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Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition

16 August 2016

Papers citing "Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition"

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Title
Adversarial Attack Generation Empowered by Min-Max Optimization Jingkang Wang Tianyun Zhang Sijia Liu Pin-Yu Chen Jiacen Xu M. Fardad Yangqiu Song AAML 74 37 0 09 Jun 2019
Last-iterate convergence rates for min-max optimization Jacob D. Abernethy Kevin A. Lai Andre Wibisono 114 74 0 05 Jun 2019
Global Optimality Guarantees For Policy Gradient Methods Jalaj Bhandari Daniel Russo 109 194 0 05 Jun 2019
Sparse optimal control of networks with multiplicative noise via policy gradient Benjamin J. Gravell Yi Guo Tyler H. Summers 26 3 0 28 May 2019
Learning robust control for LQR systems with multiplicative noise via policy gradient Benjamin J. Gravell Peyman Mohajerin Esfahani Tyler H. Summers 103 26 0 28 May 2019
Sample Complexity of Sample Average Approximation for Conditional Stochastic Optimization Yifan Hu Xin Chen Niao He 91 36 0 28 May 2019
One Method to Rule Them All: Variance Reduction for Data, Parameters and Many New Methods Filip Hanzely Peter Richtárik 90 27 0 27 May 2019
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates Sharan Vaswani Aaron Mishkin I. Laradji Mark Schmidt Gauthier Gidel Simon Lacoste-Julien ODL 111 210 0 24 May 2019
Game Theoretic Optimization via Gradient-based Nikaido-Isoda Function A. Raghunathan A. Cherian Devesh K. Jha 58 21 0 15 May 2019
On the Computation and Communication Complexity of Parallel SGD with Dynamic Batch Sizes for Stochastic Non-Convex Optimization Hao Yu Rong Jin 76 51 0 10 May 2019
On Structured Filtering-Clustering: Global Error Bound and Optimal First-Order Algorithms Nhat Ho Tianyi Lin Michael I. Jordan 126 2 0 16 Apr 2019
The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent Karthik A. Sankararaman Soham De Zheng Xu Wenjie Huang Tom Goldstein ODL 112 106 0 15 Apr 2019
Controlling Neural Networks via Energy Dissipation Michael Möller Thomas Möllenhoff Daniel Cremers 70 17 0 05 Apr 2019
Convergence rates for the stochastic gradient descent method for non-convex objective functions Benjamin J. Fehrman Benjamin Gess Arnulf Jentzen 98 101 0 02 Apr 2019
Provable Guarantees for Gradient-Based Meta-Learning M. Khodak Maria-Florina Balcan Ameet Talwalkar FedML 142 150 0 27 Feb 2019
A Dictionary-Based Generalization of Robust PCA Part II: Applications to Hyperspectral Demixing Sirisha Rambhatla Xingguo Li Jineng Ren Jarvis Haupt 59 6 0 26 Feb 2019
Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods Maher Nouiehed Maziar Sanjabi Tianjian Huang Jason D. Lee Meisam Razaviyayn 113 344 0 21 Feb 2019
ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization Nhan H. Pham Lam M. Nguyen Dzung Phan Quoc Tran-Dinh 80 141 0 15 Feb 2019
An adaptive stochastic optimization algorithm for resource allocation Xavier Fontaine Shie Mannor Vianney Perchet 26 12 0 12 Feb 2019
Stochastic first-order methods: non-asymptotic and computer-aided analyses via potential functions Adrien B. Taylor Francis R. Bach 79 64 0 03 Feb 2019
Stochastic Gradient Descent for Nonconvex Learning without Bounded Gradient Assumptions Yunwen Lei Ting Hu Guiying Li K. Tang MLT 95 119 0 03 Feb 2019
ErasureHead: Distributed Gradient Descent without Delays Using Approximate Gradient Coding Hongyi Wang Zachary B. Charles Dimitris Papailiopoulos 57 55 0 28 Jan 2019
SGD: General Analysis and Improved Rates Robert Mansel Gower Nicolas Loizou Xun Qian Alibek Sailanbayev Egor Shulgin Peter Richtárik 109 383 0 27 Jan 2019
