Generalized Proximal Point Algorithms for Quasiconvex Programming

In this paper, we proposed algorithms interior proximal methods based on entropy-like distance for the minimization of the quasiconvex function subjected to nonnegativity constraints. Under the assumptions that the objective function is bounded below and continuously di erentiable, we established the well de nedness of the sequence generated by the algorithms and obtained two important convergence results, the principal one is a sufficient conditionfor the convergence point of the sequence generated by the algorithms is apoint of solution of the problem.