Recent Theoretical Advances in Non-Convex Optimization

Recent Theoretical Advances in Non-Convex Optimization

11 December 2020

Marina Danilova

Pavel Dvurechensky

Alexander Gasnikov

Eduard A. Gorbunov

Dmitry Kamzolov

Innokentiy Shibaev

Papers citing "Recent Theoretical Advances in Non-Convex Optimization"

11 / 11 papers shown

Title
Damped Proximal Augmented Lagrangian Method for weakly-Convex Problems with Convex Constraints Hari Dahal Wei Liu Yangyang Xu 34 5 0 15 Nov 2023
Beyond Log-Concavity: Theory and Algorithm for Sum-Log-Concave Optimization Mastane Achab 23 1 0 26 Sep 2023
Learning-Rate-Free Learning: Dissecting D-Adaptation and Probabilistic Line Search Max McGuinness ODL 15 0 0 06 Aug 2023
Convergence of gradient descent for deep neural networks S. Chatterjee ODL 21 20 0 30 Mar 2022
Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam Yucheng Lu Conglong Li Minjia Zhang Christopher De Sa Yuxiong He OffRL AI4CE 24 20 0 12 Feb 2022
The Complexity of Nonconvex-Strongly-Concave Minimax Optimization Siqi Zhang Junchi Yang Cristóbal Guzmán Negar Kiyavash Niao He 33 61 0 29 Mar 2021
MARINA: Faster Non-Convex Distributed Learning with Compression Eduard A. Gorbunov Konstantin Burlachenko Zhize Li Peter Richtárik 34 108 0 15 Feb 2021
First-Order Methods for Convex Optimization Pavel Dvurechensky Mathias Staudigl Shimrit Shtern ODL 18 25 0 04 Jan 2021
Katyusha X: Practical Momentum Method for Stochastic Sum-of-Nonconvex Optimization Zeyuan Allen-Zhu ODL 42 52 0 12 Feb 2018
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition Hamed Karimi J. Nutini Mark W. Schmidt 139 1,199 0 16 Aug 2016
Incremental Majorization-Minimization Optimization with Application to Large-Scale Machine Learning Julien Mairal 76 317 0 18 Feb 2014