Fetching the paper…
Reading the bibliography…
Gradient descent ascent (GDA), the simplest single-loop algorithm for nonconvex minimax optimization, is widely used in practical applications such as generative adversarial networks (GANs) and adversarial training.
Gradient methods for minimizing functionals
Boris Teodorovich Polyak · 1963
Earlier work this paper cites.
Loss landscapes and optimization in over-parameterized non-linear systems and neural networks
Chaoyue Liu, Libin Zhu, and Mikhail Belkin · 2003
Earlier work this paper cites.
Fast objective and duality gap convergence for non-convex strongly-concave min-max problems
Zhishuai Guo, Zhuoning Yuan, Yan Yan, and Tianbao Yang · 2006
Earlier work this paper cites.
Gradient free minimax optimization: Variance reduction and faster convergence
Tengyu Xu, Zhe Wang, Yingbin Liang, and H Vincent Poor · 2006
Earlier work this paper cites.
Zi Xu, Huiling Zhang, Yang Xu, and Guanghui Lan · 2006
Earlier work this paper cites.
Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Tijmen Tieleman, Geoffrey Hinton, et al · 2012
Earlier work this paper cites.
Characterization and computation of local nash equilibria in continuous games
Lillian J Ratliff, Samuel A Burden, and S Shankar Sastry · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Linear convergence of gradient and proximal-gradient methods under the polyak-łojasiewicz condition
Hamed Karimi, Julie Nutini, and Mark Schmidt · 2016
Earlier work this paper cites.
On the characterization of local nash equilibria in continuous games
Lillian J Ratliff, Samuel A Burden, and S Shankar Sastry · 2016
Earlier work this paper cites.
Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Earlier work this paper cites.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
Earlier work this paper cites.
Deep neural networks as gaussian processes
Jaehoon Lee, Yasaman Bahri, Roman Novak, Samuel S Schoenholz, Jeffrey Pennington, and Jascha Sohl-Dickstein · 2017
Earlier work this paper cites.
The numerics of gans
Lars Mescheder, Sebastian Nowozin, and Andreas Geiger · 2017
Earlier work this paper cites.
Gradient descent gan optimization is locally stable
Vaishnavh Nagarajan and J Zico Kolter · 2017
Earlier work this paper cites.
Stochastic mirror descent in variationally coherent optimization problems
Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos, Stephen Boyd, and Peter W Glynn · 2017
Earlier work this paper cites.
The limit points of (optimistic) gradient descent in min-max optimization
Constantinos Daskalakis and Ioannis Panageas · 2018
Earlier work this paper cites.
Global convergence of policy gradient methods for the linear quadratic regulator
Maryam Fazel, Rong Ge, Sham Kakade, and Mehran Mesbahi · 2018
Earlier work this paper cites.
Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
Earlier work this paper cites.
Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile
Panayotis Mertikopoulos, Bruno Lecouat, Houssam Zenati, Chuan-Sheng Foo, Vijay Chandrasekhar, and Georgios Piliouras · 2018
Earlier work this paper cites.
Which training methods for gans do actually converge?
Lars Mescheder, Andreas Geiger, and Sebastian Nowozin · 2018
Earlier work this paper cites.
Local saddle point optimization: A curvature exploitation approach
Leonard Adolphs, Hadi Daneshmand, Aurelien Lucchi, and Thomas Hofmann · 2019
Earlier work this paper cites.
Lower bounds for non-convex stochastic optimization
Yossi Arjevani, Yair Carmon, John C Duchi, Dylan J Foster, Nathan Srebro, and Blake Woodworth · 2019
Earlier work this paper cites.
On the global convergence of imitation learning: A case for linear quadratic regulator
Qi Cai, Mingyi Hong, Yongxin Chen, and Zhaoran Wang · 2019
Cited alongside, same era.
Efficiency of minimizing compositions of convex functions and smooth maps
Dmitriy Drusvyatskiy and Courtney Paquette · 2019
Cited alongside, same era.
Gradient descent finds global minima of deep neural networks
Simon Du, Jason Lee, Haochuan Li, Liwei Wang, and Xiyu Zhai · 2019
Cited alongside, same era.
Towards better understanding of adaptive gradient algorithms in generative adversarial nets
Mingrui Liu, Youssef Mroueh, Jerret Ross, Wei Zhang, Xiaodong Cui, Payel Das, and Tianbao Yang · 2019
Cited alongside, same era.
Solving a class of non-convex min-max games using iterative first order methods
Maher Nouiehed, Maziar Sanjabi, Tianjian Huang, Jason D Lee, and Meisam Razaviyayn · 2019
Cited alongside, same era.
Hybrid variance-reduced sgd algorithms for minimax problems with nonconvex-linear function
Quoc Tran-Dinh, Deyi Liu, and Lam M Nguyen · 2020
Later among the works it cites.
Zeroth-order algorithms for nonconvex minimax problems with improved complexities
Zhongruo Wang, Krishnakumar Balasubramanian, Shiqian Ma, and Meisam Razaviyayn · 2020
Later among the works it cites.
