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A Conditional Gaussian Martingale Algorithm for Global Optimization

  • Manuel L. Esquível
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3982)

Abstract

A new stochastic algorithm for determination of a global minimum of a real valued continuous function defined on K, a compact set of ℝ n , having an unique global minimizer in K is introduced and studied, a context discussion is presented and implementations are used to compare the performance of the algorithm with other algorithms. The algorithm may be thought to belong to the random search class but although we use Gaussian distributions, the mean is changed at each step to be the intermediate minimum found at the preceding step and the standard deviations, on the diagonal of the covariance matrix, are halved from one step to the next. The convergence proof is simple relying on the fact that the sequence of intermediate random minima is an uniformly integrable conditional Gaussian martingale.

Keywords

Global Optimization Random Search Preceding Step Stochastic Algorithm Random Search Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Manuel L. Esquível
    • 1
  1. 1.Departamento de Matemática and CMAFCT/UNL, Quinta da TorreCaparicaPortugal

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