A reformulation framework for global optimization

In this paper, we present a global optimization method for solving nonconvex mixed integer nonlinear programming (MINLP) problems. A convex overestimation of the feasible region is obtained by replacing the nonconvex constraint functions with convex underestimators. For signomial functions single-va...

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Veröffentlicht in:	Journal of global optimization Jg. 57; H. 1; S. 115 - 141
Hauptverfasser:	Lundell, Andreas, Skjäl, Anders, Westerlund, Tapio
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Boston Springer US 01.09.2013 Springer Nature B.V
Schlagworte:	Algorithms Approximation Computer Science Integer programming Mathematical programming Mathematics Mathematics and Statistics Mixed integer Nonlinear programming Operations Research/Decision Theory Optimization Optimization algorithms Real Functions Solvers Studies Transformations Variables Finland SGO-algorithm Global optimization Signomial functions Reformulation technique Convex underestimators Mixed integer nonlinear programming Twice-differentiable functions Piecewise linear functions BB-underestimator
ISSN:	0925-5001, 1573-2916
Online-Zugang:	Volltext
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