A single-loop smoothed gradient descent-ascent algorithm for nonconvex-concave min-max problems
Jiawei Zhang, Peijun Xiao, Ruoyu Sun, and Zhi-Quan Luo · 2020
Later among the works it cites.
A primal dual smoothing framework for max-structured nonconvex optimization
Renbo Zhao · 2020
Later among the works it cites.
Last-iterate convergence rates for min-max optimization: Convergence of hamiltonian gradient descent and consensus optimization
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Efficient algorithms for smooth minimax optimization
Kiran K Thekumparampil, Prateek Jain, Praneeth Netrapalli, and Sewoong Oh · 2019
Cited alongside, same era.
On solving minimax optimization locally: A follow-the-ridge approach
Yuanhao Wang, Guodong Zhang, and Jimmy Ba · 2019
Cited alongside, same era.
Alternating proximal-gradient steps for (stochastic) nonconvex-concave minimax problems
Radu Ioan Boţ and Axel Böhm · 2020
Cited alongside, same era.
Lower bounds for finding stationary points i
Yair Carmon, John C Duchi, Oliver Hinder, and Aaron Sidford · 2020
Cited alongside, same era.
Local convergence analysis of gradient descent ascent with finite timescale separation
Tanner Fiez and Lillian J Ratliff · 2020
Cited alongside, same era.
The landscape of the proximal point method for nonconvex-nonconcave minimax optimization
Benjamin Grimmer, Haihao Lu, Pratik Worah, and Vahab Mirrokni · 2020
Cited alongside, same era.
Mingyi Hong, Hoi-To Wai, Zhaoran Wang, and Zhuoran Yang · 2020
Cited alongside, same era.
Jacob Abernethy, Kevin A Lai, and Andre Wibisono · 2021
Closest in time.
Direct-search for a class of stochastic min-max problems
Sotirios-Konstantinos Anagnostidis, Aurelien Lucchi, and Youssef Diouane · 2021
Closest in time.
Ziyi Chen and Yi Zhou · 2021
Closest in time.
The complexity of constrained min-max optimization
C. Daskalakis, Stratis Skoulakis, and M. Zampetakis · 2021
Closest in time.
Efficient methods for structured nonconvex-nonconcave min-max optimization
Jelena Diakonikolas, Constantinos Daskalakis, and Michael Jordan · 2021
Closest in time.
Global convergence to local minmax equilibrium in classes of nonconvex zero-sum games
Tanner Fiez, Lillian J Ratliff, Eric Mazumdar, Evan Faulkner, and Adhyyan Narang · 2021
Closest in time.
Randomized stochastic variance-reduced methods for stochastic bilevel optimization
Zhishuai Guo and Tianbao Yang · 2021
Closest in time.
Lower complexity bounds of finite-sum optimization problems: The results and construction
Yuze Han, Guangzeng Xie, and Zhihua Zhang · 2021
Closest in time.
The limits of min-max optimization algorithms: Convergence to spurious non-critical sets
Ya-Ping Hsieh, Panayotis Mertikopoulos, and Volkan Cevher · 2021
Closest in time.
Adagda: Faster adaptive gradient descent ascent methods for minimax optimization
Feihu Huang and Heng Huang · 2021
Closest in time.
Complexity lower bounds for nonconvex-strongly-concave min-max optimization
Haochuan Li, Yi Tian, Jingzhao Zhang, and Ali Jadbabaie · 2021
Closest in time.
First-order convergence theory for weakly-convex-weakly-concave min-max problems
Mingrui Liu, Hassan Rafique, Qihang Lin, and Tianbao Yang · 2021
Closest in time.
An o ( s r ) o(s^{r}) -resolution ode framework for understanding discrete-time algorithms and applications to the linear convergence of minimax problems
Haihao Lu · 2021
Closest in time.
Finding second-order stationary point for nonconvex-strongly-concave minimax problem
Luo Luo and Cheng Chen · 2021
Closest in time.
Near optimal stochastic algorithms for finite-sum unbalanced convex-concave minimax optimization
Luo Luo, Guangzeng Xie, Tong Zhang, and Zhihua Zhang · 2021
Closest in time.
Greedy adversarial equilibrium: an efficient alternative to nonconvex-nonconcave min-max optimization
Oren Mangoubi and Nisheeth K Vishnoi · 2021
Closest in time.
Weakly-convex–concave min–max optimization: provable algorithms and applications in machine learning
Hassan Rafique, Mingrui Liu, Qihang Lin, and Tianbao Yang · 2021
Closest in time.
A federated learning framework for nonconvex-pl minimax problems
Jiahao Xie, Chao Zhang, Yunsong Zhang, Zebang Shen, and Hui Qian · 2021
Closest in time.
Zi Xu, Jingjing Shen, Ziqi Wang, and Yuhong Dai · 2021
Closest in time.