作者: Andrew R. Conn , Nicholas I. M. Gould , Philippe L. Toint
DOI: 10.1007/978-1-4757-3226-9_2
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摘要: A new primal-dual algorithm is proposed for the minimization of non-convex objective functions subject to simple bounds and linear equality constraints. The method alternates between a classical step Newton-like modified barrier in order ensure descent on suitable merit function. Convergence well-defined subsequence iterates proved from arbitrary starting points. Preliminary numerical results are presented.