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Stochastic Tensor Complementarity Problem with Discrete Distribution

Author

Listed:
  • Shouqiang Du

    (Qingdao University)

  • Liyuan Cui

    (Qingdao University)

  • Yuanyuan Chen

    (Qingdao University)

  • Yimin Wei

    (Fudan University)

Abstract

Stochastic tensor complementarity problem with discrete distribution is investigated, which is a kind of stochastic tensor complementarity problem with discrete probability distribution variables. First, we formulate the stochastic tensor complementarity problem with discrete distribution as a constrained minimization problem. Some properties of this reformulation are studied based on the structured tensor. Then we propose a new semismooth Newton method for solving this problem. The proposed method combines the semismooth Newton method with the Barzilai–Borwein stepsize technique. In addition, the method uses the nonmonotone linesearch technique to ensure its global convergence. Any accumulation point of the sequence generated by the proposed method approximates to a solution of the stochastic tensor complementarity problem with discrete distribution. Finally, numerical results are given to verify our theoretical results.

Suggested Citation

  • Shouqiang Du & Liyuan Cui & Yuanyuan Chen & Yimin Wei, 2022. "Stochastic Tensor Complementarity Problem with Discrete Distribution," Journal of Optimization Theory and Applications, Springer, vol. 192(3), pages 912-929, March.
    • Handle: RePEc:spr:joptap:v:192:y:2022:i:3:d:10.1007_s10957-021-01997-7
    DOI: 10.1007/s10957-021-01997-7
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    References listed on IDEAS

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    Cited by:

    1. Xuezhong Wang & Maolin Che & Yimin Wei, 2022. "Randomized Kaczmarz methods for tensor complementarity problems," Computational Optimization and Applications, Springer, vol. 82(3), pages 595-615, July.

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