Perturbation Theory and Optimality Conditions for the Best Multilinear Rank Approximation of a Tensor-Reference-Cited by-同舟云学术

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Perturbation Theory and Optimality Conditions for the Best Multilinear Rank Approximation of a Tensor

Published:2011-10 Issue:4 Volume:32 Page:1422-1450
ISSN:0895-4798
Container-title:SIAM Journal on Matrix Analysis and Applications
language:en
Short-container-title:SIAM J. Matrix Anal. & Appl.

Author:

Eldén Lars,Savas Berkant

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

Analysis

Link

http://epubs.siam.org/doi/pdf/10.1137/110823298

Reference20 articles.

1. Algorithm 862

2. Decomposition of quantics in sums of powers of linear forms

3. First-order perturbation analysis of the best rank-(R1,R2,R3) approximation in multilinear algebra

4. A Multilinear Singular Value Decomposition

5. On the Best Rank-1 and Rank-(R1 ,R2 ,. . .,RN) Approximation of Higher-Order Tensors

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1. Optimality conditions for Tucker low-rank tensor optimization;Computational Optimization and Applications;2023-03-13

2. Perturbations of the Tcur Decomposition for Tensor Valued Data in the Tucker Format;Journal of Optimization Theory and Applications;2022-06-24

3. A Krylov-Schur-like method for computing the best rank-(r1,r2,r3) approximation of large and sparse tensors;Numerical Algorithms;2022-04-27

4. Spectral partitioning of large and sparse 3‐tensors using low‐rank tensor approximation;Numerical Linear Algebra with Applications;2022-02-28

5. On the convergence of higher-order orthogonal iteration;Linear and Multilinear Algebra;2017-10-24

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