On solving generalized convex MINLP problems using supporting hyperplane techniques

Solution methods for convex mixed integer nonlinear programming (MINLP) problems have, usually, proven convergence properties if the functions involved are differentiable and convex. For other classes of convex MINLP problems fewer results have been given. Classical differential calculus can, though...

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Vydané v:	Journal of global optimization Ročník 71; číslo 4; s. 987 - 1011
Hlavní autori:	Westerlund, Tapio, Eronen, Ville-Pekka, Mäkelä, Marko M.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.08.2018 Springer Springer Nature B.V
Predmet:	Algorithms Calculus Computer Science Convergence Convex analysis Differential calculus Mathematics Mathematics and Statistics Methods Mixed integer Nonlinear programming Operations Research/Decision Theory Optimization Properties (attributes) Real Functions Mixed-integer nonlinear programming Cutting planes 90C25 90C11 Nonsmooth optimization Supporting hyperplanes Generalized convexities
ISSN:	0925-5001, 1573-2916
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