A neural network approach to solve linear programs with joint probabilistic constraints
Résumé
This paper studies a dynamical neural network approach for solving linear programs with joint Probabilistic Constraints (LPPC) with normally distributed random variables and independent matrix row vectors. We show that the proposed neural network is stable in the sense of Lyapunov and globally convergent. Finally, numerical results are given using randomly generated data.
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