A Short Introduction to Stochastic Optimization

• Autorzy: Ombach J.

Identyfikatory

DOI

10.4467/20838476SI.14.001.3018

• Języki publikacji: EN

Abstrakty

EN

We present some typical algorithms used for finding global minimum/ maximum of a function defined on a compact finite dimensional set, discuss commonly observed procedures for assessing and comparing the algorithms’ performance and quote theoretical results on convergence of a broad class of stochastic algorithms.

Słowa kluczowe

EN

global optimization; stochastic algorithm; random search; convergence of metaheuristics

• Wydawca: Wydawnictwo Uniwersytetu Jagiellońskiego
• Czasopismo: Schedae Informaticae
• Rocznik: 2014
• Tom: Vol. 23
• Strony: 9--20
• Opis fizyczny: Bibliogr. 19 poz.

Twórcy

autor

Ombach J.

• Department of Mathematics Faculty of Mathematics and Computer Science Jagiellonian University ul. Łojasiewicza 6, 30-348 Kraków

Bibliografia

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• [6] Nocedal J., Wright S. J., Numerical optimization, Springer Series in Operations Research, Springer-Verlag, New York, 1999.
• [7] Ombach J., A Proof of Convergence of General Stochastic Search for Global Minimum, Journal of Difference Equations and Applications 13, 2007, pp. 795–802.
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• [9] Ombach J., Tarłowski D., Nonautonomous Stochastic Search in Global Optimization, Journal in Nonlinear Sci. 22, 2012, pp. 169–185.
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• [15] Tarłowski D., Nonautonomous Dynamical Systems in Stochastic Global Optimization, Ph.D. thesis, Department of Mathematics, Jagiellonian University 2014.
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• [18] Wright M., The interior-point revolution in optimization: History, recent developments, and lasting consequences, Bull. Amer. Math. Soc. 42, 2005, pp. 39–56.
• [19] Yang R.L., Convergence of the Simulated Annealing Algorithm for Continuous Global Optimization, Journal of Optimization Theory and Applications 104,2000, pp. 691–716.