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In this paper we study several classes of stochastic optimization algorithms enriched with heavy ball momentum.
Angenäherte auflösung von systemen linearer gleichungen
S. Kaczmarz · 1937
Earlier work this paper cites.
A stochastic approximation method
H. Robbins and S. Monro · 1951
Earlier work this paper cites.
Some methods of speeding up the convergence of iteration methods
B.T. Polyak · 1964
Earlier work this paper cites.
Linear recursive sequences
J.P. Fillmore and M.L. Marx · 1968
Earlier work this paper cites.
A limit theorem for the norm of random matrices
S. Geman · 1980
Earlier work this paper cites.
Problem complexity and method efficiency in optimization
A. Nemirovskii and D.B. Yudin · 1983
Earlier work this paper cites.
A method of solving a convex programming problem with convergence rate o ( 1 / k 2 ) o(1/k^{2})
Y. Nesterov · 1983
Earlier work this paper cites.
Introduction to optimization. translations series in mathematics and engineering
B.T. Polyak · 1987
Earlier work this paper cites.
Heavy-ball method in nonconvex optimization problems
S.K. Zavriev and F.V. Kostyuk · 1993
Earlier work this paper cites.
Least-squares solution of overdetermined inconsistent linear systems using Kaczmarz’s relaxation
C. Popa · 1995
Earlier work this paper cites.
An incremental gradient (-projection) method with momentum term and adaptive stepsize rule
P. Tseng · 1998
Earlier work this paper cites.
Random Geometric Graphs
M. Penrose · 2003
Earlier work this paper cites.
An Introduction to Difference Equations
S. Elaydi · 2005
Earlier work this paper cites.
Randomized gossip algorithms
S. Boyd, A. Ghosh, B. Prabhakar, and D. Shah · 2006
Earlier work this paper cites.
A convergent incremental gradient method with a constant step size
D. Blatt, A.O. Hero, and H. Gauchman · 2007
Earlier work this paper cites.
Old and new results on algebraic connectivity of graphs
Nair Maria Maia De Abreu · 2007
Earlier work this paper cites.
Applied iterative methods
C.L. Byrne · 2008
Earlier work this paper cites.
A randomized Kaczmarz algorithm with exponential convergence
T. Strohmer and R. Vershynin · 2009
Earlier work this paper cites.
Gossip algorithms for distributed signal processing
A.G. Dimakis, S. Kar, J.M.F. Moura, M.G. Rabbat, and A. Scaglione · 2010
Earlier work this paper cites.
Randomized methods for linear constraints: convergence rates and conditioning
D. Leventhal and A.S. Lewis · 2010
Earlier work this paper cites.
Randomized Kaczmarz solver for noisy linear systems
D. Needell · 2010
Earlier work this paper cites.
Incremental gradient, subgradient, and proximal methods for convex optimization: A survey
D.P. Bertsekas · 2011
Earlier work this paper cites.
Libsvm: a library for support vector machines
Chih-Chung Chang and Chih-Jen Lin · 2011
Earlier work this paper cites.
Acceleration of randomized Kaczmarz method via the Johnson–Lindenstrauss lemma
Y.C. Eldar and D. Needell · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G.E. Hinton · 2012
Earlier work this paper cites.
Efficiency of coordinate descent methods on huge-scale optimization problems
Y. Nesterov · 2012
Earlier work this paper cites.
Multi-step gradient methods for networked optimization
E. Ghadimi, I. Shames, and M. Johansson · 2013
Earlier work this paper cites.
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R. Johnson and T. Zhang · 2013
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Y.T. Lee and A. Sidford · 2013
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Y. Nesterov · 2013
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Sh. Shalev-Shwartz and T. Zhang · 2013
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I. Sutskever, J. Martens, G.E. Dahl, and G.E. Hinton · 2013
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J. Konečný, J. Liu, P. Richtárik, and M. Takáč · 2016
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L. Lessard, B. Recht, and A. Packard · 2016
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J. Liu and S. Wright · 2016
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N. Loizou and P. Richtárik · 2016
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D. Needell, N. Srebro, and R. Ward · 2016
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J. Nutini, B. Sepehry, I. Laradji, M. Schmidt, H. Koepke, and A. Virani · 2016
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