Stochastic Tensor Complementarity Problem with Discrete Distribution

Shouqiang Du, Liyuan Cui, Yuanyuan Chen and Yimin Wei
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Shouqiang Du: Qingdao University
Liyuan Cui: Qingdao University
Yuanyuan Chen: Qingdao University
Yimin Wei: Fudan University

Journal of Optimization Theory and Applications, 2022, vol. 192, issue 3, No 6, 912-929

Abstract: 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.

Keywords: Stochastic tensor complementarity problem; Semismooth Newton method; Barzilai–Borwein stepsize; Convergence; 15A69; 90C33 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10957-021-01997-7

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