A Trust Region Algorithm with Memory for Equality Constrained Optimization

In this paper, we present a trust region algorithm with memory for equality constrained optimization problems. Different from the traditional Mist region algorithms, our trust region model includes memory of the past iterations, which makes the algorithm more farsighted 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. We report numerical tests to examine the effectiveness of the algorithm.