Distributed Block Coordinate Descent for Minimizing Partially Separable Functions

DOI10.1007/978-3-319-17689-5_11zbMath1330.65089arXiv1406.0238OpenAlexW1594004154MaRDI QIDQ3462314

Jakub Mareček, Peter Richtárik, Martin Takáč

Publication date: 5 January 2016

Published in: Numerical Analysis and Optimization (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1406.0238

zbMATH Keywords

convex optimizationnumerical examplescommunication complexitysupport vector machineiteration complexitybig data optimizationcomposite objectiveexpected separable over-approximationhuge-scale optimizationpartial separabilityempirical risk minimizationdistributed coordinate descent

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Convex programming (90C25) Complexity and performance of numerical algorithms (65Y20)

Related Items (11)

A framework for parallel second order incremental optimization algorithms for solving partially separable problems ⋮ Multilevel Objective-Function-Free Optimization with an Application to Neural Networks Training ⋮ On the convergence analysis of asynchronous SGD for solving consistent linear systems ⋮ On stochastic mirror-prox algorithms for stochastic Cartesian variational inequalities: randomized block coordinate and optimal averaging schemes ⋮ Complexity of Partially Separable Convexly Constrained Optimization with Non-Lipschitzian Singularities ⋮ On the complexity of parallel coordinate descent ⋮ Matrix completion under interval uncertainty ⋮ High-order evaluation complexity for convexly-constrained optimization with non-Lipschitzian group sparsity terms ⋮ Optimization in High Dimensions via Accelerated, Parallel, and Proximal Coordinate Descent ⋮ Coordinate descent algorithms ⋮ Distributed Stochastic Optimization with Large Delays

Uses Software

• MPI
• RCV1
• Pegasos

Cites Work

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