Deterministic and randomized Kaczmarz methods for $$AXB=C$$ with applications to color image restoration
Abstract We study Kaczmarz type methods to solve consistent linear matrix equations. We first present a block Kaczmarz (BK) method that employs a deterministic cyclic row selection strategy. Assuming that the associated coefficient matrix has full column or row rank, we derive matrix formulas for a cycle of this BK method. Moreover, we propose a greedy randomized block Kaczmarz (GRBK) method and further extend it to a relaxed variant (RGRBK) and a deterministic counterpart (MWRBK). We establish the convergence properties of the proposed methods. Numerical tests verify the theoretical findings, and we apply the proposed methods to color image restoration problems.
Authors
- Gangrong Qu (ORCID: https://orcid.org/0000-0002-2004-2457)
- Duo Liu (ORCID: https://orcid.org/0000-0002-3040-2065)
- Michiel E. Hochstenbach
- Wenli Wang
Publication Details
- Journal
- BIT Numerical Mathematics
- Published
- 2026-02-04
- DOI
- https://doi.org/10.1007/s10543-025-01102-8
- Primary Topic
- Stochastic Gradient Optimization Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- China Scholarship Council
- Fundamental Research Funds for the Central Universities