Finding an optimized set of transformations for convexifying nonconvex MINLP problems

Andreas Lundell; null Andreas; Tapio Westerlund; null Tapio

Finding an optimized set of transformations for convexifying nonconvex MINLP problems

Andreas Lundell, Andreas, Tapio Westerlund, Tapio

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussa › Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

3 Sitaatiot (Scopus)

Abstrakti

In this paper we describe a method for obtaining sets of transformations for reformulating a mixed integer nonlinear programming (MINLP) problem containing nonconvex twice-differentiable (C-2) functions to a convex MINLP problem in an extended variable space. The method for obtaining the transformations is based on solving a mixed integer linear programming (MILP) problem given the structure of the nonconvex MINLP problem. The solution of the MILP problem renders a minimal set of transformations convexifying the nonconvex problem. This technique is implemented as an part of the alpha signomial global optimization algorithm (alpha SGO), a global optimization algorithm for nonconvex MINLP problems.

Alkuperäiskieli	Ei tiedossa
Otsikko	11th International Symposium on Process Systems Engineering
Toimittajat	Iftekhar A Karimi, Rajagopalan Srinivasan
Kustantaja	Elsevier
Sivut	1497–1501
Sivumäärä	5
ISBN (painettu)	978-0-444-59505-8
Tila	Julkaistu - 2012
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisuussa
Tapahtuma	conference - Kesto: 1 tammikuuta 2012 → …

Konferenssi

Konferenssi	conference
Ajanjakso	01/01/12 → …

Keywords

global optimization
nonconvex MINLP problems
reformulation techniques
signomial functions

Pääsy asiakirjaan

http://www.sciencedirect.com/science/article/pii/B9780444595065501309

Viittausmuodot

@inproceedings{6baf8ca6d4a94f3cb71a035c7f21d445,

title = "Finding an optimized set of transformations for convexifying nonconvex MINLP problems",

abstract = "In this paper we describe a method for obtaining sets of transformations for reformulating a mixed integer nonlinear programming (MINLP) problem containing nonconvex twice-differentiable (C-2) functions to a convex MINLP problem in an extended variable space. The method for obtaining the transformations is based on solving a mixed integer linear programming (MILP) problem given the structure of the nonconvex MINLP problem. The solution of the MILP problem renders a minimal set of transformations convexifying the nonconvex problem. This technique is implemented as an part of the alpha signomial global optimization algorithm (alpha SGO), a global optimization algorithm for nonconvex MINLP problems.",

keywords = "global optimization, nonconvex MINLP problems, reformulation techniques, signomial functions, global optimization, nonconvex MINLP problems, reformulation techniques, signomial functions, global optimization, nonconvex MINLP problems, reformulation techniques, signomial functions",

author = "Andreas Lundell and Andreas and Tapio Westerlund and Tapio",

year = "2012",

language = "Odefinierat/ok{\"a}nt",

isbn = "978-0-444-59505-8",

pages = "1497–1501",

editor = "{A Karimi}, Iftekhar and Rajagopalan Srinivasan",

booktitle = "11th International Symposium on Process Systems Engineering",

publisher = "Elsevier",

note = "null ; Conference date: 01-01-2012",

}

Finding an optimized set of transformations for convexifying nonconvex MINLP problems. / Lundell, Andreas; Andreas, ; Westerlund, Tapio; Tapio.

11th International Symposium on Process Systems Engineering. toim. / Iftekhar A Karimi; Rajagopalan Srinivasan. Elsevier, 2012. s. 1497–1501.

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussa › Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

TY - GEN

T1 - Finding an optimized set of transformations for convexifying nonconvex MINLP problems

AU - Lundell, Andreas

AU - Andreas, null

AU - Westerlund, Tapio

AU - Tapio, null

PY - 2012

Y1 - 2012

N2 - In this paper we describe a method for obtaining sets of transformations for reformulating a mixed integer nonlinear programming (MINLP) problem containing nonconvex twice-differentiable (C-2) functions to a convex MINLP problem in an extended variable space. The method for obtaining the transformations is based on solving a mixed integer linear programming (MILP) problem given the structure of the nonconvex MINLP problem. The solution of the MILP problem renders a minimal set of transformations convexifying the nonconvex problem. This technique is implemented as an part of the alpha signomial global optimization algorithm (alpha SGO), a global optimization algorithm for nonconvex MINLP problems.

AB - In this paper we describe a method for obtaining sets of transformations for reformulating a mixed integer nonlinear programming (MINLP) problem containing nonconvex twice-differentiable (C-2) functions to a convex MINLP problem in an extended variable space. The method for obtaining the transformations is based on solving a mixed integer linear programming (MILP) problem given the structure of the nonconvex MINLP problem. The solution of the MILP problem renders a minimal set of transformations convexifying the nonconvex problem. This technique is implemented as an part of the alpha signomial global optimization algorithm (alpha SGO), a global optimization algorithm for nonconvex MINLP problems.

KW - global optimization

KW - nonconvex MINLP problems

KW - reformulation techniques

KW - signomial functions

KW - global optimization

KW - nonconvex MINLP problems

KW - reformulation techniques

KW - signomial functions

KW - global optimization

KW - nonconvex MINLP problems

KW - reformulation techniques

KW - signomial functions

M3 - Konferensbidrag

SN - 978-0-444-59505-8

SP - 1497

EP - 1501

BT - 11th International Symposium on Process Systems Engineering

A2 - A Karimi, Iftekhar

A2 - Srinivasan, Rajagopalan

PB - Elsevier

Y2 - 1 January 2012

ER -