Two unconstrained optimization approaches for the Euclidean κ-centrum location problem

Shaohua Pan, Jein Shan Chen

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Consider the single-facility Euclidean κ-centrum location problem in Rn. This problem is a generalization of the classical Euclidean 1-median problem and 1-center problem. In this paper, we develop two efficient algorithms that are particularly suitable for problems where n is large by using unconstrained optimization techniques. The first algorithm is based on the neural networks smooth approximation for the plus function and reduces the problem to an unconstrained smooth convex minimization problem. The second algorithm is based on the Fischer-Burmeister merit function for the second-order cone complementarity problem and transforms the KKT system of the second-order cone programming reformulation for the problem into an unconstrained smooth minimization problem. Our computational experiments indicate that both methods are extremely efficient for large problems and the first algorithm is able to solve problems of dimension n up to 10,000 efficiently.

Original languageEnglish
Pages (from-to)1368-1383
Number of pages16
JournalApplied Mathematics and Computation
Volume189
Issue number2
DOIs
Publication statusPublished - 2007 Jun 15

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Keywords

  • Merit function
  • Second-order cone programming
  • Smoothing function
  • The Euclidean κ-centrum problem

ASJC Scopus subject areas

  • Computational Mathematics
  • Applied Mathematics

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