Convex mixed-integer nonlinear programs derived from generalized disjunctive programming using cones

We propose the formulation of convex Generalized Disjunctive Programming (GDP) problems using conic inequalities leading to conic GDP problems. We then show the reformulation of conic GDPs into Mixed-Integer Conic Programming (MICP) problems through both the big-M and hull reformulations. These refo...

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Veröffentlicht in:	Computational optimization and applications Jg. 88; H. 1; S. 251 - 312
Hauptverfasser:	Bernal Neira, David E., Grossmann, Ignacio E.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.05.2024 Springer Nature B.V
Schlagworte:	Algebra Approximation Artificial intelligence Chemical engineering Cones Convex analysis Convex and Discrete Geometry Integer programming Linear programming Machine learning Management Science Mathematical analysis Mathematics Mathematics and Statistics Mixed integer Nonlinear programming Operations Research Operations Research/Decision Theory Optimization Solvers Statistics Systems engineering Perspective function Generalized disjunctive programming Mixed-integer nonlinear programming Conic programming
ISSN:	0926-6003, 1573-2894
Online-Zugang:	Volltext
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