期刊
MATHEMATICAL PROGRAMMING
卷 87, 期 2, 页码 215-249出版社
SPRINGER VERLAG
DOI: 10.1007/s101070050112
关键词
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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.
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