In this paper we present a rate of convergence analysis of an inexact proximal point algorithm to solve unconstrained quasiconvex multiobjective minimi-zation problems defined in Euclidean spaces, where the vector functions are locally Lipschitz. Under some natural assumptions, we prove that the sequence generated by the algorithm converges linearly and superlinearly to a critical Pareto-Clarke point of the problem.

In this paper we present a rate of convergence analysis of an inexact proximal point algorithm to solve unconstrained quasiconvex multiobjective minimi-zation problems defined in Euclidean spaces, where the vector functions are locally Lipschitz. Under some natural assumptions, we prove that the sequence generated by the algorithm converges linearly and superlinearly to a critical Pareto-Clarke point of the problem.