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Pré-Publication, Document De Travail Année : 2016

Convergence rate of a simulated annealing algorithm with noisy observations

Résumé

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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Dates et versions

hal-01477930 , version 1 (27-02-2017)

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