A Neurodynamic Approach for Solving Robust Linear Programming under the Polyhedral Uncertainty Set

Abstract

This paper converts the robust linear programming under the polyhedral uncertainty set to standard linear programming. After eliminating the parameter uncertainty, we design a projection neural network to address the problem. The equilibrium point of the neural network model is theoretically proved to be stable in the Lyapunov sense and globally convergent to the optimal solution to robust linear programming. Two numerical examples are provided to illustrate the validity and performance of the proposed approach.