Limited Memory Block Krylov Subspace Optimization for Computing Dominant Singular Value Decompositions

dc.citation.firstpageA1641en_US
dc.citation.issueNumber3en_US
dc.citation.journalTitleSIAM Journal on Scientific Computingen_US
dc.citation.lastpageA1668en_US
dc.citation.volumeNumber35en_US
dc.contributor.authorLiu, Xinen_US
dc.contributor.authorWen, Zaiwenen_US
dc.contributor.authorZhang, Yinen_US
dc.date.accessioned2016-02-02T19:17:44Zen_US
dc.date.available2016-02-02T19:17:44Zen_US
dc.date.issued2013en_US
dc.description.abstractIn many data-intensive applications, the use of principal component analysis and other related techniques is ubiquitous for dimension reduction, data mining, or other transformational purposes. Such transformations often require efficiently, reliably, and accurately computing dominant singular value decompositions (SVDs) of large and dense matrices. In this paper, we propose and study a subspace optimization technique for significantly accelerating the classic simultaneous iteration method. We analyze the convergence of the proposed algorithm and numerically compare it with several state-of-the-art SVD solvers under the MATLAB environment. Extensive computational results show that on a wide range of large unstructured dense matrices, the proposed algorithm can often provide improved efficiency or robustness over existing algorithms.en_US
dc.identifier.citationLiu, Xin, Wen, Zaiwen and Zhang, Yin. "Limited Memory Block Krylov Subspace Optimization for Computing Dominant Singular Value Decompositions." <i>SIAM Journal on Scientific Computing,</i> 35, no. 3 (2013) SIAM: A1641-A1668. http://dx.doi.org/10.1137/120871328.en_US
dc.identifier.doihttp://dx.doi.org/10.1137/120871328en_US
dc.identifier.urihttps://hdl.handle.net/1911/88302en_US
dc.language.isoengen_US
dc.publisherSIAMen_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.titleLimited Memory Block Krylov Subspace Optimization for Computing Dominant Singular Value Decompositionsen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
120871328.pdf
Size:
1.18 MB
Format:
Adobe Portable Document Format
Description: