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...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Computational optimization and applications Ročník 81; číslo 2; s. 423 - 478
Hlavní autori:	Sharma, Meenarli, Palkar, Prashant, Mahajan, Ashutosh
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.03.2022 Springer Nature B.V
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Buďte prvý, kto okomentuje tento záznam!