A cutting plane approach for chance constrained linear programs
Operations Research
Mathematical Programming: Series A and B
Computational solution of capacity planning models under uncertainty
Parallel Computing - Special issue on parallel computing in economics, finance and decision-making
On Constraint Sampling in the Linear Programming Approach to Approximate Dynamic Programming
Mathematics of Operations Research
Uncertain convex programs: randomized solutions and confidence levels
Mathematical Programming: Series A and B
Numerical Mathematics (Texts in Applied Mathematics)
Numerical Mathematics (Texts in Applied Mathematics)
Convex Approximations of Chance Constrained Programs
SIAM Journal on Optimization
Computers & Mathematics with Applications
A dynamical model for solving degenerate quadratic minimax problems with constraints
Journal of Computational and Applied Mathematics
Neural networks for solving second-order cone constrained variational inequality problem
Computational Optimization and Applications
A capable neural network model for solving the maximum flow problem
Journal of Computational and Applied Mathematics
A novel neural network for nonlinear convex programming
IEEE Transactions on Neural Networks
Solving general convex nonlinear optimization problems by an efficient neurodynamic model
Engineering Applications of Artificial Intelligence
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This paper presents a neural network model to solve chance constrained optimization (CCO) problems. The main idea is to convert the chance constrained problem into an equivalent convex second order cone programming (CSOCP) problem. A neural network model is then constructed for solving the obtained CSOCP problem. By employing Lyapunov function approach, it is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the original problem. The simulation results also show that the proposed neural network is feasible and efficient.