[Show abstract][Hide abstract]ABSTRACT: In this paper, we consider a trust region algorithm for unconstrained optimization problems. Unlike the traditional memoryless
trust region methods, our trust region model includes memory of the past iteration, which makes the algorithm less myopic
in the sense that its behavior is not completely dominated by the local nature of the objective function, but rather by a
more global view. The global convergence is established by using a nonmonotone technique. The numerical tests are also given
to show the efficiency of our proposed method.
Article · Jun 2011 · Journal of Mathematical Modelling and Algorithms