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 |
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Format: | Artikel |
Sprache: | eng |
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Online-Zugang: | Volltext |
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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] |
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ISSN: | 0926-6003 1573-2894 |
DOI: | 10.1023/A:1020533003783 |