The k-means clustering-based CD method for solving large-scale matrix equation $ AX=B $
Abstract
In this paper, the coordinate descent (CD) method integrated with k-means clustering is proposed to address the least-squares problem of the large-scale matrix equation $ AX=B $, where the coefficient matrix $ A $ is assumed to be of full column rank. Rigorous theoretical analysis shows that the iteration sequence generated by this method converges to the unique least-norm least-squares solution of the problem. The performed numerical results demonstrate that the method developed here is feasible and efficient.
Fields of Study & Subjects
Matrix Theory and Algorithms
Advanced Optimization Algorithms Research
Stochastic Gradient Optimization Techniques
Publication details
- Journal / source
- AIMS Mathematics
- Publisher
- American Institute of Mathematical Sciences (AIMS)
- Volume
- 11
- Issue
- 4
- Pages
- 8859–8878
- Publication date
- 1 January 2026