Weighted Euclidean centers

A. Arbel, R. Sadka

Research output: Contribution to journalArticlepeer-review

Abstract

This article addresses the derivation of a Euclidean center, which is defined as the point in decision space from which one can inscribe the largest sphere contained by the constraints. We extend this concept by introducing its weighted version, which we term the weighted Euclidean center. We show that by assigning weights to the different decision variables we can traverse the entire decision space. In addition, we show that the concept of a weighted Euclidean center and that of the achievement scalarizing function introduced by Wierzbicki are intimately related.

Original languageEnglish
Pages (from-to)239-251
Number of pages13
JournalOptimization
Volume54
Issue number3
DOIs
StatePublished - Jun 2005

Keywords

  • Achievement scalarizing function
  • Euclidean centers
  • Multiple-objective linear programming (MOLP)

Fingerprint

Dive into the research topics of 'Weighted Euclidean centers'. Together they form a unique fingerprint.

Cite this