Structure-Preserving Low Multilinear Rank Approximation of Antisymmetric Tensors

DOI10.1137/16M106618XzbMath1373.65028arXiv1603.05010OpenAlexW3099740672MaRDI QIDQ5358301

Erna Begović Kovač, Daniel Kressner

Publication date: 20 September 2017

Published in: SIAM Journal on Matrix Analysis and Applications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1603.05010

zbMATH Keywords

singular value decompositionantisymmetric tensorsJacobi rotationalgorithm: Jacobi algorithmmultilinear rank approximation

Mathematics Subject Classification ID

Numerical solutions to overdetermined systems, pseudoinverses (65F20) Multilinear algebra, tensor calculus (15A69)

Related Items (3)

Globally Convergent Jacobi-Type Algorithms for Simultaneous Orthogonal Symmetric Tensor Diagonalization ⋮ Convergence of a Jacobi-type method for the approximate orthogonal tensor diagonalization ⋮ Hybrid CUR-type decomposition of tensors in the Tucker format

Cites Work

• Tensor Decompositions and Applications
• Tensor-Train Decomposition
• Best rank one approximation of real symmetric tensors can be chosen symmetric
• On the Best Rank-1 Approximation of Higher-Order Supersymmetric Tensors
• Jacobi Algorithm for the Best Low Multilinear Rank Approximation of Symmetric Tensors
• A literature survey of low-rank tensor approximation techniques
• Approximating a wavefunction as an unconstrained sum of Slater determinants
• A Multilinear Singular Value Decomposition
• On the Best Rank-1 and Rank-(R₁ ,R₂ ,. . .,R_N) Approximation of Higher-Order Tensors
• Algorithms for Numerical Analysis in High Dimensions

This page was built for publication: Structure-Preserving Low Multilinear Rank Approximation of Antisymmetric Tensors