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Pré-Publication, Document De Travail Année : 2023

A Neurodynamic Duplex for Distributionally Robust Joint Chance-Constrained Optimization

Siham Tassouli
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Abdel Lisser

Résumé

This paper introduces a new neurodynamic duplex approach to address distributionally robust joint chanceconstrained optimization problems. We assume that the constraints' row vectors are independent, and their probability distributions belong to a specific distributional uncertainty set that is not known beforehand. Within our study, we examine two uncertainty sets for these unknown distributions. Our framework's key feature is the use of a neural network-based method to solve distributionally robust joint chance-constrained optimization problems, achieving an almost sure convergence to the optimum without relying on standard state-of-the-art solving methods. In the numerical section, we apply our proposed approach to solve a profit maximization problem, demonstrating its performance and comparing it against existing state-of-the-art methods.
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Dates et versions

hal-04225695 , version 1 (03-10-2023)

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  • HAL Id : hal-04225695 , version 1

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Siham Tassouli, Abdel Lisser. A Neurodynamic Duplex for Distributionally Robust Joint Chance-Constrained Optimization. 2023. ⟨hal-04225695⟩
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