Some Convergence Results on the Regularized Alternating Least-Squares Method for Tensor Decomposition

Na Li; Stefan Kindermann; Carmeliza Navasca

Paper

Some Convergence Results on the Regularized Alternating Least-Squares Method for Tensor Decomposition

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.