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Developing iterative algorithms to solve Sylvester tensor equations

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  • Zhang, Xin-Fang
  • Wang, Qing-Wen

Abstract

This paper is concerned with solving high order Sylvester tensor equation arising in control theory. We propose the tensor forms of the bi-conjugate gradient and bi-conjugate residual methods for solving the tensor equation. To improve their performance, two preconditioned iterative algorithms based on the nearest Kronecker product are developed for finding its solution. We also prove that the proposed algorithms are convergent to an exact solution within finite iteration steps for any initial tensor in the absence of round-off errors. At last, some numerical examples are provided to illustrate the feasibility and validity of the algorithms proposed.

Suggested Citation

  • Zhang, Xin-Fang & Wang, Qing-Wen, 2021. "Developing iterative algorithms to solve Sylvester tensor equations," Applied Mathematics and Computation, Elsevier, vol. 409(C).
    • Handle: RePEc:eee:apmaco:v:409:y:2021:i:c:s0096300321004926
    DOI: 10.1016/j.amc.2021.126403
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    References listed on IDEAS

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    1. Huang, Baohua & Ma, Changfeng, 2020. "Global least squares methods based on tensor form to solve a class of generalized Sylvester tensor equations," Applied Mathematics and Computation, Elsevier, vol. 369(C).
    2. Zhen Chen & Linzhang Lu, 2013. "A Gradient Based Iterative Solutions for Sylvester Tensor Equations," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-7, March.
    3. Lv, Changqing & Ma, Changfeng, 2020. "A modified CG algorithm for solving generalized coupled Sylvester tensor equations," Applied Mathematics and Computation, Elsevier, vol. 365(C).
    4. Amy N. Langville & William J. Stewart, 2004. "Testing the Nearest Kronecker Product Preconditioner on Markov Chains and Stochastic Automata Networks," INFORMS Journal on Computing, INFORMS, vol. 16(3), pages 300-315, August.
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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. Qi, Zhaohui & Ning, Yingqiang & Xiao, Lin & Luo, Jiajie & Li, Xiaopeng, 2023. "Finite-time zeroing neural networks with novel activation function and variable parameter for solving time-varying Lyapunov tensor equation," Applied Mathematics and Computation, Elsevier, vol. 452(C).

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