Scheme for unifying optimization and constraint satisfaction methods

John Hooker, Greger Ottosson, Erlender S. Thorsteinsson, Hak Jin Kim

Research output: Contribution to journalArticle

39 Citations (Scopus)

Abstract

Optimization and constraint satisfaction methods are complementary to a large extent, and there has been much recent interest in combining them. Yet no generally accepted principle or scheme for their merger has evolved. We propose a scheme based on two fundamental dualities: the duality of search and inference, and the duality of strengthening and relaxation. Optimization as well as constraint satisfaction methods can be seen as exploiting these dualities in their respective ways. Our proposal is that rather than employ either type of method exclusively, one can focus on how these dualities can be exploited in a given problem class. The resulting algorithm is likely to contain elements from both optimization and constraint satisfaction, and perhaps new methods that belong to neither.

Original languageEnglish
Pages (from-to)11-30
Number of pages20
JournalKnowledge Engineering Review
Volume15
Issue number1
DOIs
Publication statusPublished - 2000 Mar 1

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

Cite this

Hooker, John ; Ottosson, Greger ; Thorsteinsson, Erlender S. ; Kim, Hak Jin. / Scheme for unifying optimization and constraint satisfaction methods. In: Knowledge Engineering Review. 2000 ; Vol. 15, No. 1. pp. 11-30.
@article{c6b21d83f61747278fb97cecd666c480,
title = "Scheme for unifying optimization and constraint satisfaction methods",
abstract = "Optimization and constraint satisfaction methods are complementary to a large extent, and there has been much recent interest in combining them. Yet no generally accepted principle or scheme for their merger has evolved. We propose a scheme based on two fundamental dualities: the duality of search and inference, and the duality of strengthening and relaxation. Optimization as well as constraint satisfaction methods can be seen as exploiting these dualities in their respective ways. Our proposal is that rather than employ either type of method exclusively, one can focus on how these dualities can be exploited in a given problem class. The resulting algorithm is likely to contain elements from both optimization and constraint satisfaction, and perhaps new methods that belong to neither.",
author = "John Hooker and Greger Ottosson and Thorsteinsson, {Erlender S.} and Kim, {Hak Jin}",
year = "2000",
month = "3",
day = "1",
doi = "10.1017/S0269888900001077",
language = "English",
volume = "15",
pages = "11--30",
journal = "Knowledge Engineering Review",
issn = "0269-8889",
publisher = "Cambridge University Press",
number = "1",

}

Scheme for unifying optimization and constraint satisfaction methods. / Hooker, John; Ottosson, Greger; Thorsteinsson, Erlender S.; Kim, Hak Jin.

In: Knowledge Engineering Review, Vol. 15, No. 1, 01.03.2000, p. 11-30.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Scheme for unifying optimization and constraint satisfaction methods

AU - Hooker, John

AU - Ottosson, Greger

AU - Thorsteinsson, Erlender S.

AU - Kim, Hak Jin

PY - 2000/3/1

Y1 - 2000/3/1

N2 - Optimization and constraint satisfaction methods are complementary to a large extent, and there has been much recent interest in combining them. Yet no generally accepted principle or scheme for their merger has evolved. We propose a scheme based on two fundamental dualities: the duality of search and inference, and the duality of strengthening and relaxation. Optimization as well as constraint satisfaction methods can be seen as exploiting these dualities in their respective ways. Our proposal is that rather than employ either type of method exclusively, one can focus on how these dualities can be exploited in a given problem class. The resulting algorithm is likely to contain elements from both optimization and constraint satisfaction, and perhaps new methods that belong to neither.

AB - Optimization and constraint satisfaction methods are complementary to a large extent, and there has been much recent interest in combining them. Yet no generally accepted principle or scheme for their merger has evolved. We propose a scheme based on two fundamental dualities: the duality of search and inference, and the duality of strengthening and relaxation. Optimization as well as constraint satisfaction methods can be seen as exploiting these dualities in their respective ways. Our proposal is that rather than employ either type of method exclusively, one can focus on how these dualities can be exploited in a given problem class. The resulting algorithm is likely to contain elements from both optimization and constraint satisfaction, and perhaps new methods that belong to neither.

UR - http://www.scopus.com/inward/record.url?scp=0034155409&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0034155409&partnerID=8YFLogxK

U2 - 10.1017/S0269888900001077

DO - 10.1017/S0269888900001077

M3 - Article

AN - SCOPUS:0034155409

VL - 15

SP - 11

EP - 30

JO - Knowledge Engineering Review

JF - Knowledge Engineering Review

SN - 0269-8889

IS - 1

ER -