Fast low rank approximations of matrices and tensors

DOI10.13001/1081-3810.1489zbMath1254.65054OpenAlexW1516744990MaRDI QIDQ3165168

M. Nkengla, Volker Mehrmann, Agnieszka Miedlar, Shmuel Friedland

Publication date: 25 October 2012

Published in: The Electronic Journal of Linear Algebra (Search for Journal in Brave)

Full work available at URL: http://www.emis.de/journals/ELA/ela-articles/articles/vol22_pp1031-1048.pdf

zbMATH Keywords

algorithmcomputational complexitynumerical resultssingular value decompositionleast squaresTucker decompositionCUR decompositionrank \(k\) approximation

Mathematics Subject Classification ID

Related Items (12)

Finding metastabilities in reversible Markov chains based on incomplete sampling ⋮ Submodular spectral functions of principal submatrices of a Hermitian matrix, extensions and applications ⋮ Monotonically convergent algorithms for symmetric tensor approximation ⋮ Literature survey on low rank approximation of matrices ⋮ On the generic and typical ranks of 3-tensors ⋮ A note on tensor chain approximation ⋮ Fast randomized matrix and tensor interpolative decomposition using countsketch ⋮ On the rank-one approximation of positive matrices using tropical optimization methods ⋮ HOID: Higher Order Interpolatory Decomposition for Tensors Based on Tucker Representation ⋮ Pseudospectra localizations for generalized tensor eigenvalues to seek more positive definite tensors ⋮ Efficient Construction of Tensor Ring Representations from Sampling ⋮ A literature survey of low-rank tensor approximation techniques

Uses Software

• ARPACK

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