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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. Numerical results are presented for general quadratic programs.
|Number of pages||35|
|Journal||Mathematical Programming Series B|
|Publication status||Published - 1 Apr 2000|