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A Primal-Dual Trust-Region Algorithmfor Minimizing a Non-convex FunctionSubject to General Inequalityand Linear Equality

A new primal-dual algorithm is proposed for the minimization of non-convex objective functions subject to general inequality and linear equality constraints. The method uses a primal-dual trust-region model to ensure descent on a suitable merit function. Convergence is proved to second-order critical points from arbitrary starting points. Preliminary numerical results are presented.