Fetching the paper…
Reading the bibliography…
We analyze the convergence rate of the random reshuffling (RR) method, which is a randomized first-order incremental algorithm for minimizing a finite sum of convex component functions.
A stochastic approximation method
H. Robbins and S. Monro · 1951
Earlier work this paper cites.
On a stochastic approximation method
K. L. Chung · 1954
Earlier work this paper cites.
Stochastic approximation of minima with improved asymptotic speed
V. Fabian · 1967
Earlier work this paper cites.
On asymptotic normality in stochastic approximation
V. Fabian · 1968
Earlier work this paper cites.
Problem complexity and method efficiency in optimization
A. Nemirovskii, D.B. Yudin, and E.R. Dawson · 1983
Earlier work this paper cites.
Acceleration of stochastic approximation by averaging
B. T. Polyak and A. B. Juditsky · 1992
Earlier work this paper cites.
Incremental least squares methods and the extended Kalman filter
D. Bertsekas · 1996
Earlier work this paper cites.
A hybrid incremental gradient method for least squares
D. Bertsekas · 1997
Earlier work this paper cites.
Nonlinear programming
D. Bertsekas · 1999
Earlier work this paper cites.
An introduction to algorithms for nonlinear optimization
N.I.M. Gould and S. Leyffer · 2003
Earlier work this paper cites.
Stochastic approximation and recursive algorithms and applications
H. J. Kushner and G. Yin · 2003
Earlier work this paper cites.
Solving large scale linear prediction problems using stochastic gradient descent algorithms
T. Zhang · 2004
Earlier work this paper cites.
On-line learning for very large data sets
L. Bottou and Y. Le Cun · 2005
Earlier work this paper cites.
Convergence of weighted averages of random variables revisited
N. Etemadi · 2006
Cited alongside, same era.
On the rate of convergence of distributed subgradient methods for multi-agent optimization
A. Nedić and A. Ozdaglar · 2007
Cited alongside, same era.
Stochastic incremental gradient descent for estimation in sensor networks
S.S. Ram, A. Nedic, and V.V. Veeravalli · 2007
Cited alongside, same era.
Curiously fast convergence of some stochastic gradient descent algorithms
L. Bottou · 2009
Cited alongside, same era.
Distributed subgradient methods for multi-agent optimization
A. Nedić and A. Ozdaglar · 2009
Cited alongside, same era.
Robust Stochastic Approximation Approach to Stochastic Programming
A. Nemirovski, A. Juditsky, G. Lan, and A. Shapiro · 2009
Cited alongside, same era.
Stochastic gradient descent tricks
L. Bottou · 2012
Later among the works it cites.
Towards a unified architecture for in-rdbms analytics
X. Feng, A. Kumar, B. Recht, and C. Ré · 2012
Later among the works it cites.
Making gradient descent optimal for strongly convex stochastic optimization
A. Rakhlin, O. Shamir, and K. Sridharan · 2012
Later among the works it cites.
Toward a noncommutative arithmetic-geometric mean inequality: Conjectures, case-studies, and consequences
B. Recht and C. Ré · 2012
Later among the works it cites.
A stochastic gradient method with an exponential convergence rate for finite training sets
N. L. Roux, M. Schmidt, and F. R. Bach · 2012
Later among the works it cites.
Open Problem: Is Averaging Needed for Strongly Convex Stochastic Gradient Descent?
O. Shamir · 2012
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Large-scale machine learning with stochastic gradient descent
L. Bottou · 2010
Cited alongside, same era.
Incremental gradient, subgradient, and proximal methods for convex optimization: a survey
D. Bertsekas · 2011
Cited alongside, same era.
Distributed optimization and statistical learning via the alternating direction method of multipliers
S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein · 2011
Cited alongside, same era.
Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning
E. Moulines and F. R. Bach · 2011
Cited alongside, same era.
Hogwild: A lock-free approach to parallelizing stochastic gradient descent
B. Recht, C. Ré, S. Wright, and Feng N · 2011
Cited alongside, same era.
Information-theoretic lower bounds on the oracle complexity of stochastic convex optimization
A. Agarwal, P.L. Bartlett, P. Ravikumar, and M.J. Wainwright · 2012
Cited alongside, same era.
Later among the works it cites.
Parallel stochastic gradient algorithms for large-scale matrix completion
B. Recht and C. Ré · 2013
Later among the works it cites.
MLI: An API for distributed machine learning
E.R. Sparks, A. Talwalkar, V. Smith, J. Kottalam, P. Xinghao, J. Gonzalez, M.J. Franklin, M.I Jordan, and T. Kraska · 2013
Later among the works it cites.
Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods
J. Sohl-Dickstein, B. Poole, and S. Ganguli · 2014
Later among the works it cites.
A note on the non-commutative arithmetic-geometric mean inequality
T. Zhang · 2014
Later among the works it cites.
Convex Optimization Algorithms
D. Bertsekas · 2015
Closest in time.
Convergence rate of incremental gradient and Newton methods
M. Gürbüzbalaban, A. Ozdaglar, and P. Parrilo · 2015
Closest in time.
An arithmetic-geometric mean inequality for products of three matrices
A. Israel, F. Krahmer, and R. Ward · 2016
Closest in time.