Some Convergence Results on the Regularized Alternating Least-Squares Method for Tensor Decomposition
Numerical Analysis
2015-03-19 v1
Abstract
We study the convergence of the Regularized Alternating Least-Squares algorithm for tensor decompositions. As a main result, we have shown that given the existence of critical points of the Alternating Least-Squares method, the limit points of the converging subsequences of the RALS are the critical points of the least squares cost functional. Some numerical examples indicate a faster convergence rate for the RALS in comparison to the usual alternating least squares method.
Keywords
Cite
@article{arxiv.1109.3831,
title = {Some Convergence Results on the Regularized Alternating Least-Squares Method for Tensor Decomposition},
author = {Na Li and Stefan Kindermann and Carmeliza Navasca},
journal= {arXiv preprint arXiv:1109.3831},
year = {2015}
}