Global solution of optimization problems with signomial parts

In this paper a new approach for the global solution of nonconvex MINLP (Mixed Integer NonLinear Programming) problems that contain signomial (generalized geometric) expressions is proposed and illustrated. By applying different variable transformation techniques and a discretization scheme a lower...

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Veröffentlicht in:Discrete optimization Jg. 5; H. 1; S. 108 - 120
Hauptverfasser: Pörn, Ray, Björk, Kaj-Mikael, Westerlund, Tapio
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.02.2008
Schlagworte:
Convexification
Global optimization
Mixed integer nonlinear programming
Signomials
Variable transformations
Signomials
Variable transformations
Global optimization
Mixed integer nonlinear programming
Convexification
ISSN:1572-5286, 1873-636X
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Beschreibung
Zusammenfassung:In this paper a new approach for the global solution of nonconvex MINLP (Mixed Integer NonLinear Programming) problems that contain signomial (generalized geometric) expressions is proposed and illustrated. By applying different variable transformation techniques and a discretization scheme a lower bounding convex MINLP problem can be derived. The convexified MINLP problem can be solved with standard methods. The key element in this approach is that all transformations are applied termwise. In this way all convex parts of the problem are left unaffected by the transformations. The method is illustrated by four example problems.
ISSN:1572-5286
1873-636X
DOI:10.1016/j.disopt.2007.11.005