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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Veröffentlicht in:Conference record - Asilomar Conference on Signals, Systems, & Computers S. 659 - 646
Hauptverfasser: Peng, Zhimin, Yan, Ming, Yin, Wotao
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.11.2013
Schlagworte:
Broadcasting
Clustering algorithms
Convergence
Distributed databases
GRock
l 1 minimization
LASSO
Logistics
Optimization
parallel and distributed computing
sparse optimization
Vectors
ISSN:1058-6393
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Beschreibung
Zusammenfassung: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