, M., and Richtárik, P. (2019) · 1901
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Stochastic distributed learning with gradient quantization and variance reduction
Horváth, S., Kovalev, D., Mishchenko, K., Stich, S., and Richtárik, P. (2019) · 1904
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Unified optimal analysis of the (stochastic) gradient method
Stich, S. U. (2019) · 1907
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Theory of games and economic behavior
Morgenstern, O. and Von Neumann, J. (1953) · 1953
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Numerical methods for finding saddle points
Dem’yanov, V. F. and Pevnyi, A. B. (1972) · 1972
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The extragradient method for finding saddle points and other problems
Korpelevich, G. M. (1976) · 1976
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A modification of the arrow-hurwicz method for search of saddle points
Popov, L. D. (1980) · 1980
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Co-coercivity and its role in the convergence of iterative schemes for solving variational inequalities
Zhu, D. L. and Marcotte, P. (1996) · 1996
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Zeroth-order algorithms for nonconvex minimax problems with improved complexities
Wang, Z., Balasubramanian, K., Ma, S., and Razaviyayn, M. (2020) · 2001
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On biased compression for distributed learning
Beznosikov, A., Horváth, S., Richtárik, P., and Safaryan, M. (2020a) · 2002
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Prediction, learning, and games
Cesa-Bianchi, N. and Lugosi, G. (2006) · 2006
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Unified analysis of stochastic gradient methods for composite convex and smooth optimization
Khaled, A., Sebbouh, O., Loizou, N., Gower, R. M., and Richtárik, P. (2020) · 2006
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Dual extrapolation and its applications to solving variational inequalities and related problems
Nesterov, Y. (2007) · 2007
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Enhancing sparsity by reweighted ℓ 1 \ell_{1} minimization
Candes, E. J., Wakin, M. B., and Boyd, S. P. (2008) · 2008
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Robust stochastic approximation approach to stochastic programming
Nemirovski, A., Juditsky, A., Lan, G., and Shapiro, A. (2009) · 2009
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Primal-dual subgradient methods for convex problems
Nesterov, Y. (2009) · 2009
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Distributed saddle-point problems: Lower bounds, optimal algorithms and federated gans
Beznosikov, A., Samokhin, V., and Gasnikov, A. (2020c) · 2010
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Convex analysis and monotone operator theory in Hilbert spaces
Bauschke, H. H., Combettes, P. L., et al. (2011) · 2011
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Solving variational inequalities with stochastic mirror-prox algorithm
Juditsky, A., Nemirovski, A., and Tauvel, C. (2011) · 2011
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Accelerating stochastic gradient descent using predictive variance reduction
Johnson, R. and Zhang, T. (2013) · 2013
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A splitting algorithm for dual monotone inclusions involving cocoercive operators
Vũ, B. C. (2013) · 2013
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SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives
Defazio, A., Bach, F., and Lacoste-Julien, S. (2014) · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014) · 2014
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1-bit stochastic gradient descent and its application to data-parallel distributed training of speech dnns
Seide, F., Fu, H., Droppo, J., Li, G., and Yu, D. (2014) · 2014
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Dual averaging method for solving multi-agent saddle-point problems with quantized information
Yuan, D., Ma, Q., and Wang, Z. (2014) · 2014
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Variance reduced stochastic gradient descent with neighbors
Hofmann, T., Lucchi, A., Lacoste-Julien, S., and McWilliams, B. (2015) · 2015
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Stochastic variance reduction methods for saddle-point problems
Palaniappan, B. and Bach, F. (2016) · 2016
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Qsgd: Communication-efficient sgd via gradient quantization and encoding
Alistarh, D., Grubic, D., Li, J., Tomioka, R., and Vojnovic, M. (2017) · 2017
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First-order methods in optimization
Beck, A. (2017) · 2017
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A three-operator splitting scheme and its optimization applications
Davis, D. and Yin, W. (2017) · 2017
Finite-time last-iterate convergence for multi-agent learning in games
Lin, T., Zhou, Z., Mertikopoulos, P., and Jordan, M. (2020) · 2020
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Min-max optimization without gradients: Convergence and applications to black-box evasion and poisoning attacks
Liu, S., Lu, S., Chen, X., Feng, Y., Xu, K., Al-Dujaili, A., Hong, M., and O’Reilly, U.-M. (2020) · 2020
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Stochastic hamiltonian gradient methods for smooth games
Loizou, N., Berard, H., Jolicoeur-Martineau, A., Vincent, P., Lacoste-Julien, S., and Mitliagkas, I. (2020) · 2020
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A forward-backward splitting method for monotone inclusions without cocoercivity
Malitsky, Y. and Tam, M. K. (2020) · 2020
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Revisiting stochastic extragradient
Mishchenko, K., Kovalev, D., Shulgin, E., Richtarik, P., and Malitsky, Y. (2020) · 2020
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Cited alongside, same era.
