Randomized regularized extended Kaczmarz algorithms for tensor recovery

Autor:	Du, Kui, Sun, Xiao-Hui
Rok vydání:	2021
Předmět:	Mathematics - Numerical Analysis Mathematics - Optimization and Control 65F10 68W20 90C25 15A69
Druh dokumentu:	Working Paper
Popis:	Randomized regularized Kaczmarz algorithms have recently been proposed to solve tensor recovery models with {\it consistent} linear measurements. In this work, we propose a novel algorithm based on the randomized extended Kaczmarz algorithm (which converges linearly in expectation to the unique minimum norm least squares solution of a linear system) for tensor recovery models with {\it inconsistent} linear measurements. We prove the linear convergence in expectation of our algorithm. Numerical experiments on a tensor least squares problem and a sparse tensor recovery problem are given to illustrate the theoretical results. Comment: 17 pages, 2 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2112.08566 Zobrazit plný text záznamu View this record from Arxiv