A Trust Region Algorithm with Memory Model for Convex Constrained Optimization

In this paper, we develop a trust region algorithm for convex constrained optimizationproblems. Different from the traditional trust region algorithms, our trust region model includesmemory of the past iteration, which makes the algorithm more farsighted in the sense that its behav-ior is not completely dominated by the local nature of the objective function. We present a nonmono-tone algorithm that has this feature and prove its global convergence under suitable conditions.