Condition numbers for the tensor rank decomposition

DOI10.1016/j.laa.2017.08.014zbMath1375.65063arXiv1604.00052OpenAlexW2316966455MaRDI QIDQ2408941

Nick Vannieuwenhoven

Publication date: 10 October 2017

Published in: Linear Algebra and its Applications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1604.00052

zbMATH Keywords

algorithmstabilitycondition numbernumerical experimentsingular valuetensor rank decompositionTerracini's matrix

Mathematics Subject Classification ID

Eigenvalues, singular values, and eigenvectors (15A18) Numerical computation of matrix norms, conditioning, scaling (65F35) Multilinear algebra, tensor calculus (15A69)

Related Items (10)

Perturbations of the \textsc{Tcur} decomposition for tensor valued data in the Tucker format ⋮ An approximation method of CP rank for third-order tensor completion ⋮ The condition number of many tensor decompositions is invariant under Tucker compression ⋮ Alternating Mahalanobis Distance Minimization for Accurate and Well-Conditioned CP Decomposition ⋮ The Condition Number of Join Decompositions ⋮ A Riemannian Trust Region Method for the Canonical Tensor Rank Approximation Problem ⋮ Exploiting Efficient Representations in Large-Scale Tensor Decompositions ⋮ Perturbation analysis for matrix joint block diagonalization ⋮ Pencil-Based Algorithms for Tensor Rank Decomposition are not Stable ⋮ Guarantees for Existence of a Best Canonical Polyadic Approximation of a Noisy Low-Rank Tensor

Uses Software

• mctoolbox

Cites Work

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