Distributionally Robust Geometric Joint Chance-Constrained Optimization: Neurodynamic Approaches
Résumé
This paper proposes a two-time scale neurodynamic duplex approach to solve distributionally robust geometric joint chance-constrained optimization problems. The probability distributions of the row vectors are not known in advance and belong to a certain distributional uncertainty set. In our paper, we study three uncertainty sets for the unknown distributions. The neurodynamic duplex is designed based on three projection equations. The main feature of our framework is to propose a neural network-based method to solve distributionally robust joint chance-constrained optimization problems that converges in probability to the global optimum without the use of standard stateof-the-art solving methods. In the numerical Section, we apply the proposed approach to solve a problem of shape optimization and a telecommunication problem.
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