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National Repository of Grey Literature 2 records found  Search took 0.00 seconds. 
Penalizační metody ve stochastické optimalizaci
 Kálosi, Szilárd ; Branda, Martin (advisor) ; Kaňková, Vlasta (referee)
The submitted thesis studies penalty function methods for stochastic programming problems. The main objective of the paper is to examine penalty function methods for deterministic nonlinear programming, in particular exact penalty function methods, in order to enhance penalty function methods for stochastic programming. For this purpose, the equivalence of the original de- terministic nonlinear and the corresponding penalty function problem using arbi- trary vector norm as the penalty function is shown for convex and invex functions occurring in the problems, respectively. The obtained theorems are consequently applied to multiple chance constrained problems under finite discrete probability distribution to show the asymptotic equivalence of the probabilistic and the cor- responding penalty function problems. The practical use of the newly obtained methods is demonstrated on a numerical study, in which a comparison with other approaches is provided as well. 1
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Asymptotické vlastnosti intervalových statistik
 Vajda, Igor ; van der Meulen, E. C.
This is a continuation of our previous paper dealing with simple spacings. Here we deal with arbitrary m-spacings. We introduce new spacings statistics measuring divergence of hypothetical and empirical distributions. It is proved that they are asymptotically equivalent with all spacings statistics known from the literature. General asymptotic equivalence of this type is a new result with interesting applications.
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