Convex mixed integer nonlinear programming problems and an outer approximation algorithm

In this paper, we mainly study one class of convex mixed-integer nonlinear programming problems (MINLPs) with non-differentiable data. By dropping the differentiability assumption, we substitute gradients with subgradients obtained from KKT conditions, and use the outer approximation method to refor...

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Vydané v:	Journal of global optimization Ročník 63; číslo 2; s. 213 - 227
Hlavní autori:	Wei, Zhou, Ali, M. Montaz
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.10.2015 Springer Springer Nature B.V
Predmet:	Algorithms Applied mathematics Approximation Computer Science Convergence Convex analysis Equivalence Integer programming Mathematical analysis Mathematics Mathematics and Statistics Mixed integer Nonlinear programming Operations Research/Decision Theory Optimization Optimization algorithms Real Functions Variables Convex MINLP 90C30 90C25 90C11 Master program Decomposition Outer approximation
ISSN:	0925-5001, 1573-2916
On-line prístup:	Získať plný text
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