Pith. sign in

REVIEW 1 cited by

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

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2004.12530 v1 pith:DTW6G4ZX submitted 2020-04-27 math.NA cs.NAmath.OC

Yanzhao Cao , Somak Das , Luke Oeding , Hans-Werner van Wyk This is my paper · ORCID
classification math.NAcs.NAmath.OC
keywords salstensorsalgorithmalternatingdecompositionleastmethodrandom
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Efficient Tensor Decomposition via Moment Matrix Extension

    math.AG 2025-06 conditional novelty 6.0 of 10

    Generic order-4 symmetric tensors of rank up to 2n+1 are efficiently decomposable via moment matrix extension, with a conjectured extension to O(n^2) rank.

Pith tools