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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"

38 / 588 papers shown

Title
Differentially Private Empirical Risk Minimization Revisited: Faster and More General Di Wang Minwei Ye Jinhui Xu 130 273 0 14 Feb 2018
Logarithmic Regret for Online Gradient Descent Beyond Strong Convexity Dan Garber 78 6 0 13 Feb 2018
Fast Global Convergence via Landscape of Empirical Loss Chao Qu Yan Li Huan Xu 19 0 0 13 Feb 2018
A Simple Proximal Stochastic Gradient Method for Nonsmooth Nonconvex Optimization Zhize Li Jian Li 97 116 0 13 Feb 2018
signSGD: Compressed Optimisation for Non-Convex Problems Jeremy Bernstein Yu Wang Kamyar Azizzadenesheli Anima Anandkumar FedML ODL 118 1,050 0 13 Feb 2018
On the Proximal Gradient Algorithm with Alternated Inertia F. Iutzeler J. Malick 36 33 0 17 Jan 2018
Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator Maryam Fazel Rong Ge Sham Kakade M. Mesbahi 102 611 0 15 Jan 2018
A Stochastic Trust Region Algorithm Based on Careful Step Normalization Frank E. Curtis K. Scheinberg R. Shi 66 45 0 29 Dec 2017
Run-and-Inspect Method for Nonconvex Optimization and Global Optimality Bounds for R-Local Minimizers Yifan Chen Yuejiao Sun W. Yin 38 5 0 22 Nov 2017
Riemannian Optimization via Frank-Wolfe Methods Melanie Weber S. Sra 69 33 0 30 Oct 2017
Stability and Generalization of Learning Algorithms that Converge to Global Optima Zachary B. Charles Dimitris Papailiopoulos MLT 57 163 0 23 Oct 2017
Characterization of Gradient Dominance and Regularity Conditions for Neural Networks Yi Zhou Yingbin Liang 78 33 0 18 Oct 2017
A Modular Analysis of Adaptive (Non-)Convex Optimization: Optimism, Composite Objectives, and Variational Bounds Pooria Joulani András Gyorgy Csaba Szepesvári 55 42 0 08 Sep 2017
Nonconvex Sparse Logistic Regression with Weakly Convex Regularization Xinyue Shen Yuantao Gu 122 31 0 07 Aug 2017
A Unified Analysis of Stochastic Optimization Methods Using Jump System Theory and Quadratic Constraints Bin Hu Peter M. Seiler Anders Rantzer 121 35 0 25 Jun 2017
Gradient Diversity: a Key Ingredient for Scalable Distributed Learning Dong Yin A. Pananjady Max Lam Dimitris Papailiopoulos Kannan Ramchandran Peter L. Bartlett 77 11 0 18 Jun 2017
YellowFin and the Art of Momentum Tuning Jian Zhang Ioannis Mitliagkas ODL 94 108 0 12 Jun 2017
Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients Lukas Balles Philipp Hennig 100 169 0 22 May 2017
Convergence Analysis of Proximal Gradient with Momentum for Nonconvex Optimization Qunwei Li Yi Zhou Yingbin Liang P. Varshney 127 94 0 14 May 2017
Linear Convergence of Accelerated Stochastic Gradient Descent for Nonconvex Nonsmooth Optimization Feihu Huang Songcan Chen 42 2 0 26 Apr 2017
Faster Subgradient Methods for Functions with Hölderian Growth Patrick R. Johnstone P. Moulin 57 35 0 01 Apr 2017
Convergence of the Forward-Backward Algorithm: Beyond the Worst Case with the Help of Geometry Guillaume Garrigos Lorenzo Rosasco S. Villa 92 42 0 28 Mar 2017
Online Learning Rate Adaptation with Hypergradient Descent A. G. Baydin R. Cornish David Martínez-Rubio Mark Schmidt Frank Wood ODL 92 250 0 14 Mar 2017
Learn-and-Adapt Stochastic Dual Gradients for Network Resource Allocation Tianyi Chen Qing Ling G. Giannakis 69 20 0 05 Mar 2017
How to Escape Saddle Points Efficiently Chi Jin Rong Ge Praneeth Netrapalli Sham Kakade Michael I. Jordan ODL 237 838 0 02 Mar 2017
SAGA and Restricted Strong Convexity Chao Qu Yan Li Huan Xu 41 5 0 19 Feb 2017
Linear convergence of SDCA in statistical estimation Chao Qu Huan Xu 75 8 0 26 Jan 2017
Symmetry, Saddle Points, and Global Optimization Landscape of Nonconvex Matrix Factorization Xingguo Li Junwei Lu R. Arora Jarvis Haupt Han Liu Zhaoran Wang T. Zhao 92 53 0 29 Dec 2016
Projected Semi-Stochastic Gradient Descent Method with Mini-Batch Scheme under Weak Strong Convexity Assumption Jie Liu Martin Takáč ODL 117 4 0 16 Dec 2016
The Physical Systems Behind Optimization Algorithms Lin F. Yang R. Arora Vladimir Braverman T. Zhao AI4CE 70 19 0 08 Dec 2016
Adaptive Accelerated Gradient Converging Methods under Holderian Error Bound Condition Mingrui Liu Tianbao Yang 91 15 0 23 Nov 2016
Identity Matters in Deep Learning Moritz Hardt Tengyu Ma OOD 101 399 0 14 Nov 2016
CoCoA: A General Framework for Communication-Efficient Distributed Optimization Virginia Smith Simone Forte Chenxin Ma Martin Takáč Michael I. Jordan Martin Jaggi 103 273 0 07 Nov 2016
Linear Convergence of SVRG in Statistical Estimation Chao Qu Yan Li Huan Xu 61 11 0 07 Nov 2016
Big Batch SGD: Automated Inference using Adaptive Batch Sizes Soham De A. Yadav David Jacobs Tom Goldstein ODL 177 62 0 18 Oct 2016
Accelerating Stochastic Composition Optimization Mengdi Wang Ji Liu Ethan X. Fang 86 148 0 25 Jul 2016
Accelerate Stochastic Subgradient Method by Leveraging Local Growth Condition Yi Tian Xu Qihang Lin Tianbao Yang 106 11 0 04 Jul 2016
RSG: Beating Subgradient Method without Smoothness and Strong Convexity Tianbao Yang Qihang Lin 138 85 0 09 Dec 2015