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Residual-based Kaczmarz methods for tensor linear equations with t-product

arXiv CS Friday 05 June 2026, 04:00 UTC By Li-Lin Ji, Juanjuan Sun, Jun-Feng Yin 1 min read
Key Points

arXiv:2606.06057v1 Announce Type: new Abstract: Tensor linear systems widely arise from high-dimensional data mining and computing, for instance, natural language processing and machine learning. A class of residual-based tensor Kaczmarz method is proposed for tensor linear equations with t-product. Theoretical analyses prove the convergence and give an upper bound of the convergence rate of the proposed method.

arXiv:2606.06057v1 Announce Type: new Abstract: Tensor linear systems widely arise from high-dimensional data mining and computing, for instance, natural language processing and machine learning. A class of residual-based tensor Kaczmarz method is proposed for tensor linear equations with t-product. Theoretical analyses prove the convergence and give an upper bound of the convergence rate of the proposed method. Furthermore, an accelerated residual-based Kaczmarz method with heavy ball momentum is developed. Numerical experiments verify the efficiency of the proposed methods and demonstrate that they are faster than the existing tensor Kaczmarz methods.
Kaczmarz (PERSON) linear (ORG)
Originally published by arXiv CS Read original →

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