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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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Contributor : Clément Bouttier Connect in order to contact the contributor
Submitted on : Monday, February 27, 2017 - 5:32:31 PM
Last modification on : Friday, December 24, 2021 - 3:18:05 PM
Long-term archiving on: : Sunday, May 28, 2017 - 2:34:49 PM


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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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