Minimizing finite sums with the stochastic average gradient
type: Item
instance of: scholarly article
title: Minimizing finite sums with the stochastic average gradient
zbMATH Open document ID: 1358.90073
author: Mark Schmidt
author: Nicolas Le Roux
author: Francis Bach
published in: Mathematical Programming. Series A. Series B
publication date: 2017-03-23T00:00:00Z
full work available at URL: https://arxiv.org/abs/1309.2388
Mathematics Subject Classification ID: 90C06
Mathematics Subject Classification ID: 90C15
Mathematics Subject Classification ID: 90C25
Mathematics Subject Classification ID: 90C30
Mathematics Subject Classification ID: 65K05
Mathematics Subject Classification ID: 68Q25
Mathematics Subject Classification ID: 62L20
zbMATH DE Number: 6696631
zbMATH Keywords: convex optimization
zbMATH Keywords: stochastic gradient methods
zbMATH Keywords: first-order methods
zbMATH Keywords: convergence rates
describes a project that uses: SGD-QN
describes a project that uses: RCV1
describes a project that uses: UCI-ml
describes a project that uses: Pegasos
describes a project that uses: minFunc
describes a project that uses: AdaGrad
MaRDI profile type: Publication
OpenAlex ID: W2963156201
arXiv ID: 1309.2388
cites work: Information-Theoretic Lower Bounds on the Oracle Complexity of Stochastic Convex Optimization
cites work: A Proximal Stochastic Gradient Method with Progressive Variance Reduction
cites work: A New Class of Incremental Gradient Methods for Least Squares Problems
cites work: A Convergent Incremental Gradient Method with a Constant Step Size
cites work: SGD-QN: careful quasi-Newton stochastic gradient descent
cites work: Acceleration of Stochastic Approximation by Averaging
cites work: Q3096171
cites work: Accelerated Stochastic Approximation
cites work: Q5396673
cites work: An Incremental Gradient(-Projection) Method with Momentum Term and Adaptive Stepsize Rule
cites work: Hybrid deterministic-stochastic methods for data fitting
cites work: Optimal Stochastic Approximation Algorithms for Strongly Convex Stochastic Composite Optimization, II: Shrinking Procedures and Optimal Algorithms
cites work: An optimal algorithm for stochastic strongly-convex optimization
cites work: Q3093253
cites work: Semi-stochastic coordinate descent
cites work: Q4421713
cites work: Dual averaging methods for regularized stochastic learning and online optimization
cites work: A randomized Kaczmarz algorithm with exponential convergence
cites work: A Stochastic Approximation Method
cites work: Incremental gradient algorithms with stepsizes bounded away from zero
cites work: Convergence rate of incremental subgradient algorithms
cites work: Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm
cites work: Q3967358
cites work: Robust Stochastic Approximation Approach to Stochastic Programming
cites work: Introductory lectures on convex optimization. A basic course.
cites work: Smooth minimization of non-smooth functions
cites work: Primal-dual subgradient methods for convex problems
cites work: Efficiency of coordinate descent methods on huge-scale optimization problems
cites work: Q5491447
cites work: Q4828566
cites work: Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization
cites work: Pegasos: primal estimated sub-gradient solver for SVM
cites work: A nonlinear GMRES optimization algorithm for canonical tensor decomposition
DOI: 10.1007/S10107-016-1030-6
description: scientific article