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dc.contributor.authorZhou, Guanglu
dc.contributor.authorCaccetta, Louis
dc.contributor.authorTeo, Kok Lay
dc.contributor.authorWu, S.
dc.date.accessioned2017-01-30T15:28:18Z
dc.date.available2017-01-30T15:28:18Z
dc.date.created2013-03-06T20:00:39Z
dc.date.issued2012
dc.identifier.citationZhou, Guanglu and Caccetta, Louis and Teo, Kok Lay and Wu, Soon-yi. 2012. Nonnegative polynomial optimization over unit spheres and convex programming relaxations. SIAM Journal on Optimization. 22 (3): pp. 987-1008.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/46637
dc.identifier.doi10.1137/110827910
dc.description.abstract

We consider approximation algorithms for nonnegative polynomial optimization over unit spheres. Such optimization models have wide applications, e.g., in signal and image processing, high order statistics, and computer vision. Since polynomial functions are nonconvex, the problems under consideration are all NP-hard. In this paper, based on convex polynomial optimization relaxations, we propose polynomial-time approximation algorithms with new approximation bounds. Numerical results are reported to show the effectiveness of the proposed approximation algorithms.

dc.publisherSociety for Industrial and Applied Mathematics
dc.subjectnonnegative polynomial optimization
dc.subjectapproximation - algorithm
dc.subjectconvex optimization relaxation
dc.titleNonnegative polynomial optimization over unit spheres and convex programming relaxations
dc.typeJournal Article
dcterms.source.volume22
dcterms.source.startPage987
dcterms.source.endPage1008
dcterms.source.issn0363-0129
dcterms.source.titleSIAM Journal on Optimization
curtin.note

Copyright © 2012 Society for Industrial and Applied Mathematics

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curtin.accessStatusOpen access


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