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In this paper, we present a trust region algorithm with memory for equality constrained optimization problems. Different from the traditional trust 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. [ABSTRACT FROM AUTHOR]

In this paper, we develop convergence theory of the implicit filtering method for solving the box constrained optimization whose objective function includes a smooth term and a noisy term. It is shown that under certain assumption on the noisy function, the sequence of projected gradients on the smooth function produced by the method goes to zero. Moreover, it is shown that if the smooth function is convex and the noisy function decays near optimality, the whole sequence of iterates converges to a solution of the concerned problem and possesses the finite identification for the optimal active set under the nondegenerate assumption. Finally, preliminary numerical results are reported. [ABSTRACT FROM AUTHOR]