Linearization and parallelization schemes for convex mixed-integer nonlinear optimization

We develop and test linearization and parallelization schemes for convex mixed-integer nonlinear programming. Several linearization approaches are proposed for LP/NLP based branch-and-bound. Some of these approaches strengthen the linear approximation to nonlinear constraints at the root node and so...

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Bibliographic Details
Published in:	Computational optimization and applications Vol. 81; no. 2; pp. 423 - 478
Main Authors:	Sharma, Meenarli, Palkar, Prashant, Mahajan, Ashutosh
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.03.2022 Springer Nature B.V
Subjects:	Algorithms Approximation Convex and Discrete Geometry Linearization Management Science Mathematics Mathematics and Statistics Mixed integer Nodes Nonlinear programming Operations Research Operations Research/Decision Theory Optimization Statistics Toolkits Variables Branch-and-bound Convex MINLP Outer approximation Linearization techniques Shared-memory parallel
ISSN:	0926-6003, 1573-2894
Online Access:	Get full text
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