Convergence rate of a simulated annealing algorithm with noisy observations

Clément Bouttier; Ioana Gavra

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arXiv · 1703.00329

Convergence rate of a simulated annealing algorithm with noisy observations

Clément Bouttier·Ioana Gavra

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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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BibTeX RIS

Clément Bouttier, Ioana Gavra. 2017-03-01. Convergence rate of a simulated annealing algorithm with noisy observations. https://arxiv.org/abs/1703.00329

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