Surrogate Losses for Online Learning of Stepsizes in Stochastic Non-Convex Optimization Zhenxun Zhuang Ashok Cutkosky Francesco Orabona 117 5 0 25 Jan 2019
Overparameterized Nonlinear Learning: Gradient Descent Takes the Shortest Path? Samet Oymak Mahdi Soltanolkotabi ODL 73 177 0 25 Dec 2018
Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems Dhruv Malik A. Pananjady Kush S. Bhatia K. Khamaru Peter L. Bartlett Martin J. Wainwright 73 201 0 20 Dec 2018
Stagewise Training Accelerates Convergence of Testing Error Over SGD Zhuoning Yuan Yan Yan Rong Jin Tianbao Yang 111 11 0 10 Dec 2018
Solving Non-Convex Non-Concave Min-Max Games Under Polyak-Łojasiewicz Condition Maziar Sanjabi Meisam Razaviyayn Jason D. Lee 60 35 0 07 Dec 2018
Inexact SARAH Algorithm for Stochastic Optimization Lam M. Nguyen K. Scheinberg Martin Takáč 93 51 0 25 Nov 2018
On exponential convergence of SGD in non-convex over-parametrized learning Xinhai Liu M. Belkin Yu-Shen Liu 88 103 0 06 Nov 2018
Uniform Convergence of Gradients for Non-Convex Learning and Optimization Dylan J. Foster Ayush Sekhari Karthik Sridharan 82 68 0 25 Oct 2018
SpiderBoost and Momentum: Faster Stochastic Variance Reduction Algorithms Zhe Wang Kaiyi Ji Yi Zhou Yingbin Liang Vahid Tarokh ODL 98 82 0 25 Oct 2018
Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptron Sharan Vaswani Francis R. Bach Mark Schmidt 116 301 0 16 Oct 2018
Efficient Greedy Coordinate Descent for Composite Problems Sai Praneeth Karimireddy Anastasia Koloskova Sebastian U. Stich Martin Jaggi 58 30 0 16 Oct 2018
Continuous-time Models for Stochastic Optimization Algorithms Antonio Orvieto Aurelien Lucchi 119 32 0 05 Oct 2018
Newton-MR: Inexact Newton Method With Minimum Residual Sub-problem Solver Fred Roosta Yang Liu Peng Xu Michael W. Mahoney 69 15 0 30 Sep 2018
Exponential Convergence Time of Gradient Descent for One-Dimensional Deep Linear Neural Networks Ohad Shamir 110 47 0 23 Sep 2018
SEGA: Variance Reduction via Gradient Sketching Filip Hanzely Konstantin Mishchenko Peter Richtárik 92 71 0 09 Sep 2018
Convergence of Cubic Regularization for Nonconvex Optimization under KL Property Yi Zhou Zhe Wang Yingbin Liang 80 23 0 22 Aug 2018
Discrete linear-complexity reinforcement learning in continuous action spaces for Q-learning algorithms P. Tavallali G. Doran L. Mandrake 28 0 0 16 Jul 2018
Accelerating likelihood optimization for ICA on real signals Pierre Ablin J. Cardoso Alexandre Gramfort 30 2 0 25 Jun 2018
Stochastic Nested Variance Reduction for Nonconvex Optimization Dongruo Zhou Pan Xu Quanquan Gu 85 147 0 20 Jun 2018
ATOMO: Communication-efficient Learning via Atomic Sparsification Hongyi Wang Scott Sievert Zachary B. Charles Shengchao Liu S. Wright Dimitris Papailiopoulos 95 356 0 11 Jun 2018
LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning Tianyi Chen G. Giannakis Tao Sun W. Yin 60 299 0 25 May 2018
On the Convergence of Stochastic Gradient Descent with Adaptive Stepsizes Xiaoyun Li Francesco Orabona 89 299 0 21 May 2018
Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions Mingrui Liu Xiaoxuan Zhang Lijun Zhang Rong Jin Tianbao Yang 86 25 0 11 May 2018
Frank-Wolfe Splitting via Augmented Lagrangian Method Gauthier Gidel Fabian Pedregosa Simon Lacoste-Julien 64 30 0 09 Apr 2018
Revisiting Decomposable Submodular Function Minimization with Incidence Relations Pan Li O. Milenkovic 134 19 0 10 Mar 2018
Sampling as optimization in the space of measures: The Langevin dynamics as a composite optimization problem Andre Wibisono 123 183 0 22 Feb 2018
Generalization Error Bounds with Probabilistic Guarantee for SGD in Nonconvex Optimization Yi Zhou Yingbin Liang Huishuai Zhang MLT 85 26 0 19 Feb 2018