Liu, Xin; Wen, Zaiwen; Zhang, Yin Limited memory block Krylov subspace optimization for computing dominant singular value decompositions. (English) Zbl 1278.65045 SIAM J. Sci. Comput. 35, No. 3, A1641-A1668 (2013). Authors’ abstract: In 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. Reviewer: Juri M. Rappoport (Moskva) Cited in 1 ReviewCited in 18 Documents MSC: 65F20 Numerical solutions to overdetermined systems, pseudoinverses 65K05 Numerical mathematical programming methods 65F15 Numerical computation of eigenvalues and eigenvectors of matrices 90C26 Nonconvex programming, global optimization Keywords:subspace optimization; dominant singular value decomposition; Krylov subspace method; eigenvalue decomposition; numerical examples; principal component analysis; dimension reduction; data mining; large and dense matrices; iteration method; convergence; algorithm Software:ARPACK; JADAMILU; Matlab PDFBibTeX XMLCite \textit{X. Liu} et al., SIAM J. Sci. Comput. 35, No. 3, A1641--A1668 (2013; Zbl 1278.65045) Full Text: DOI