A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Differentiable Games

By Waïss Azizian, Ioannis Mitliagkas, Simon Lacoste-Julien, Gauthier Gidel. 2020

66 citations recorded by the source corpus.

Subfields: Optimization & Training

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In Optimization & Training

1. Gauthier Gidel
2. Simon Lacoste-Julien
3. Nicolas Loizou
4. Ioannis Mitliagkas
5. Hugo Berard
6. Peter Richtárik
7. Sebastian U. Stich
8. Martin Jaggi

Citations in the corpus

• Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity (2021, background): “…that CO can be viewed as a perturbation of the deterministic Hamiltonian gradient descent (HGD) and explain how CO converges at the same rate as HGD, while Azizian et al. [2020] prove convergence of CO for µ -strongly monotone operators with positive singular values of the Jacobian matrix J = ∇ ξ .”
• Stochastic Hamiltonian Gradient Methods for Smooth Games (2020, neutral): “…of the problem has been studied in a number of classic (Korpelevich, 1976; Nemirovski, 2004) and recent results (Mescheder et al., 2017; Ibrahim et al., 2019; Gidel et al., 2018; Daskalakis et al., 2018; Gidel et al., 2019; Mokhtari et al., 2020; Azizian et al., 2020a;b) in various settings.”