Interior-Point Methods for Nonconvex Nonlinear Programming: Filter Methods and Merit Functions

Recently, Fletcher and Leyffer proposed using filter methods instead of a merit function to control steplengths in a sequential quadratic programming algorithm. In this paper, we analyze possible ways to implement a filter-based approach in an interior-point algorithm. Extensive numerical testing sh...

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Veröffentlicht in:Computational optimization and applications 2002-11, Vol.23 (2), p.257-257
Hauptverfasser: Benson, Hande Y, Vanderbei, Robert J, Shanno, David F
Format: Artikel
Sprache:eng
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Zusammenfassung:Recently, Fletcher and Leyffer proposed using filter methods instead of a merit function to control steplengths in a sequential quadratic programming algorithm. In this paper, we analyze possible ways to implement a filter-based approach in an interior-point algorithm. Extensive numerical testing shows that such an approach is more efficient than using a merit function alone. [PUBLICATION ABSTRACT]
ISSN:0926-6003
1573-2894
DOI:10.1023/A:1020533003783