A Short Introduction to Stochastic Optimization
cytuj
pobierz pliki
RIS BIB ENDNOTEChoose format
RIS BIB ENDNOTEA Short Introduction to Stochastic Optimization
Publication date: 14.04.2015
Schedae Informaticae, 2014, Volume 23, pp. 9 - 20
https://doi.org/10.4467/20838476SI.14.001.3018Authors
A Short Introduction to Stochastic Optimization
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.
Appel M.J., Labarre R., Radulovic D., On Accelerated Random Search, SIAM J. Optim. 14(3), 2003, pp. 708–731.
Bennett K.P., Parrado-Hern E., The Interplay of Optimization and Machine Learning Research, Journal of Machine Learning Research 7, 2006, pp. 1265– 1281.
Bialy J., Ciecko A., Cwiklak J., Grzegorzewski M., Koscielniak P., Ombach J., Oszczak S., Aircraft Landing System Utilizing a GPS Receiver with Position Prediction Functionality, Proceedings of the 24th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS 2011), Portland, OR, September 2011, pp. 457–467.
Locatelli M., Convergence of a Simulated Annealing Algorithm for Continuous Global Optimization, J. Global Optim. 18, 2000, pp. 219–233.
Luke S., Essentials of Metaheuristics, Lulu.com, 2011.
Nocedal J., Wright S. J., Numerical optimization, Springer Series in Operations Research, Springer-Verlag, New York, 1999.
Ombach J., A Proof of Convergence of General Stochastic Search for Global Minimum, Journal of Difference Equations and Applications 13, 2007, pp. 795–802.
Ombach J., Stability of evolutionary algorithms, Journal Math Anal Appl. 342, 2008, pp. 326–333.
Ombach J., Tar lowski D., Nonautonomous Stochastic Search in Global Optimization, Journal in Nonlinear Sci. 22, 2012, pp. 169–185.
Ombach J., Tar lowski D., Stochastyczne algorytmy optymalizacji z perspektywy uk lad´ow dynamicznych, in Polish, preprint.
Radwa´nski M., Convergence of nonautonomous evolutionary algorithm, Universitatis Iagellonicae Acta Mathematica 45, 2007, pp. 197–206.
Robert Ch., Casella G., Monte Carlo Statistical Methods. Springer Heidelberg 2004.
Tar lowski D., Sufficient conditions for the convergence of non-autonomous stochastic search for a global minimum, UIAM, 2011, pp. 73–83.
Tar lowski D., Nonautonomous stochastic search for global minimum in continuous optimization, Journal Math Anal Appl. 412, 2014, pp. 631–645.
Tar lowski D., Nonautonomous Dynamical Systems in Stochastic Global Optimization, Ph.D. thesis, Department of Mathematics, Jagiellonian University 2014.
Whitley D., Mathias K., Rana S., Dzubera J., Evaluating Evolutionary Algorithms, preprint.
Weise T., Global Optimization Algorithms – Theory and Application, http://www.it-weise.de/
Wright M., The interior-point revolution in optimization: History, recent developments, and lasting consequences, Bull. Amer. Math. Soc. 42, 2005, pp. 39–56.
Yang R.L., Convergence of the Simulated Annealing Algorithm for Continuous Global Optimization, Journal of Optimization Theory and Applications 104, 2000, pp. 691–716.
Information: Schedae Informaticae, 2014, Volume 23, pp. 9 - 20
Article type: Original article
Titles:
A Short Introduction to Stochastic Optimization
A Short Introduction to Stochastic Optimization
Department of Mathematics Faculty of Mathematics and Computer Science Jagiellonian University, ul. Łojasiewicza 6, 30-348 Kraków, Poland
Published at: 14.04.2015
Article status: Open
Licence: None
Percentage share of authors:
Article corrections:
-Publication languages:
English