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|Abstract=To compute one of the nonisolated Pareto-critical points of an unconstrained multicriteria optimization problem a Levenberg–Marquardt algorithm is applied. Sufficient conditions for an error bound are provided to prove its fast local convergence. A globalized version is shown to converge to a Pareto-optimal point under convexity assumptions. | |Abstract=To compute one of the nonisolated Pareto-critical points of an unconstrained multicriteria optimization problem a Levenberg–Marquardt algorithm is applied. Sufficient conditions for an error bound are provided to prove its fast local convergence. A globalized version is shown to converge to a Pareto-optimal point under convexity assumptions. | ||
| − | |DOI Name=doi:10.1016/j.orl.2008.02.006 | + | |DOI Name=doi:10.1016/j.orl.2008.02.006 |
|Forschungsgruppe=Effiziente Algorithmen | |Forschungsgruppe=Effiziente Algorithmen | ||
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Aktuelle Version vom 28. September 2009, 07:54 Uhr
A Levenberg–Marquardt algorithm for unconstrained multicriteria optimization
A Levenberg–Marquardt algorithm for unconstrained multicriteria optimization
Veröffentlicht: 2008
Journal: Operations Research Letters
Nummer: 5
Seiten: 643-646
Verlag: Elsevier
Volume: 36
Referierte Veröffentlichung
Kurzfassung
To compute one of the nonisolated Pareto-critical points of an unconstrained multicriteria optimization problem a Levenberg–Marquardt algorithm is applied. Sufficient conditions for an error bound are provided to prove its fast local convergence. A globalized version is shown to converge to a Pareto-optimal point under convexity assumptions.
DOI Link: doi:10.1016/j.orl.2008.02.006
Forschungsgruppe
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