Stochastic R tensors to stochastic tensor complementarity problems

Maolin Che, Liqun Qi, Yimin Wei

Research output: Journal article publicationJournal articleAcademic researchpeer-review

16 Citations (Scopus)

Abstract

The main purpose of this paper is devoted to an introduction of the stochastic tensor complementarity problem. We consider the expected residual minimization formulation of the stochastic tensor complementarity problem. We show that the solution set of the expected residual minimization problem is nonempty and bounded, if the associated tensor is an R tensor. We also prove that the associated tensor being a stochastic R tensor is a necessary and sufficient condition for the existence of the solution of the expected residual minimization problem to be nonempty and bounded.

Original languageEnglish
Pages (from-to)261-279
Number of pages19
JournalOptimization Letters
Volume13
Issue number2
DOIs
Publication statusPublished - 8 Mar 2019

Keywords

  • R tensors
  • Stochastic R tensors
  • Stochastic tensor complementarity problems
  • Tensor complementarity problem
  • The expected residual minimization formulation

ASJC Scopus subject areas

  • Control and Optimization

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