Alternative regularizations for Outer-Approximation algorithms for convex MINLP

In this work, we extend the regularization framework from Kronqvist et al. (Math Program 180(1):285–310, 2020) by incorporating several new regularization functions and develop a regularized single-tree search method for solving convex mixed-integer nonlinear programming (MINLP) problems. We propose...

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Bibliographic Details
Published in:	Journal of global optimization Vol. 84; no. 4; pp. 807 - 842
Main Authors:	Bernal, David E., Peng, Zedong, Kronqvist, Jan, Grossmann, Ignacio E.
Format:	Journal Article
Language:	English
Published:	Ames Research Center Springer 01.12.2022 Springer US Springer Nature B.V
Subjects:	Algorithms Approximation Approximation algorithms Branch and bound method Branch and bound methods Computer Programming And Software Computer Science Convex MINLP Convex mixed-integer nonlinear programming Integer programming Mathematical analysis Mathematics Mathematics and Statistics Mixed integer Mixed integer non-linear programming problems Mixed integer optimization Mixed-integer nonlinear programming Nonlinear programming Open source software Operations Research/Decision Theory Optimization Outer approximation Outer approximation algorithm Outer approximation methods Real Functions Regularisation Regularization Regularization for mixed-integer optimization Regularization function Trees (mathematics) Outer Approximation Convex Mixed-Integer Nonlinear Programming Regularization Mixed-Integer Optimization Regularization for mixed-integer optimization Outer-Approximation Convex MINLP
ISSN:	0925-5001, 1573-2916, 1573-2916
Online Access:	Get full text
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