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dc.contributor.authorRoma, M.
dc.contributor.authorLucidi, S.
dc.contributor.authorFerris, Michael
dc.description.abstractWe present a new algorithmic framework for solving unconstrained minimization problems that incorporates a curvilinear linesearch. The search direction used in our framework is a combination of an approximate Newton direction and a direction of negative curvature. Global convergence to a stationary point where the Hessian matrix is positive semidefinite is a exhibited for this class of algorithms by means of a nonmonotone stabilization strategy. An implementation using the Bunch-Parlett decomposition is shown to outperform several other techniques on a large class of test problems.en
dc.subjectunconstrained optimizationen
dc.titleNonmonotone Curvilinear Line Search Methods for Unconstrained Optimizationen
dc.typeTechnical Reporten

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  • Math Prog Technical Reports
    Math Prog Technical Reports Archive for the Department of Computer Sciences at the University of Wisconsin-Madison

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