A feasible trust-region algorithm for inequality constrained optimization

The paper presents an algorithm for smooth nonlinearly inequality constrained optimization problems, in which a sequence of feasible iterates is generated by a trust-region sequential quadratic programming subproblem at each iteration. Because of retaining feasibility, the objective function can be...

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Veröffentlicht in:Applied mathematics and computation Jg. 173; H. 1; S. 513 - 522
Hauptverfasser: Peng, Ye-hui, Yao, Shengbao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY Elsevier Inc 01.02.2006
Elsevier
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ISSN:0096-3003, 1873-5649
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Zusammenfassung:The paper presents an algorithm for smooth nonlinearly inequality constrained optimization problems, in which a sequence of feasible iterates is generated by a trust-region sequential quadratic programming subproblem at each iteration. Because of retaining feasibility, the objective function can be used as a merit function and the subproblems are feasible. Under common assumptions, the algorithm is globally convergent. The numerical results show it is promising.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2005.04.080