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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SG-PALM: a Fast Physically Interpretable Tensor Graphical Model Yu Wang Alfred Hero 34 4 0 26 May 2021
Stochastic gradient descent with noise of machine learning type. Part I: Discrete time analysis Stephan Wojtowytsch 21 50 0 04 May 2021
Convergence Analysis and System Design for Federated Learning over Wireless Networks Shuo Wan Jiaxun Lu Pingyi Fan Yunfeng Shao Chenghui Peng Khaled B. Letaief 34 54 0 30 Apr 2021
Decentralized Federated Averaging Tao Sun Dongsheng Li Bao Wang FedML 38 206 0 23 Apr 2021
Local Stochastic Gradient Descent Ascent: Convergence Analysis and Communication Efficiency Yuyang Deng M. Mahdavi 19 58 0 25 Feb 2021
Provable Super-Convergence with a Large Cyclical Learning Rate Samet Oymak 28 12 0 22 Feb 2021
Convergence of stochastic gradient descent schemes for Lojasiewicz-landscapes Steffen Dereich Sebastian Kassing 26 27 0 16 Feb 2021
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients A. Mitra Rayana H. Jaafar George J. Pappas Hamed Hassani FedML 55 157 0 14 Feb 2021
Stochastic Gradient Langevin Dynamics with Variance Reduction Zhishen Huang Stephen Becker 13 7 0 12 Feb 2021
AEGD: Adaptive Gradient Descent with Energy Hailiang Liu Xuping Tian ODL 25 11 0 10 Oct 2020
On Communication Compression for Distributed Optimization on Heterogeneous Data Sebastian U. Stich 45 22 0 04 Sep 2020
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters Filip Hanzely 19 0 0 26 Aug 2020
AdaScale SGD: A User-Friendly Algorithm for Distributed Training Tyler B. Johnson Pulkit Agrawal Haijie Gu Carlos Guestrin ODL 19 37 0 09 Jul 2020
Stochastic Hamiltonian Gradient Methods for Smooth Games Nicolas Loizou Hugo Berard Alexia Jolicoeur-Martineau Pascal Vincent Simon Lacoste-Julien Ioannis Mitliagkas 25 50 0 08 Jul 2020
DeltaGrad: Rapid retraining of machine learning models Yinjun Wu Edgar Dobriban S. Davidson MU 11 194 0 26 Jun 2020
SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation Robert Mansel Gower Othmane Sebbouh Nicolas Loizou 25 74 0 18 Jun 2020
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Linear Last-iterate Convergence in Constrained Saddle-point Optimization Chen-Yu Wei Chung-Wei Lee Mengxiao Zhang Haipeng Luo 8 11 0 16 Jun 2020
Walking in the Shadow: A New Perspective on Descent Directions for Constrained Minimization Hassan Mortagy Swati Gupta S. Pokutta 16 7 0 15 Jun 2020
An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias Lu Yu Krishnakumar Balasubramanian S. Volgushev Murat A. Erdogdu 24 50 0 14 Jun 2020
SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence Sinho Chewi Thibaut Le Gouic Chen Lu Tyler Maunu Philippe Rigollet 25 66 0 03 Jun 2020
Detached Error Feedback for Distributed SGD with Random Sparsification An Xu Heng-Chiao Huang 31 9 0 11 Apr 2020
Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence Nicolas Loizou Sharan Vaswani I. Laradji Simon Lacoste-Julien 11 181 0 24 Feb 2020
Global Convergence and Variance-Reduced Optimization for a Class of Nonconvex-Nonconcave Minimax Problems Junchi Yang Negar Kiyavash Niao He 21 83 0 22 Feb 2020
A Unified Convergence Analysis for Shuffling-Type Gradient Methods Lam M. Nguyen Quoc Tran-Dinh Dzung Phan Phuong Ha Nguyen Marten van Dijk 26 78 0 19 Feb 2020
Better Theory for SGD in the Nonconvex World Ahmed Khaled Peter Richtárik 11 178 0 09 Feb 2020
Complexity Guarantees for Polyak Steps with Momentum Mathieu Barré Adrien B. Taylor Alexandre d’Aspremont 12 26 0 03 Feb 2020
Convergence and sample complexity of gradient methods for the model-free linear quadratic regulator problem Hesameddin Mohammadi A. Zare Mahdi Soltanolkotabi M. Jovanović 30 121 0 26 Dec 2019
Fast Stochastic Ordinal Embedding with Variance Reduction and Adaptive Step Size Ke Ma Jinshan Zeng Qianqian Xu Xiaochun Cao Wei Liu Yuan Yao 20 3 0 01 Dec 2019
On the Convergence of Local Descent Methods in Federated Learning Farzin Haddadpour M. Mahdavi FedML 16 265 0 31 Oct 2019
Linear-Quadratic Mean-Field Reinforcement Learning: Convergence of Policy Gradient Methods René Carmona Mathieu Laurière Zongjun Tan 35 61 0 09 Oct 2019
Stochastic gradient descent for hybrid quantum-classical optimization R. Sweke Frederik Wilde Johannes Jakob Meyer Maria Schuld Paul K. Fährmann Barthélémy Meynard-Piganeau Jens Eisert 17 236 0 02 Oct 2019
Differentially Private Meta-Learning Jeffrey Li M. Khodak S. Caldas Ameet Talwalkar FedML 23 106 0 12 Sep 2019
On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift Alekh Agarwal Sham Kakade J. Lee G. Mahajan 11 315 0 01 Aug 2019
Adversarial Attack Generation Empowered by Min-Max Optimization Jingkang Wang Tianyun Zhang Sijia Liu Pin-Yu Chen Jiacen Xu M. Fardad B. Li AAML 23 35 0 09 Jun 2019
Global Optimality Guarantees For Policy Gradient Methods Jalaj Bhandari Daniel Russo 21 185 0 05 Jun 2019
Controlling Neural Networks via Energy Dissipation Michael Möller Thomas Möllenhoff Daniel Cremers 25 17 0 05 Apr 2019
Provable Guarantees for Gradient-Based Meta-Learning M. Khodak Maria-Florina Balcan Ameet Talwalkar FedML 17 147 0 27 Feb 2019
ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization Nhan H. Pham Lam M. Nguyen Dzung Phan Quoc Tran-Dinh 11 139 0 15 Feb 2019
Solving Non-Convex Non-Concave Min-Max Games Under Polyak-Łojasiewicz Condition Maziar Sanjabi Meisam Razaviyayn J. Lee 6 35 0 07 Dec 2018
Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptron Sharan Vaswani Francis R. Bach Mark W. Schmidt 28 296 0 16 Oct 2018
Exponential Convergence Time of Gradient Descent for One-Dimensional Deep Linear Neural Networks Ohad Shamir 22 45 0 23 Sep 2018
SEGA: Variance Reduction via Gradient Sketching Filip Hanzely Konstantin Mishchenko Peter Richtárik 23 71 0 09 Sep 2018
Convergence of Cubic Regularization for Nonconvex Optimization under KL Property Yi Zhou Zhe Wang Yingbin Liang 19 23 0 22 Aug 2018
Stochastic Nested Variance Reduction for Nonconvex Optimization Dongruo Zhou Pan Xu Quanquan Gu 25 146 0 20 Jun 2018