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Two of the most prominent algorithms for solving unconstrained smooth games are the classical stochastic gradient descent-ascent (SGDA) and the recently introduced stochastic consensus optimization (SCO) [Mescheder et al., 2017].
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Stochastic variance reduction methods for saddle-point problems
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Convergence analysis of two-layer neural networks with relu activation
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The numerics of GAN
L. Mescheder, S. Nowozin, and A. Geiger · 2017
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Minimizing finite sums with the stochastic average gradient
M. Schmidt, N. Le Roux, and F. Bach · 2017
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Hybrid variance-reduced SGD algorithms for minimax problems with nonconvex-linear function
Q. Tran Dinh, D. Liu, and L. Nguyen · 2020
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Global convergence and variance-reduced optimization for a class of nonconvex-nonconcave minimax problems
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Last-iterate convergence rates for min-max optimization: Convergence of Hamiltonian gradient descent and consensus optimization
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Robust power management via learning and game design
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