Randomized Average Kaczmarz Algorithm for Tensor Linear Systems

For solving tensor linear systems under the tensor–tensor t-product, we propose the randomized average Kaczmarz (TRAK) algorithm, the randomized average Kaczmarz algorithm with random sampling (TRAKS), and their Fourier version, which can be effectively implemented in a distributed environment. We a...

Full description

Saved in:
Bibliographic Details
Published in:Mathematics (Basel) Vol. 10; no. 23; p. 4594
Main Authors: Bao, Wendi, Zhang, Feiyu, Li, Weiguo, Wang, Qin, Gao, Ying
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.12.2022
Subjects:
Algorithms
Analysis
Fourier domain
Fourier transforms
Iterative methods
least-norm problem
Linear systems
Mathematical analysis
Methods
Random sampling
randomized average Kaczmarz method
T-product
tensor linear system
Tensors
Tensors (Mathematics)
Video data
China
ISSN:2227-7390, 2227-7390
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:For solving tensor linear systems under the tensor–tensor t-product, we propose the randomized average Kaczmarz (TRAK) algorithm, the randomized average Kaczmarz algorithm with random sampling (TRAKS), and their Fourier version, which can be effectively implemented in a distributed environment. We analyzed the relationships (of the updated formulas) between the original algorithms and their Fourier versions in detail and prove that these new algorithms can converge to the unique least F-norm solution of the consistent tensor linear systems. Extensive numerical experiments show that they significantly outperform the tensor-randomized Kaczmarz (TRK) algorithm in terms of both iteration counts and computing times and have potential in real-world data, such as video data, CT data, etc.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2227-7390
2227-7390
DOI:10.3390/math10234594