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Global Convergence and Variance-Reduced Optimization for a Class of
  Nonconvex-Nonconcave Minimax Problems

Global Convergence and Variance-Reduced Optimization for a Class of Nonconvex-Nonconcave Minimax Problems

22 February 2020
Junchi Yang
Negar Kiyavash
Niao He
ArXivPDFHTML

Papers citing "Global Convergence and Variance-Reduced Optimization for a Class of Nonconvex-Nonconcave Minimax Problems"

12 / 12 papers shown
Title
Can We Find Nash Equilibria at a Linear Rate in Markov Games?
Can We Find Nash Equilibria at a Linear Rate in Markov Games?
Zhuoqing Song
Jason D. Lee
Zhuoran Yang
16
8
0
03 Mar 2023
Escaping limit cycles: Global convergence for constrained
  nonconvex-nonconcave minimax problems
Escaping limit cycles: Global convergence for constrained nonconvex-nonconcave minimax problems
Thomas Pethick
P. Latafat
Panagiotis Patrinos
Olivier Fercoq
V. Cevher
12
45
0
20 Feb 2023
Decentralized Stochastic Gradient Descent Ascent for Finite-Sum Minimax
  Problems
Decentralized Stochastic Gradient Descent Ascent for Finite-Sum Minimax Problems
Hongchang Gao
10
16
0
06 Dec 2022
An Optimal Transport Approach to Personalized Federated Learning
An Optimal Transport Approach to Personalized Federated Learning
Farzan Farnia
Amirhossein Reisizadeh
Ramtin Pedarsani
Ali Jadbabaie
OT
OOD
FedML
14
12
0
06 Jun 2022
A Decentralized Adaptive Momentum Method for Solving a Class of Min-Max
  Optimization Problems
A Decentralized Adaptive Momentum Method for Solving a Class of Min-Max Optimization Problems
Babak Barazandeh
Tianjian Huang
George Michailidis
11
12
0
10 Jun 2021
Complexity Lower Bounds for Nonconvex-Strongly-Concave Min-Max
  Optimization
Complexity Lower Bounds for Nonconvex-Strongly-Concave Min-Max Optimization
Haochuan Li
Yi Tian
Jingzhao Zhang
Ali Jadbabaie
9
40
0
18 Apr 2021
Stochastic Hamiltonian Gradient Methods for Smooth Games
Stochastic Hamiltonian Gradient Methods for Smooth Games
Nicolas Loizou
Hugo Berard
Alexia Jolicoeur-Martineau
Pascal Vincent
Simon Lacoste-Julien
Ioannis Mitliagkas
14
51
0
08 Jul 2020
Robust Federated Learning: The Case of Affine Distribution Shifts
Robust Federated Learning: The Case of Affine Distribution Shifts
Amirhossein Reisizadeh
Farzan Farnia
Ramtin Pedarsani
Ali Jadbabaie
FedML
OOD
22
160
0
16 Jun 2020
The Landscape of the Proximal Point Method for Nonconvex-Nonconcave
  Minimax Optimization
The Landscape of the Proximal Point Method for Nonconvex-Nonconcave Minimax Optimization
Benjamin Grimmer
Haihao Lu
Pratik Worah
Vahab Mirrokni
10
9
0
15 Jun 2020
Linear Convergence of the Primal-Dual Gradient Method for Convex-Concave
  Saddle Point Problems without Strong Convexity
Linear Convergence of the Primal-Dual Gradient Method for Convex-Concave Saddle Point Problems without Strong Convexity
S. Du
Wei Hu
53
119
0
05 Feb 2018
Linear Convergence of Gradient and Proximal-Gradient Methods Under the
  Polyak-Łojasiewicz Condition
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition
Hamed Karimi
J. Nutini
Mark W. Schmidt
119
1,190
0
16 Aug 2016
A Proximal Stochastic Gradient Method with Progressive Variance
  Reduction
A Proximal Stochastic Gradient Method with Progressive Variance Reduction
Lin Xiao
Tong Zhang
ODL
76
736
0
19 Mar 2014
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