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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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Optimization of Graph Total Variation via Active-Set-based Combinatorial Reconditioning Zhenzhang Ye Thomas Möllenhoff Tao Wu Daniel Cremers 8 3 0 27 Feb 2020
PrIU: A Provenance-Based Approach for Incrementally Updating Regression Models Yinjun Wu V. Tannen S. Davidson 78 37 0 26 Feb 2020
Stagewise Enlargement of Batch Size for SGD-based Learning Shen-Yi Zhao Yin-Peng Xie Wu-Jun Li 20 1 0 26 Feb 2020
Proximal Gradient Algorithm with Momentum and Flexible Parameter Restart for Nonconvex Optimization Yi Zhou Zhe Wang Kaiyi Ji Yingbin Liang Vahid Tarokh 38 8 0 26 Feb 2020
Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence Nicolas Loizou Sharan Vaswani I. Laradji Simon Lacoste-Julien 105 188 0 24 Feb 2020
Global Convergence and Variance-Reduced Optimization for a Class of Nonconvex-Nonconcave Minimax Problems Junchi Yang Negar Kiyavash Niao He 95 84 0 22 Feb 2020
Stochastic Subspace Cubic Newton Method Filip Hanzely N. Doikov Peter Richtárik Y. Nesterov 48 53 0 21 Feb 2020
Data Heterogeneity Differential Privacy: From Theory to Algorithm Yilin Kang Jian Li Yong Liu Weiping Wang 65 1 0 20 Feb 2020
Input Perturbation: A New Paradigm between Central and Local Differential Privacy Yilin Kang Yong Liu Ben Niu Xin-Yi Tong Likun Zhang Weiping Wang 64 11 0 20 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 104 79 0 19 Feb 2020
The Geometry of Sign Gradient Descent Lukas Balles Fabian Pedregosa Nicolas Le Roux ODL 72 27 0 19 Feb 2020
A Second look at Exponential and Cosine Step Sizes: Simplicity, Adaptivity, and Performance Xiaoyun Li Zhenxun Zhuang Francesco Orabona 73 20 0 12 Feb 2020
Better Theory for SGD in the Nonconvex World Ahmed Khaled Peter Richtárik 101 186 0 09 Feb 2020
Almost Sure Convergence of Dropout Algorithms for Neural Networks Albert Senen-Cerda J. Sanders 57 8 0 06 Feb 2020
Complexity Guarantees for Polyak Steps with Momentum Mathieu Barré Adrien B. Taylor Alexandre d’Aspremont 65 27 0 03 Feb 2020
Resolving learning rates adaptively by locating Stochastic Non-Negative Associated Gradient Projection Points using line searches D. Kafka D. Wilke 39 8 0 15 Jan 2020
Choosing the Sample with Lowest Loss makes SGD Robust Vatsal Shah Xiaoxia Wu Sujay Sanghavi 52 44 0 10 Jan 2020
Gradient descent algorithms for Bures-Wasserstein barycenters Sinho Chewi Tyler Maunu Philippe Rigollet Austin J. Stromme 58 87 0 06 Jan 2020
Convergence and sample complexity of gradient methods for the model-free linear quadratic regulator problem Hesameddin Mohammadi A. Zare Mahdi Soltanolkotabi M. Jovanović 87 124 0 26 Dec 2019
Advances and Open Problems in Federated Learning Peter Kairouz H. B. McMahan Brendan Avent A. Bellet M. Bennis ... Zheng Xu Qiang Yang Felix X. Yu Han Yu Sen Zhao FedML AI4CE 287 6,315 0 10 Dec 2019
On the rate of convergence of a neural network regression estimate learned by gradient descent Alina Braun Michael Kohler Harro Walk 23 10 0 09 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 60 3 0 01 Dec 2019
Convergence Analysis of a Momentum Algorithm with Adaptive Step Size for Non Convex Optimization Anas Barakat Pascal Bianchi 67 12 0 18 Nov 2019
Revisiting the Approximate Carathéodory Problem via the Frank-Wolfe Algorithm Cyrille W. Combettes Sebastian Pokutta 90 26 0 11 Nov 2019
