A projected-based neural network method for second-order cone programming

Abstract

A projected-based neural network method for second-order cone programming is proposed. The second-order cone programming is transformed into an equivalent projection equation. The projection on the second-order cone is simple and costs less computation time. We prove that the proposed neural network is stable in the sense of Lyapunov and converges to an exact solution of the second-order cone programming problem. The simulation experiments show our method is an efficient method for second-order cone programming problems.