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Momentum and Stochastic Momentum for Stochastic Gradient, Newton,
  Proximal Point and Subspace Descent Methods

Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods

27 December 2017
Nicolas Loizou
Peter Richtárik
ArXivPDFHTML

Papers citing "Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods"

44 / 44 papers shown
Title
HOME-3: High-Order Momentum Estimator with Third-Power Gradient for Convex and Smooth Nonconvex Optimization
HOME-3: High-Order Momentum Estimator with Third-Power Gradient for Convex and Smooth Nonconvex Optimization
Wei Zhang
Arif Hassan Zidan
Afrar Jahin
Wei Zhang
Tianming Liu
ODL
55
0
0
16 May 2025
Stochastic Polyak Step-sizes and Momentum: Convergence Guarantees and Practical Performance
Stochastic Polyak Step-sizes and Momentum: Convergence Guarantees and Practical Performance
Dimitris Oikonomou
Nicolas Loizou
66
6
0
06 Jun 2024
SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and
  Interpolation
SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation
Robert Mansel Gower
Othmane Sebbouh
Nicolas Loizou
83
75
0
18 Jun 2020
Reducing the variance in online optimization by transporting past
  gradients
Reducing the variance in online optimization by transporting past gradients
Sébastien M. R. Arnold
Pierre-Antoine Manzagol
Reza Babanezhad
Ioannis Mitliagkas
Nicolas Le Roux
61
28
0
08 Jun 2019
Revisiting Randomized Gossip Algorithms: General Framework, Convergence
  Rates and Novel Block and Accelerated Protocols
Revisiting Randomized Gossip Algorithms: General Framework, Convergence Rates and Novel Block and Accelerated Protocols
Nicolas Loizou
Peter Richtárik
45
35
0
20 May 2019
Convergence Analysis of Inexact Randomized Iterative Methods
Convergence Analysis of Inexact Randomized Iterative Methods
Nicolas Loizou
Peter Richtárik
50
21
0
19 Mar 2019
A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise
  Insertion
A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion
Filip Hanzely
Jakub Konecný
Nicolas Loizou
Peter Richtárik
Dmitry Grishchenko
39
10
0
27 Jan 2019
Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are
  Better Without the Outer Loop
Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop
D. Kovalev
Samuel Horváth
Peter Richtárik
86
156
0
24 Jan 2019
Accelerated Linear Convergence of Stochastic Momentum Methods in
  Wasserstein Distances
Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances
Bugra Can
Mert Gurbuzbalaban
Lingjiong Zhu
48
45
0
22 Jan 2019
Provably Accelerated Randomized Gossip Algorithms
Provably Accelerated Randomized Gossip Algorithms
Nicolas Loizou
Michael G. Rabbat
Peter Richtárik
42
19
0
31 Oct 2018
Quasi-hyperbolic momentum and Adam for deep learning
Quasi-hyperbolic momentum and Adam for deep learning
Jerry Ma
Denis Yarats
ODL
123
130
0
16 Oct 2018
Accelerated Gossip via Stochastic Heavy Ball Method
Accelerated Gossip via Stochastic Heavy Ball Method
Nicolas Loizou
Peter Richtárik
38
27
0
23 Sep 2018
Optimal Matrix Momentum Stochastic Approximation and Applications to
  Q-learning
Optimal Matrix Momentum Stochastic Approximation and Applications to Q-learning
Adithya M. Devraj
Ana Bušić
Sean P. Meyn
114
4
0
17 Sep 2018
Direct Acceleration of SAGA using Sampled Negative Momentum
Direct Acceleration of SAGA using Sampled Negative Momentum
Kaiwen Zhou
56
45
0
28 Jun 2018
A Simple Stochastic Variance Reduced Algorithm with Fast Convergence
  Rates
A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates
Kaiwen Zhou
Fanhua Shang
James Cheng
60
75
0
28 Jun 2018
Linearly convergent stochastic heavy ball method for minimizing
  generalization error
Linearly convergent stochastic heavy ball method for minimizing generalization error
Nicolas Loizou
Peter Richtárik
106
45
0
30 Oct 2017
Accelerated Stochastic Power Iteration
Accelerated Stochastic Power Iteration
Christopher De Sa
Bryan D. He
Ioannis Mitliagkas
Christopher Ré
Peng Xu
72
90
0
10 Jul 2017
Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling
  and Imaging Applications
Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications
A. Chambolle
Matthias Joachim Ehrhardt
Peter Richtárik
Carola-Bibiane Schönlieb
80
185
0
15 Jun 2017
YellowFin and the Art of Momentum Tuning
YellowFin and the Art of Momentum Tuning
Jian Zhang
Ioannis Mitliagkas
ODL
49
108
0
12 Jun 2017
Stochastic Reformulations of Linear Systems: Algorithms and Convergence
  Theory
Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory
Peter Richtárik
Martin Takáč
61
93
0
04 Jun 2017
The Marginal Value of Adaptive Gradient Methods in Machine Learning
