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

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dc.contributor.authorLiu, Xinen_US
dc.contributor.authorWen, Zaiwenen_US
dc.contributor.authorZhang, Yinen_US
dc.date.accessioned2018-06-19T17:47:59Zen_US
dc.date.available2018-06-19T17:47:59Zen_US
dc.date.issued2012-03en_US
dc.date.noteMarch 2012en_US
dc.description.abstractIn many data-intensive applications, the use of principal component analysis (PCA) 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 unstructured matrices. In this paper, we propose and study a subspace optimization technique to significantly accelerate 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 matrices, the proposed algorithm can often provide improved efficiency or robustness over existing algorithms.en_US
dc.format.extent31 ppen_US
dc.identifier.citationLiu, Xin, Wen, Zaiwen and Zhang, Yin. "Limited Memory Block Krylov Subspace Optimization for Computing Dominant Singular Value Decompositions." (2012) <a href="https://hdl.handle.net/1911/102197">https://hdl.handle.net/1911/102197</a>.en_US
dc.identifier.digitalTR12-07en_US
dc.identifier.urihttps://hdl.handle.net/1911/102197en_US
dc.language.isoengen_US
dc.titleLimited Memory Block Krylov Subspace Optimization for Computing Dominant Singular Value Decompositionsen_US
dc.typeTechnical reporten_US
dc.type.dcmiTexten_US
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