A neurodynamic approach to nonsmooth constrained pseudoconvex optimization problem

This paper presents a new neurodynamic approach for solving the constrained pseudoconvex optimization problem based on more general assumptions. The proposed neural network is equipped with a hard comparator function and a piecewise linear function, which make the state solution not only stay in the feasible region, but also converge to an optimal solution of the constrained pseudoconvex optimization problem. Compared with other related existing conclusions, the neurodynamic approach here enjoys global convergence and lower dimension of the solution space. Moreover, the neurodynamic approach does not depend on some additional assumptions, such as the feasible region is bounded, the objective function is lower bounded over the feasible region or the objective function is coercive. Finally, both numerical illustrations and simulation results in support vector regression problem show the well performance and the viability of the proposed neurodynamic approach.