The Marginal Value of Adaptive Gradient Methods in Machine Learning
Ashia Wilson
Rebecca Roelofs
Mitchell Stern
Nathan Srebro
Benjamin Recht
ODL
58
1,030
0
23 May 2017
A New Perspective on Randomized Gossip Algorithms
A New Perspective on Randomized Gossip Algorithms
Nicolas Loizou
Peter Richtárik
36
30
0
15 Oct 2016
Stochastic Heavy Ball
Stochastic Heavy Ball
S. Gadat
Fabien Panloup
Sofiane Saadane
93
103
0
14 Sep 2016
Unified Convergence Analysis of Stochastic Momentum Methods for Convex
  and Non-convex Optimization
Unified Convergence Analysis of Stochastic Momentum Methods for Convex and Non-convex Optimization
Tianbao Yang
Qihang Lin
Zhe Li
59
122
0
12 Apr 2016
Katyusha: The First Direct Acceleration of Stochastic Gradient Methods
Katyusha: The First Direct Acceleration of Stochastic Gradient Methods
Zeyuan Allen-Zhu
ODL
96
580
0
18 Mar 2016
A Simple Practical Accelerated Method for Finite Sums
A Simple Practical Accelerated Method for Finite Sums
Aaron Defazio
117
121
0
08 Feb 2016
Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling
Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling
Zeyuan Allen-Zhu
Zheng Qu
Peter Richtárik
Yang Yuan
74
171
0
30 Dec 2015
Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than
  Random Selection
Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection
J. Nutini
Mark Schmidt
I. Laradji
M. Friedlander
H. Koepke
66
223
0
01 Jun 2015
Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting
Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting
Jakub Konecný
Jie Liu
Peter Richtárik
Martin Takáč
ODL
97
273
0
16 Apr 2015
SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization
SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization
Zheng Qu
Peter Richtárik
Martin Takáč
Olivier Fercoq
ODL
76
99
0
08 Feb 2015
Coordinate Descent with Arbitrary Sampling II: Expected Separable
  Overapproximation
Coordinate Descent with Arbitrary Sampling II: Expected Separable Overapproximation
Zheng Qu
Peter Richtárik
156
83
0
27 Dec 2014
Coordinate Descent with Arbitrary Sampling I: Algorithms and Complexity
Coordinate Descent with Arbitrary Sampling I: Algorithms and Complexity
Zheng Qu
Peter Richtárik
69
130
0
27 Dec 2014
Going Deeper with Convolutions
Going Deeper with Convolutions
Christian Szegedy
Wei Liu
Yangqing Jia
P. Sermanet
Scott E. Reed
Dragomir Anguelov
D. Erhan
Vincent Vanhoucke
Andrew Rabinovich
457
43,649
0
17 Sep 2014
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly
  Convex Composite Objectives
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives
Aaron Defazio
Francis R. Bach
Simon Lacoste-Julien
ODL
131
1,822
0
01 Jul 2014
iPiano: Inertial Proximal Algorithm for Non-Convex Optimization
iPiano: Inertial Proximal Algorithm for Non-Convex Optimization
Peter Ochs
Yunjin Chen
Thomas Brox
Thomas Pock
73
433
0
18 Apr 2014
Accelerated, Parallel and Proximal Coordinate Descent
Accelerated, Parallel and Proximal Coordinate Descent
Olivier Fercoq
Peter Richtárik
88
372
0
20 Dec 2013
Semi-Stochastic Gradient Descent Methods
Semi-Stochastic Gradient Descent Methods
Jakub Konecný
Peter Richtárik
ODL
113
238
0
05 Dec 2013
Stochastic Gradient Descent, Weighted Sampling, and the Randomized
  Kaczmarz algorithm
Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm
Deanna Needell
Nathan Srebro
Rachel A. Ward
134
554
0
21 Oct 2013
Minimizing Finite Sums with the Stochastic Average Gradient
Minimizing Finite Sums with the Stochastic Average Gradient
Mark Schmidt
Nicolas Le Roux
Francis R. Bach
316
1,246
0
10 Sep 2013
Parallel Coordinate Descent Methods for Big Data Optimization
Parallel Coordinate Descent Methods for Big Data Optimization
Peter Richtárik
Martin Takáč
127
487
0
04 Dec 2012
Accelerated Gradient Methods for Networked Optimization
Accelerated Gradient Methods for Networked Optimization
E. Ghadimi
Iman Shames
M. Johansson
175
96
0
09 Nov 2012
Stochastic Dual Coordinate Ascent Methods for Regularized Loss
  Minimization
Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization
Shai Shalev-Shwartz
Tong Zhang
178
1,033
0
10 Sep 2012
Iteration Complexity of Randomized Block-Coordinate Descent Methods for
  Minimizing a Composite Function
Iteration Complexity of Randomized Block-Coordinate Descent Methods for Minimizing a Composite Function
Peter Richtárik
Martin Takáč
91
770
0
14 Jul 2011
Gossip Algorithms for Distributed Signal Processing
Gossip Algorithms for Distributed Signal Processing
A. Dimakis
S. Kar
José M. F. Moura
Michael G. Rabbat
Anna Scaglione
132
857
0
27 Mar 2010
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