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.

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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

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article

Deterministic and randomized Kaczmarz methods for $$AXB=C$$ with applications to color image restoration

Gangrong Qu, Duo Liu, Michiel E. Hochstenbach, Wenli Wang
BIT Numerical Mathematics
Stochastic Gradient Optimization Techniques
article

Deterministic and randomized Kaczmarz methods for $$AXB=C$$ with applications to color image restoration

Gangrong Qu, Duo Liu, Michiel E. Hochstenbach, Wenli Wang
article en

Abstract

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.

BIT Numerical MathematicsVol. 66(1)
China Scholarship Council, Fundamental Research Funds for the Central Universities
Life in Land
Openalex Percentile: Top 68%
Stochastic Gradient Optimization Techniques
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