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In this article, we propose a three-dimensional dwindling filter algorithm for general nonlinear programming. The envelope of the three-dimensional dwindling filter becomes thinner and thinner as the step size approaches zero so that the new filter has more flexibility for the acceptance of the trial step size. Moreover, we show that the feasibility restoration phase, which is always used in traditional filter method, is not needed. The modified limited memory Broyden-Fletcher-Goldfarb-Shanno method is employed in the algorithm, and the update matrices are positive definite when the Lagrangian function is a general convex function. Under mild conditions, the global convergence of the new algorithm is analyzed. The primary numerical experiments are reported to show effectiveness of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a nonmonotone sequential quadratic programming-filter method for solving nonlinear equality constrained optimization. This new method has more flexibility for the acceptance of the trial step and requires less computational costs compared with the monotone methods. Under reasonable conditions, we give the global convergence properties. Further, the second-order correction step and nonmonotone reduction conditions are used to overcome Maratos effect so that quadratic local convergence is achieved. The numerical experiments are reported to show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]