Sufficient conditions for the convergence of nonautonomous stochastic search for a global minimum
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Sufficient conditions for the convergence of nonautonomous stochastic search for a global minimum
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RIS BIB ENDNOTESufficient conditions for the convergence of nonautonomous stochastic search for a global minimum
Publication date: 05.06.2012
Universitatis Iagellonicae Acta Mathematica, 2011, Volume 49, pp. 73-83
https://doi.org/10.4467/20843828AM.12.005.0457Authors
Sufficient conditions for the convergence of nonautonomous stochastic search for a global minimum
The majority of stochastic optimization algorithms can be written in the general form xt+1 = Tt(xt; yt), where xt is a sequence of points and parameters which are transformed by the algorithm, Tt are the methods of the algorithm and yt represent the randomness of the algorithm. We extend the results of papers [11] and [14] to provide some new general conditions under which the algorithm finds a global minimum with probability one.
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Information: Universitatis Iagellonicae Acta Mathematica, 2011, Volume 49, pp. 73-83
Article type: Original article
Titles:
Sufficient conditions for the convergence of nonautonomous stochastic search for a global minimum
Sufficient conditions for the convergence of nonautonomous stochastic search for a global minimum
Institute of Mathematics, Jagiellonian University, Cracow, Poland
Published at: 05.06.2012
Article status: Open
Licence: None
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