Work

A Class of Coordinate Descent Methods With Angle Probability for Solving Linear Systems

Year: 2025

Type: article

Abstract: In order to resolve large-scale linear systems, we construct the new probability criterion based on the angle between hyperplanes and propose the randomized coordinate descent method with the angle probability criterion. Furthermore, we propose the randomized extended coordinate descent method with the angle probability criterion, which allows to solve underdetermined linear systems. The convergence properties of the two methods are analyzed from the expectation perspective and upper bounds on their convergence rate are derived when the matrix is full rank. Lastly, we conduct numerical experiments to verify the efficacy of our new methods.

Source: Journal of Applied Analysis & Computation

DOI: 10.11948/20240569

Authors: Wenjun Yu, Hailong Shen, Xinhui Shao

Institution: Northeastern University

Language: English

Topic: Matrix Theory and Algorithms

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: Fundamental Research Funds for the Central Universities

Award: G7282000605