Hybrid Random/Deterministic Parallel Algorithms for Convex and Nonconvex Big Data Optimization
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00231892" target="_blank" >RIV/68407700:21230/15:00231892 - isvavai.cz</a>
Result on the web
<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7113892" target="_blank" >http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7113892</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TSP.2015.2436357" target="_blank" >10.1109/TSP.2015.2436357</a>
Alternative languages
Result language
angličtina
Original language name
Hybrid Random/Deterministic Parallel Algorithms for Convex and Nonconvex Big Data Optimization
Original language description
We propose a decomposition framework for the parallel optimization of the sum of a differentiable (possibly nonconvex) function and a nonsmooth (possibly nonseparable), convex one. The latter term is usually employed to enforce structure in the solution,typically sparsity. The main contribution of this work is a novel parallel, hybrid random/deterministic decomposition scheme wherein, at each iteration, a subset of (block) variables is updated at the same time by minimizing a convex surrogate of the original nonconvex function. To tackle huge-scale problems, the (block) variables to be updated are chosen according to a mixed random and deterministic procedure, which captures the advantages of both pure deterministic and random update-based schemes. Almost sure convergence of the proposed scheme is established. Numerical results show that on huge-scale problems the proposed hybrid random/deterministic algorithm compares favorably to random and deterministic schemes on both convex and n
Czech name
—
Czech description
—
Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
JC - Computer hardware and software
OECD FORD branch
—
Result continuities
Project
<a href="/en/project/EE2.3.30.0034" target="_blank" >EE2.3.30.0034: Support of inter-sectoral mobility and quality enhancement of research teams at Czech Technical University in Prague</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Signal Processing
ISSN
1053-587X
e-ISSN
—
Volume of the periodical
63
Issue of the periodical within the volume
15
Country of publishing house
US - UNITED STATES
Number of pages
16
Pages from-to
3914-3929
UT code for WoS article
000357112300006
EID of the result in the Scopus database
2-s2.0-84934290103