Globally Convergent Interior-Point Algorithm for Nonlinear Programming

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Dettagli Bibliografici
Pubblicato in:	Journal of Optimization Theory and Applications vol. 125, no. 3 (Jun 2005), p. 497
Autore principale:	Akrotirianakis, I
Altri autori:	Rustem, B
Pubblicazione:	Springer Nature B.V.
Soggetti:	Algorithms Optimization
Accesso online:	Citation/Abstract Full Text Full Text - PDF
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Descrizione
Abstract:	This paper presents a primal-dual interior-point algorithm for solving general constrained nonlinear programming problems. The inequality constraints are incorporated into the objective function by means of a logarithmic barrier function. Also, satisfaction of the equality constraints is enforced through the use of an adaptive quadratic penalty function. The penalty parameter is determined using a strategy that ensures a descent property for a merit function. Global convergence of the algorithm is achieved through the monotonic decrease of a merit function. Finally, extensive computational results show that the algorithm can solve large and difficult problems in an efficient and robust way.
ISSN:	0022-3239 1573-2878
DOI:	10.1007/s10957-005-2086-2
Fonte:	ABI/INFORM Global