Fetching the paper…
Reading the bibliography…
The incremental gradient method is a prominent algorithm for minimizing a finite sum of smooth convex functions, used in many contexts including large-scale data processing applications and distributed optimization over networks.
On a Stochastic Approximation Method
K. L. Chung · 1954
Earlier work this paper cites.
B. Widrow and M. E. Hoff · 1960
Earlier work this paper cites.
An adaptive associative memory principle
T. Kohonen · 1974
Earlier work this paper cites.
On the mathematical foundations of nondifferentiable optimization in engineering design
E. Polak · 1987
Earlier work this paper cites.
On the convergence of the lms algorithm with adaptive learning rate for linear feedforward networks
Z. Luo · 1991
Earlier work this paper cites.
Convergence properties of backpropagation for neural nets via theory of stochastic gradient methods. part 1
A. A. Gaivoronski · 1994
Earlier work this paper cites.
A class of unconstrained minimization methods for neural network training
L. Grippo · 1994
Earlier work this paper cites.
Serial and parallel backpropagation convergence via nonmonotone perturbed minimization
O.L. Mangasarian and M.V. Solodov · 1994
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.
Incremental gradient algorithms with stepsizes bounded away from zero
M.V. Solodov · 1998
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.
Nonlinear programming
D. Bertsekas · 1999
Cited alongside, same era.
Gradient convergence in gradient methods with errors
D. Bertsekas and J. Tsitsiklis · 2000
Cited alongside, same era.
Convergence rate of incremental subgradient algorithms
A. Nedić and D. Bertsekas · 2001
Cited alongside, same era.
The incremental gauss-newton algorithm with adaptive stepsize rule
H. Moriyama, N. Yamashita, and M. Fukushima · 2003
Cited alongside, same era.
Introductory lectures on convex optimization: A basic course
Y. Nesterov · 2004
Cited alongside, same era.
On-line learning for very large data sets
L. Bottou and Y. Le Cun · 2005
Cited alongside, same era.
Logarithmic regret algorithms for online convex optimization
Robust Stochastic Approximation Approach to Stochastic Programming
A. Nemirovski, A. Juditsky, G. Lan, and A. Shapiro · 2009
Later among the works it cites.
Incremental gradient, subgradient, and proximal methods for convex optimization: a survey
D. Bertsekas · 2011
Later among the works it cites.
Distributed optimization and statistical learning via the alternating direction method of multipliers
S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein · 2011
Later among the works it cites.
Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning
E. Moulines and F. R. Bach · 2011
Later among the works it cites.
Making gradient descent optimal for strongly convex stochastic optimization
A. Rakhlin, O. Shamir, and K. Sridharan · 2011
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
E. Hazan, A. Agarwal, and S. Kale · 2007
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.
Analysis of an approximate gradient projection method with applications to the backpropagation algorithm
Z. Luo and P. Tseng · 2008
Cited alongside, same era.
Distributed subgradient methods for multi-agent optimization
A. Nedic and A. Ozdaglar · 2009
Cited alongside, same era.
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.
Adaptivity of averaged stochastic gradient descent to local strong convexity for logistic regression
F. Bach · 2014
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.
Convex Optimization Algorithms
D. Bertsekas · 2015
Closest in time.
A globally convergent incremental Newton method
M. Gürbüzbalaban, A. Ozdaglar, and P. Parrilo · 2015
Closest in time.
Why random reshuffling beats stochastic gradient descent
M. Gürbüzbalaban, A. Ozdaglar, and P. Parrilo · 2015
Closest in time.