A class of trust region methods for linear inequality constrained optimization and its theory analysis II. local convergence rate and numerical tests

Abstract

In this paper we prove that a class of trust region methods presented in part I is superlinearly convergent. Numerical tests are reported thereafter. Results by solving a set of typical problems selected from literatures have demonstrated that our algorithm is effective.