Convergence rate of a simulated annealing algorithm with noisy observations

Abstract : In this paper we propose a modified version of the simulated annealing algorithm for solving a stochastic global optimization problem. More precisely, we address the problem of finding a global minimizer of a function with noisy evaluations. We provide a rate of convergence and its optimized parametrization to ensure a minimal number of evaluations for a given accuracy and a confidence level close to 1. This work is completed with a set of numerical experimentations and assesses the practical performance both on benchmark test cases and on real world examples.
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Contributeur : Clément Bouttier <>
Soumis le : lundi 27 février 2017 - 17:32:31
Dernière modification le : mercredi 23 mai 2018 - 17:58:04
Document(s) archivé(s) le : dimanche 28 mai 2017 - 14:34:49


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  • HAL Id : hal-01477930, version 1
  • ARXIV : 1703.00329


Clément Bouttier, Ioana Gavra. Convergence rate of a simulated annealing algorithm with noisy observations. 2016. 〈hal-01477930〉



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