A Globally Convergent Primal-Dual Interior-Point Filter Method for Nonconvex Nonlinear Programming

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dc.contributor.authorUlbrich, Michaelen_US
dc.contributor.authorUlbrich, Stefanen_US
dc.contributor.authorVicente, Luis N.en_US
dc.date.accessioned2018-06-18T17:48:13Zen_US
dc.date.available2018-06-18T17:48:13Zen_US
dc.date.issued2000-04en_US
dc.date.noteApril 2000en_US
dc.description.abstractIn this paper, the filter technique of Fletcher and Leyffer (1997) is used to globalize the primal-dual interior-point algorithm for nonlinear programming, avoiding the use of merit functions and the updating of penalty parameters. The new algorithm decomposes the primal-dual step obtained from the perturbed first-order necessary conditions into a normal and a tangential step, whose sizes are controlled by a trust-region type parameter. Each entry in the filter is a pair of coordinates: one resulting from feasibility and centrality, and associated with the normal step; the other resulting from optimality (complementarity and duality), and related with the tangential step. Global convergence to first-order critical points is proved for the new primal-dual interior-point filter algorithm.en_US
dc.format.extent29 ppen_US
dc.identifier.citationUlbrich, Michael, Ulbrich, Stefan and Vicente, Luis N.. "A Globally Convergent Primal-Dual Interior-Point Filter Method for Nonconvex Nonlinear Programming." (2000) <a href="https://hdl.handle.net/1911/101941">https://hdl.handle.net/1911/101941</a>.en_US
dc.identifier.digitalTR00-12en_US
dc.identifier.urihttps://hdl.handle.net/1911/101941en_US
dc.language.isoengen_US
dc.titleA Globally Convergent Primal-Dual Interior-Point Filter Method for Nonconvex Nonlinear Programmingen_US
dc.typeTechnical reporten_US
dc.type.dcmiTexten_US
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