Incremental tensor regularized least squares with multiple right-hand sides
Abstract
Solving linear discrete ill-posed problems for third order tensor equations based on a tensor t-product has attracted much attention. But when the data tensor is produced continuously, current algorithms are not time-saving. Here, we propose an incremental tensor regularized least squares (t-IRLS) algorithm with the t-product that incrementally computes the solution to the tensor regularized least squares (t-RLS) problem with multiple lateral slices on the right-hand side. More specifically, we update its solution by solving a t-RLS problem with a single lateral slice on the right-hand side whenever a new horizontal sample arrives, instead of solving the t-RLS problem from scratch. The t-IRLS algorithm is well suited for large data sets and real time operation. Numerical examples are presented to demonstrate the efficiency of our algorithm.
Cite
@article{arxiv.2111.14449,
title = {Incremental tensor regularized least squares with multiple right-hand sides},
author = {Zhengbang Cao and Pengpeng Xie},
journal= {arXiv preprint arXiv:2111.14449},
year = {2021}
}