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A modified CG algorithm for solving generalized coupled Sylvester tensor equations

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  • Lv, Changqing
  • Ma, Changfeng

Abstract

In this paper, a modified conjugate gradient (MCG) algorithm is proposed for solving generalized coupled Sylverster tensor equations. If the tensor equations are consistent, we show the solution can be obtained within finite steps in the absence of roundoff errors for any initial value. At last, some numerical examples are provided to illustrate the efficiency and validity of the proposed algorithm.

Suggested Citation

  • Lv, Changqing & Ma, Changfeng, 2020. "A modified CG algorithm for solving generalized coupled Sylvester tensor equations," Applied Mathematics and Computation, Elsevier, vol. 365(C).
    • Handle: RePEc:eee:apmaco:v:365:y:2020:i:c:s0096300319306915
    DOI: 10.1016/j.amc.2019.124699
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    Cited by:

    1. Huang, Guang-Xin & Chen, Qi-Xing & Yin, Feng, 2022. "Preconditioned TBiCOR and TCORS algorithms for solving the Sylvester tensor equation," Applied Mathematics and Computation, Elsevier, vol. 422(C).
    2. Chen, Qi-Xing & Huang, Guang-Xin & Zhang, Ming-Yue, 2024. "Preconditioned BiCGSTAB and BiCRSTAB methods for solving the Sylvester tensor equation," Applied Mathematics and Computation, Elsevier, vol. 466(C).
    3. Tao Li & Qing-Wen Wang & Xin-Fang Zhang, 2022. "A Modified Conjugate Residual Method and Nearest Kronecker Product Preconditioner for the Generalized Coupled Sylvester Tensor Equations," Mathematics, MDPI, vol. 10(10), pages 1-19, May.
    4. Zhang, Xin-Fang & Wang, Qing-Wen, 2021. "Developing iterative algorithms to solve Sylvester tensor equations," Applied Mathematics and Computation, Elsevier, vol. 409(C).
    5. Xiao, Lin & Li, Xiaopeng & Jia, Lei & Liu, Sai, 2022. "Improved finite-time solutions to time-varying Sylvester tensor equation via zeroing neural networks," Applied Mathematics and Computation, Elsevier, vol. 416(C).

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