Constructing large feasible suboptimal intervals for constrained nonlinear optimization

T. Csendes, Zelda B. Zabinsky, Birna P. Kristinsdottir

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

An algorithm for finding a large feasible n-dimensional interval for constrained global optimization is presented. The n-dimensional interval is iteratively enlarged about a seed point while maintaining feasibility. An interval subdivision method may be used to check feasibility of the growing box. The resultant feasible interval is constrained to lie within a given level set, thus ensuring it is close to the optimum. The ability to determine such a feasible interval is useful for exploring the neighbourhood of the optimum, and can be practically used in manufacturing considerations. The numerical properties of the algorithm are tested and demonstrated by an example problem.

Original languageEnglish
Pages (from-to)279-293
Number of pages15
JournalAnnals of Operations Research
Volume58
Issue number4
DOIs
Publication statusPublished - Jul 1995

Fingerprint

Nonlinear optimization
Global optimization
Manufacturing

Keywords

  • AMS subject classification: 90C30, 65K05
  • Constrained nonlinear optimization
  • inclusion function
  • interval arithmetic
  • sensitivity analysis

ASJC Scopus subject areas

  • Management Science and Operations Research
  • Decision Sciences(all)

Cite this

Constructing large feasible suboptimal intervals for constrained nonlinear optimization. / Csendes, T.; Zabinsky, Zelda B.; Kristinsdottir, Birna P.

In: Annals of Operations Research, Vol. 58, No. 4, 07.1995, p. 279-293.

Research output: Contribution to journalArticle

Csendes, T. ; Zabinsky, Zelda B. ; Kristinsdottir, Birna P. / Constructing large feasible suboptimal intervals for constrained nonlinear optimization. In: Annals of Operations Research. 1995 ; Vol. 58, No. 4. pp. 279-293.
@article{bb85e1138b264ae59ac2e6af6aa02168,
title = "Constructing large feasible suboptimal intervals for constrained nonlinear optimization",
abstract = "An algorithm for finding a large feasible n-dimensional interval for constrained global optimization is presented. The n-dimensional interval is iteratively enlarged about a seed point while maintaining feasibility. An interval subdivision method may be used to check feasibility of the growing box. The resultant feasible interval is constrained to lie within a given level set, thus ensuring it is close to the optimum. The ability to determine such a feasible interval is useful for exploring the neighbourhood of the optimum, and can be practically used in manufacturing considerations. The numerical properties of the algorithm are tested and demonstrated by an example problem.",
keywords = "AMS subject classification: 90C30, 65K05, Constrained nonlinear optimization, inclusion function, interval arithmetic, sensitivity analysis",
author = "T. Csendes and Zabinsky, {Zelda B.} and Kristinsdottir, {Birna P.}",
year = "1995",
month = "7",
doi = "10.1007/BF02096403",
language = "English",
volume = "58",
pages = "279--293",
journal = "Annals of Operations Research",
issn = "0254-5330",
publisher = "Springer Netherlands",
number = "4",

}

TY - JOUR

T1 - Constructing large feasible suboptimal intervals for constrained nonlinear optimization

AU - Csendes, T.

AU - Zabinsky, Zelda B.

AU - Kristinsdottir, Birna P.

PY - 1995/7

Y1 - 1995/7

N2 - An algorithm for finding a large feasible n-dimensional interval for constrained global optimization is presented. The n-dimensional interval is iteratively enlarged about a seed point while maintaining feasibility. An interval subdivision method may be used to check feasibility of the growing box. The resultant feasible interval is constrained to lie within a given level set, thus ensuring it is close to the optimum. The ability to determine such a feasible interval is useful for exploring the neighbourhood of the optimum, and can be practically used in manufacturing considerations. The numerical properties of the algorithm are tested and demonstrated by an example problem.

AB - An algorithm for finding a large feasible n-dimensional interval for constrained global optimization is presented. The n-dimensional interval is iteratively enlarged about a seed point while maintaining feasibility. An interval subdivision method may be used to check feasibility of the growing box. The resultant feasible interval is constrained to lie within a given level set, thus ensuring it is close to the optimum. The ability to determine such a feasible interval is useful for exploring the neighbourhood of the optimum, and can be practically used in manufacturing considerations. The numerical properties of the algorithm are tested and demonstrated by an example problem.

KW - AMS subject classification: 90C30, 65K05

KW - Constrained nonlinear optimization

KW - inclusion function

KW - interval arithmetic

KW - sensitivity analysis

UR - http://www.scopus.com/inward/record.url?scp=21844495592&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=21844495592&partnerID=8YFLogxK

U2 - 10.1007/BF02096403

DO - 10.1007/BF02096403

M3 - Article

VL - 58

SP - 279

EP - 293

JO - Annals of Operations Research

JF - Annals of Operations Research

SN - 0254-5330

IS - 4

ER -