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112 results on '"Kolonko, Michael"'

1. Accelerating the Visualization of Gaps and Overlaps for Large and Dynamic Sphere Packings with Bounding Volume Hierarchies

Author

Gu, Feng, Yang, Zhixing, Kolonko, Michael, Grosch, Thorsten, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Gunkelmann, Nina, editor, and Baum, Marcus, editor

Published
2020
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2. Improving the Quality of Random Number Generators by Applying a Simple Ratio Transformation

Author

Kolonko, Michael, Wu, Zijun, and Gu, Feng

Subjects
Computer Science - Other Computer Science
Abstract

It is well-known that the quality of random number generators can often be improved by combining several generators, e.g. by summing or subtracting their results. In this paper we investigate the ratio of two random number generators as an alternative approach: the smaller of two input random numbers is divided by the larger, resulting in a rational number from $[0,1]$. We investigate theoretical properties of this approach and show that it yields a good approximation to the ideal uniform distribution. To evaluate the empirical properties we use the well-known test suite \textsc{TestU01}. We apply the ratio transformation to moderately bad generators, i.e. those that failed up to 40\% of the tests from the test battery \textsc{Crush} of \textsc{TestU01}. We show that more than half of them turn into very good generators that pass all tests of \textsc{Crush} and \textsc{BigCrush} from \textsc{TestU01} when the ratio transformation is applied. In particular, generators based on linear operations seem to benefit from the ratio, as this breaks up some of the unwanted regularities in the input sequences. Thus the additional effort to produce a second random number and to calculate the ratio allows to increase the quality of available random number generators., Comment: 18 pages

Published
2016

3. Ranking and Selection: A New Sequential Bayesian Procedure for Use with Common Random Numbers

Author

Görder, Björn and Kolonko, Michael

Subjects
Statistics - Methodology, 62F07, 62F15, 11K45
Abstract

We want to select the best systems out of a given set of systems (or rank them) with respect to their expected performance. The systems allow random observations only and we assume that the joint observation of the systems has a multivariate normal distribution with unknown mean and covariance. We allow dependent marginal observations as they occur when common random numbers are used for the simulation of the systems. In particular, we focus on positively dependent observations as they might be expected in heuristic optimization where `systems' are different solutions to an optimization problem with common random inputs. In each iteration, we allocate a fixed budget of simulation runs to the solutions. We use a Bayesian setup and allocate the simulation effort according to the posterior covariances of the solutions until the ranking and selection decision is correct with a given high probability. Here, the complex posterior distributions are approximated only but we give extensive empirical evidence that the observed error probabilities are well below the given bounds in most cases. We also use a generalized scheme for the target of the ranking and selection that allows to bound the error probabilities with a Bonferroni approach. Our test results show that our procedure uses less simulations than comparable procedures from literature even in most of the cases where the observations are not positively correlated., Comment: 28 pages, 11 figures, extended discussion of literature, improved arguments in section 2.5 on approximate distribution, extended empirical comparisons

Published
2014

4. Accelerated Simulation of Sphere Packings Using Parallel Hardware

Author

Yang, Zhixing, Gu, Feng, Grosch, Thorsten, Kolonko, Michael, Barbosa, Simone Diniz Junqueira, Series Editor, Filipe, Joaquim, Series Editor, Kotenko, Igor, Series Editor, Sivalingam, Krishna M., Series Editor, Washio, Takashi, Series Editor, Yuan, Junsong, Series Editor, Zhou, Lizhu, Series Editor, Baum, Marcus, editor, Brenner, Gunther, editor, Grabowski, Jens, editor, Hanschke, Thomas, editor, Hartmann, Stefan, editor, and Schöbel, Anita, editor

Published
2018
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25. Ranking and Selection: A New Sequential Bayesian Procedure for Use with Common Random Numbers

Author

G��rder, Bj��rn and Kolonko, Michael

Subjects
Methodology (stat.ME), FOS: Computer and information sciences, 62F07, 62F15, 11K45
Abstract

We want to select the best systems out of a given set of systems (or rank them) with respect to their expected performance. The systems allow random observations only and we assume that the joint observation of the systems has a multivariate normal distribution with unknown mean and covariance. We allow dependent marginal observations as they occur when common random numbers are used for the simulation of the systems. In particular, we focus on positively dependent observations as they might be expected in heuristic optimization where `systems' are different solutions to an optimization problem with common random inputs. In each iteration, we allocate a fixed budget of simulation runs to the solutions. We use a Bayesian setup and allocate the simulation effort according to the posterior covariances of the solutions until the ranking and selection decision is correct with a given high probability. Here, the complex posterior distributions are approximated only but we give extensive empirical evidence that the observed error probabilities are well below the given bounds in most cases. We also use a generalized scheme for the target of the ranking and selection that allows to bound the error probabilities with a Bonferroni approach. Our test results show that our procedure uses less simulations than comparable procedures from literature even in most of the cases where the observations are not positively correlated., 28 pages, 11 figures, extended discussion of literature, improved arguments in section 2.5 on approximate distribution, extended empirical comparisons

Published
2014
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33. Optimales Landebahnmanagement.

Author

Kolonko, Michael

Abstract

Copyright of Stochastische Simulation is the property of Springer Nature / Books and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2008
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34. Varianzreduktion.

Author

Kolonko, Michael

Abstract

Copyright of Stochastische Simulation is the property of Springer Nature / Books and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2008
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35. BackMatter.

Author

Kolonko, Michael

Published
2008

36. Konfidenzintervall und Stichprobenumfang bei Mittelwertschätzungen.

Author

Kolonko, Michael

Abstract

Copyright of Stochastische Simulation is the property of Springer Nature / Books and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2008
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37. Regenerative Simulation.

Author

Kolonko, Michael

Abstract

Copyright of Stochastische Simulation is the property of Springer Nature / Books and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2008
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38. Beispiele und Anwendungen.

Author

Kolonko, Michael

Abstract

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Published
2008
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39. Ziehen einer zufälligen Stichprobe.

Author

Kolonko, Michael

Abstract

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Published
2008
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40. Simulation von Gleichverteilungen auf Flächen.

Author

Kolonko, Michael

Abstract

Copyright of Stochastische Simulation is the property of Springer Nature / Books and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2008
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41. Simulationsverfahren für einige Standardverteilungen.

Author

Kolonko, Michael

Abstract

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Published
2008
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42. Tabellen-Nachschlagemethoden.

Author

Kolonko, Michael

Abstract

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Published
2008
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43. Kompositionsmethode.

Author

Kolonko, Michael

Abstract

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Published
2008
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44. Annahme-Verwerfungsmethode.

Author

Kolonko, Michael

Abstract

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Published
2008
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45. Inversionsmethode.

Author

Kolonko, Michael

Abstract

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Published
2008
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46. Statistische Gütekriterien.

Author

Kolonko, Michael

Abstract

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Published
2008
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47. Analytische Gütekriterien.

Author

Kolonko, Michael

Abstract

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Published
2008
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48. Weitere Zufallsgeneratoren.

Author

Kolonko, Michael

Abstract

Copyright of Stochastische Simulation is the property of Springer Nature / Books and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2008
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49. Zufallsgeneratoren mit dem Modulus 2.

Author

Kolonko, Michael

Abstract

Copyright of Stochastische Simulation is the property of Springer Nature / Books and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2008
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50. Lineare Kongruenzgeneratoren.

Author

Kolonko, Michael

Abstract

Copyright of Stochastische Simulation is the property of Springer Nature / Books and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2008
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