On the convergence of localisation search

David Bulger, Graham Wood

Research output: Chapter in Book/Report/Conference proceedingChapter


Localisation Search, a generalisation of Pure Adaptive Search, is a stochastic global optimisation algorithm in which iterates are chosen randomly from a superset, or localisation, of the improving region. Recent theoretical results are applied to determine how closely the localisation must track the improving region in order that the number of function evaluations to convergence increases at most polynomially with dimension, for Lipschitz objective functions and uniformly bounded domains.
Original languageEnglish
Title of host publicationState of the Art in Global Optimisation
Subtitle of host publicationComputational Methods and Applications
EditorsChris Floudas, Panos Pardalos
Place of PublicationBoston
PublisherSpringer, Springer Nature
Number of pages7
ISBN (Electronic)9781461334378
ISBN (Print)9781461334392
Publication statusPublished - 1996
Externally publishedYes

Publication series

NameNonconvex Optimization and Its Applications
ISSN (Print)1571-568X


  • global optimization
  • algorithm complexity
  • random search
  • adaptive search
  • localisation
  • Lipschitz function


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