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Catalyst Acceleration for First-order Convex Optimization: from Theory to Practice

15 December 2017

Papers citing "Catalyst Acceleration for First-order Convex Optimization: from Theory to Practice"

38 / 38 papers shown

Title
Solving Convex-Concave Problems with $\tilde{\mathcal{O}}(ε^{-4/7})$ Second-Order Oracle Complexity Lesi Chen Chengchang Liu Luo Luo Jingzhao Zhang 18 0 0 10 Jun 2025
Functionally Constrained Algorithm Solves Convex Simple Bilevel Problems Huaqing Zhang Lesi Chen Jing Xu J.N. Zhang 127 2 0 28 Jan 2025
SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Jingruo Sun Zachary Frangella Madeleine Udell 75 0 0 28 Jan 2025
A Coefficient Makes SVRG Effective Yida Yin Zhiqiu Xu Zhiyuan Li Trevor Darrell Zhuang Liu 87 1 0 09 Nov 2023
Statistical and Computational Guarantees for Influence Diagnostics Jillian R. Fisher Lang Liu Krishna Pillutla Y. Choi Zaïd Harchaoui TDI 61 0 0 08 Dec 2022
Accelerated Riemannian Optimization: Handling Constraints with a Prox to Bound Geometric Penalties David Martínez-Rubio Sebastian Pokutta 78 10 0 26 Nov 2022
RECAPP: Crafting a More Efficient Catalyst for Convex Optimization Y. Carmon A. Jambulapati Yujia Jin Aaron Sidford 125 12 0 17 Jun 2022
On the fast convergence of minibatch heavy ball momentum Raghu Bollapragada Tyler Chen Rachel A. Ward 102 19 0 15 Jun 2022
Optimal Gradient Sliding and its Application to Distributed Optimization Under Similarity D. Kovalev Aleksandr Beznosikov Ekaterina Borodich Alexander Gasnikov G. Scutari 70 13 0 30 May 2022
On Acceleration of Gradient-Based Empirical Risk Minimization using Local Polynomial Regression Ekaterina Trimbach Edward Duc Hien Nguyen César A. Uribe 57 1 0 16 Apr 2022
Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods Aleksandr Beznosikov Eduard A. Gorbunov Hugo Berard Nicolas Loizou 100 50 0 15 Feb 2022
Anticorrelated Noise Injection for Improved Generalization Antonio Orvieto Hans Kersting F. Proske Francis R. Bach Aurelien Lucchi 116 48 0 06 Feb 2022
Amortized Implicit Differentiation for Stochastic Bilevel Optimization Michael Arbel Julien Mairal 178 60 0 29 Nov 2021
Acceleration in Distributed Optimization under Similarity Helena Lofstrom G. Scutari Tianyue Cao Alexander Gasnikov 77 28 0 24 Oct 2021
Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition Alessandro De Palma Rudy Bunel Alban Desmaison Krishnamurthy Dvijotham Pushmeet Kohli Philip Torr M. P. Kumar 76 52 0 14 Apr 2021
The Complexity of Nonconvex-Strongly-Concave Minimax Optimization Siqi Zhang Junchi Yang Cristóbal Guzmán Negar Kiyavash Niao He 90 62 0 29 Mar 2021
Accelerated, Optimal, and Parallel: Some Results on Model-Based Stochastic Optimization Karan N. Chadha Gary Cheng John C. Duchi 93 16 0 07 Jan 2021
Differentiable Programming à la Moreau Vincent Roulet Zaïd Harchaoui 81 5 0 31 Dec 2020
Accelerated Algorithms for Convex and Non-Convex Optimization on Manifolds Lizhen Lin B. Saparbayeva M. Zhang David B. Dunson 47 7 0 18 Oct 2020
Variance-Reduced Methods for Machine Learning Robert Mansel Gower Mark Schmidt Francis R. Bach Peter Richtárik 109 117 0 02 Oct 2020
Dual-Free Stochastic Decentralized Optimization with Variance Reduction Hadrien Hendrikx Francis R. Bach Laurent Massoulié 52 26 0 25 Jun 2020
Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization D. Kovalev Adil Salim Peter Richtárik 73 83 0 21 Jun 2020
Personalized Federated Learning with Moreau Envelopes Canh T. Dinh N. H. Tran Tuan Dung Nguyen FedML 94 1,013 0 16 Jun 2020
Lagrangian Decomposition for Neural Network Verification Rudy Bunel Alessandro De Palma Alban Desmaison Krishnamurthy Dvijotham Pushmeet Kohli Philip Torr M. P. Kumar 81 50 0 24 Feb 2020
Halpern Iteration for Near-Optimal and Parameter-Free Monotone Inclusion and Strong Solutions to Variational Inequalities Jelena Diakonikolas 84 77 0 20 Feb 2020
Cyanure: An Open-Source Toolbox for Empirical Risk Minimization for Python, C++, and soon more Julien Mairal 87 22 0 17 Dec 2019
A Distributed Quasi-Newton Algorithm for Primal and Dual Regularized Empirical Risk Minimization Ching-pei Lee Cong Han Lim Stephen J. Wright 20 3 0 12 Dec 2019
A Generic Acceleration Framework for Stochastic Composite Optimization A. Kulunchakov Julien Mairal 105 43 0 03 Jun 2019
Solving Empirical Risk Minimization in the Current Matrix Multiplication Time Y. Lee Zhao Song Qiuyi Zhang 104 117 0 11 May 2019
Deep Neural Networks for Rotation-Invariance Approximation and Learning C. Chui Shao-Bo Lin Ding-Xuan Zhou 162 34 0 03 Apr 2019
The importance of better models in stochastic optimization Hilal Asi John C. Duchi 62 73 0 20 Mar 2019
Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise A. Kulunchakov Julien Mairal 88 45 0 25 Jan 2019
Kernel Conjugate Gradient Methods with Random Projections Bailey Kacsmar Douglas R Stinson 49 4 0 05 Nov 2018
Understanding the Acceleration Phenomenon via High-Resolution Differential Equations Bin Shi S. Du Michael I. Jordan Weijie J. Su 68 264 0 21 Oct 2018
AdaGrad stepsizes: Sharp convergence over nonconvex landscapes Rachel A. Ward Xiaoxia Wu Léon Bottou ODL 113 369 0 05 Jun 2018
Katyusha X: Practical Momentum Method for Stochastic Sum-of-Nonconvex Optimization Zeyuan Allen-Zhu ODL 105 52 0 12 Feb 2018
Catalyst Acceleration for Gradient-Based Non-Convex Optimization Courtney Paquette Hongzhou Lin Dmitriy Drusvyatskiy Julien Mairal Zaïd Harchaoui ODL 76 40 0 31 Mar 2017
An Inexact Variable Metric Proximal Point Algorithm for Generic Quasi-Newton Acceleration Hongzhou Lin Julien Mairal Zaïd Harchaoui 90 13 0 04 Oct 2016