Distributionally Robust Geometric Joint Chance-Constrained Optimization: Neurodynamic Approaches

ArXi:2603.06597v1 Announce Type: cross 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.