arXiv Analytics

Sign in

arXiv:2004.12530 [math.NA]AbstractReferencesReviewsResources

Analysis of the Stochastic Alternating Least Squares Method for the Decomposition of Random Tensors

Yanzhao Cao, Somak Das, Luke Oeding, Hans-Werner van Wyk

Published 2020-04-27Version 1

Stochastic Alternating Least Squares (SALS) is a method that approximates the canonical decomposition of averages of sampled random tensors. Its simplicity and efficient memory usage make SALS an ideal tool for decomposing tensors in an online setting. We show, under mild regularization and readily verifiable assumptions on the boundedness of the data, that the SALS algorithm is globally convergent. Numerical experiments validate our theoretical findings and demonstrate the algorithm's performance and complexity.

Comments: 22 pages
Categories: math.NA, cs.NA, math.OC
Subjects: 65K10, 90C15, 15A69, 68W20
Related articles: Most relevant | Search more
arXiv:2111.03245 [math.NA] (Published 2021-11-05)
The decompositions and positive semidefiniteness of fourth-order conjugate partial-symmetric tensors with applications
arXiv:2407.15557 [math.NA] (Published 2024-07-22)
Hierarchical Alternating Least Squares Methods for Quaternion Nonnegative Matrix Factorizations
arXiv:2302.00922 [math.NA] (Published 2023-02-02)
An algebraic algorithm for rank-2 ParaTuck-2 decomposition

Links

Toolbox