A Gradient Projection Method for Quasiconvex Programming with Nonlinear Constraints

Abstract:

      This paper proposes a gradient projection algorithm to hanlde quasiconvex programming with nonlinear constraints. The algorithm not only has avoided Polak's perturbation procedure, but it needs only one gradient projection at each iteration instead of projecting two times as in most of the projection methods; and a simple method for constructing a new improved feasible direction is given, It is proved that the algorithm either terminates at an optimal solution after finitely many steps or it generates a sequence of feasible points whose every limit point is an optimal solution of the original problem.