On the Convergence of Local Descent Methods in Federated Learning Farzin Haddadpour M. Mahdavi FedML 91 275 0 31 Oct 2019
Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization Farzin Haddadpour Mohammad Mahdi Kamani M. Mahdavi V. Cadambe FedML 87 201 0 30 Oct 2019
Weighted Distributed Differential Privacy ERM: Convex and Non-convex Yilin Kang Yong Liu Weiping Wang 63 10 0 23 Oct 2019
First-Order Preconditioning via Hypergradient Descent Theodore H. Moskovitz Rui Wang Janice Lan Sanyam Kapoor Thomas Miconi J. Yosinski Aditya Rawal AI4CE 75 8 0 18 Oct 2019
Improving the convergence of SGD through adaptive batch sizes Scott Sievert Zachary B. Charles ODL 63 8 0 18 Oct 2019
Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation S. Meng Sharan Vaswani I. Laradji Mark Schmidt Simon Lacoste-Julien 96 34 0 11 Oct 2019
Nearly Minimal Over-Parametrization of Shallow Neural Networks Armin Eftekhari Chaehwan Song Volkan Cevher 45 1 0 09 Oct 2019
Linear-Quadratic Mean-Field Reinforcement Learning: Convergence of Policy Gradient Methods René Carmona Mathieu Laurière Zongjun Tan 99 63 0 09 Oct 2019
Distributed Learning of Deep Neural Networks using Independent Subnet Training John Shelton Hyatt Cameron R. Wolfe Michael Lee Yuxin Tang Anastasios Kyrillidis Christopher M. Jermaine OOD 92 39 0 04 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 86 240 0 02 Oct 2019
Randomized Iterative Methods for Linear Systems: Momentum, Inexactness and Gossip Nicolas Loizou 60 5 0 26 Sep 2019
Differentially Private Meta-Learning Jeffrey Li M. Khodak S. Caldas Ameet Talwalkar FedML 131 108 0 12 Sep 2019
The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Communication Sebastian U. Stich Sai Praneeth Karimireddy FedML 80 20 0 11 Sep 2019
Communication-Censored Distributed Stochastic Gradient Descent Weiyu Li Tianyi Chen Liping Li Zhaoxian Wu Qing Ling 55 17 0 09 Sep 2019
Proximal gradient flow and Douglas-Rachford splitting dynamics: global exponential stability via integral quadratic constraints Sepideh Hassan-Moghaddam Mihailo R. Jovanović 16 0 0 23 Aug 2019
Towards Better Generalization: BP-SVRG in Training Deep Neural Networks Hao Jin Dachao Lin Zhihua Zhang ODL 35 2 0 18 Aug 2019
Path Length Bounds for Gradient Descent and Flow Chirag Gupta Sivaraman Balakrishnan Aaditya Ramdas 132 15 0 02 Aug 2019
On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift Alekh Agarwal Sham Kakade Jason D. Lee G. Mahajan 83 321 0 01 Aug 2019
Sparse Optimization on Measures with Over-parameterized Gradient Descent Lénaïc Chizat 94 93 0 24 Jul 2019
signADAM: Learning Confidences for Deep Neural Networks Dong Wang Yicheng Liu Wenwo Tang Fanhua Shang Hongying Liu Qigong Sun Licheng Jiao ODL FedML 27 1 0 21 Jul 2019
A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization Quoc Tran-Dinh Nhan H. Pham T. Dzung Lam M. Nguyen 78 51 0 08 Jul 2019
The Role of Memory in Stochastic Optimization Antonio Orvieto Jonas Köhler Aurelien Lucchi 92 30 0 02 Jul 2019
Near-Optimal Methods for Minimizing Star-Convex Functions and Beyond Oliver Hinder Aaron Sidford N. Sohoni 67 72 0 27 Jun 2019
A Stochastic Composite Gradient Method with Incremental Variance Reduction Junyu Zhang Lin Xiao 59 68 0 24 Jun 2019
Tensor Canonical Correlation Analysis with Convergence and Statistical Guarantees You-Lin Chen Mladen Kolar R. Tsay 48 0 0 12 Jun 2019
Adversarial Attack Generation Empowered by Min-Max Optimization Jingkang Wang Tianyun Zhang Sijia Liu Pin-Yu Chen Jiacen Xu M. Fardad Yangqiu Song AAML 70 37 0 09 Jun 2019