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dc.contributor.authorUlbrich, Michael
Ulbrich, Stefan
Vicente, Luis N.
dc.date.accessioned 2018-06-18T17:48:13Z
dc.date.available 2018-06-18T17:48:13Z
dc.date.issued 2000-04
dc.identifier.citation Ulbrich, Michael, Ulbrich, Stefan and Vicente, Luis N.. "A Globally Convergent Primal-Dual Interior-Point Filter Method for Nonconvex Nonlinear Programming." (2000) https://hdl.handle.net/1911/101941.
dc.identifier.urihttps://hdl.handle.net/1911/101941
dc.description.abstract In 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.
dc.format.extent 29 pp
dc.title A Globally Convergent Primal-Dual Interior-Point Filter Method for Nonconvex Nonlinear Programming
dc.type Technical report
dc.date.note April 2000
dc.identifier.digital TR00-12
dc.type.dcmi Text


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