Parallel and distributed sparse optimization

This paper proposes parallel and distributed algorithms for solving very large-scale sparse optimization problems on computer clusters and clouds. Modern datasets usually have a large number of features or training samples, and they are usually stored in a distributed manner. Motivated by the need o...

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Bibliographic Details
Published in:Conference record - Asilomar Conference on Signals, Systems, & Computers pp. 659 - 646
Main Authors: Peng, Zhimin, Yan, Ming, Yin, Wotao
Format: Conference Proceeding
Language:English
Published: IEEE 01.11.2013
Subjects:
Broadcasting
Clustering algorithms
Convergence
Distributed databases
GRock
l 1 minimization
LASSO
Logistics
Optimization
parallel and distributed computing
sparse optimization
Vectors
ISSN:1058-6393
Online Access:Get full text
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Summary:This paper proposes parallel and distributed algorithms for solving very large-scale sparse optimization problems on computer clusters and clouds. Modern datasets usually have a large number of features or training samples, and they are usually stored in a distributed manner. Motivated by the need of solving sparse optimization problems with large datasets, we propose two approaches including (i) distributed implementations of prox-linear algorithms and (ii) GRock, a parallel greedy block coordinate descent method. Different separability properties of the objective terms in the problem enable different data distributed schemes along with their corresponding algorithm implementations. We also establish the convergence of GRock and explain why it often performs exceptionally well for sparse optimization. Numerical results on a computer cluster and Amazon EC2 demonstrate the efficiency and elasticity of our algorithms.
ISSN:1058-6393
DOI:10.1109/ACSSC.2013.6810364