Terngrad: ternary gradients to reduce communication in distributed deep learning
Wen, W., Xu, C., Yan, F., Wu, C., Wang, Y., Chen, Y., and Li, H. (2017) · 2017
Cited alongside, same era.
SEGA: Variance reduction via gradient sketching
Hanzely, F., Mishchenko, K., and Richtárik, P. (2018) · 2018
Cited alongside, same era.
Catalyst acceleration for first-order convex optimization: from theory to practice
Lin, H., Mairal, J., and Harchaoui, Z. (2018) · 2018
Cited alongside, same era.
Sparsified sgd with memory
Stich, S. U., Cordonnier, J.-B., and Jaggi, M. (2018) · 2018
Cited alongside, same era.
The “ η \eta -trick” or the effectiveness of reweighted least-squares
Bach, F. (2019) · 2019
Cited alongside, same era.
Variance reduction for matrix games
Carmon, Y., Jin, Y., Sidford, A., and Tian, K. (2019) · 2019
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Richtárik, P. and Takác, M. (2020) · 2020
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Optimistic dual extrapolation for coherent non-monotone variational inequalities
Song, C., Zhou, Z., Zhou, Y., Jiang, Y., and Ma, Y. (2020) · 2020
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Global convergence and variance reduction for a class of nonconvex-nonconcave minimax problems
Yang, J., Kiyavash, N., and He, N. (2020) · 2020
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Stochastic variance reduction for variational inequality methods
Alacaoglu, A. and Malitsky, Y. (2021) · 2021
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Forward-reflected-backward method with variance reduction
Alacaoglu, A., Malitsky, Y., and Cevher, V. (2021) · 2021
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The last-iterate convergence rate of optimistic mirror descent in stochastic variational inequalities
Azizian, W., Iutzeler, F., Malick, J., and Mertikopoulos, P. (2021) · 2021
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The complexity of constrained min-max optimization
Daskalakis, C., Skoulakis, S., and Zampetakis, M. (2021) · 2021
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Efficient methods for structured nonconvex-nonconcave min-max optimization
Diakonikolas, J., Daskalakis, C., and Jordan, M. (2021) · 2021
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MARINA: Faster non-convex distributed learning with compression
Gorbunov, E., Burlachenko, K. P., Li, Z., and Richtarik, P. (2021) · 2021
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Sgd for structured nonconvex functions: Learning rates, minibatching and interpolation
Gower, R., Sebbouh, O., and Loizou, N. (2021) · 2021
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Lower complexity bounds of finite-sum optimization problems: The results and construction
Han, Y., Xie, G., and Zhang, Z. (2021) · 2021
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On the convergence of stochastic extragradient for bilinear games with restarted iteration averaging
Li, C. J., Yu, Y., Loizou, N., Gidel, G., Ma, Y., Roux, N. L., and Jordan, M. I. (2021) · 2021
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Stochastic gradient descent-ascent and consensus optimization for smooth games: Convergence analysis under expected co-coercivity
Loizou, N., Berard, H., Gidel, G., Mitliagkas, I., and Lacoste-Julien, S. (2021) · 2021
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Near optimal stochastic algorithms for finite-sum unbalanced convex-concave minimax optimization
Luo, L., Xie, G., Zhang, T., and Zhang, Z. (2021) · 2021
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EF21: A new, simpler, theoretically better, and practically faster error feedback
Richtárik, P., Sokolov, I., and Fatkhullin, I. (2021) · 2021
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Zeroth-order algorithms for smooth saddle-point problems
Sadiev, A., Beznosikov, A., Dvurechensky, P., and Gasnikov, A. (2021) · 2021
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On accelerated methods for saddle-point problems with composite structure
Tominin, V., Tominin, Y., Borodich, E., Kovalev, D., Gasnikov, A., and Dvurechensky, P. (2021) · 2021
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Accelerated algorithms for smooth convex-concave minimax problems with O ( 1 / k 2 ) (1/k^{2}) rate on squared gradient norm
Yoon, T. and Ryu, E. K. (2021) · 2021
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