SOTT: Greedy Approximation of a Tensor as a Sum of Tensor Trains

DOI10.1137/20M1381472zbMath1492.65108OpenAlexW3170050064WikidataQ114074131 ScholiaQ114074131MaRDI QIDQ5065504

Damiano Lombardi, Maria Fuente Ruiz, Virginie Ehrlacher

Publication date: 22 March 2022

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1137/20m1381472

zbMATH Keywords

tensor methodstensor traincanonical polyadic

Mathematics Subject Classification ID

Multilinear algebra, tensor calculus (15A69) Numerical methods for low-rank matrix approximation; matrix compression (65F55)

Related Items (max. 100)

The condition number of many tensor decompositions is invariant under Tucker compression

Uses Software

• TKPSVD

Cites Work

• Tensor Decompositions and Applications
• Tensor-Train Decomposition
• Tensor numerical methods in scientific computing
• The number of singular vector tuples and uniqueness of best rank-one approximation of tensors
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• Tensor Spaces and Numerical Tensor Calculus
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• Alternating Least Squares as Moving Subspace Correction
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• Tensor Rank and the Ill-Posedness of the Best Low-Rank Approximation Problem
• Enhanced Line Search: A Novel Method to Accelerate PARAFAC
• Algorithms for Numerical Analysis in High Dimensions
• A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data

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