Mathematical Programming

, Volume 54, Issue 1–3, pp 223–232

Convergence rates of a global optimization algorithm

  • Regina Hunter Mladineo
Article
  • 79 Downloads

Abstract

This paper presents a best and worst case analysis of convergence rates for a deterministic global optimization algorithm. Superlinear convergence is proved for Lipschitz functions which are convex in the direction of the global maximum (concave in the direction of the global minimum). Computer results are given, which confirm the theoretical convergence rates.

Key words

Global optimization Lipschitz functions 

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Copyright information

© The Mathematical Programming Society, Inc. 1992

Authors and Affiliations

  • Regina Hunter Mladineo
    • 1
  1. 1.Rider CollegeSchool of Business AdministrationLawrencevilleUSA

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