Effective Method for Constructing Pseudo-Random Vectors Uniformly Distributed in Cone

Valery Reizlin; Anastasiya A. Nefedova

doi:10.4028/www.scientific.net/KEM.685.852

Paper Titles

Development of Intelligent Algorithm for Positioning a Vehicle on a Route
p.833

Simulation of Telephone System with Reserved Communicational Channels and Automatic Correction of Transmission’s Delay of the Audio Stream
p.838

Architecture of Enterprise Information Systems Based on the Semantic Web Technologies
p.843

Method of Evaluation of Social Network User Sentiments Based on Fuzzy Logic
p.847

Effective Method for Constructing Pseudo-Random Vectors Uniformly Distributed in Cone
p.852

Optimal Methods of Segmentation of Tomographic Volume Searching System: A Preliminary Review
p.857

Integration of Heterogeneous Company Information Resources Using Semantic Technologies
p.863

Distributed Information System for Processing and Storage of Meteorological Data
p.867

Content-Based Image Retrieval Using Color, Texture and Shape Features
p.872

HomeKey Engineering MaterialsKey Engineering Materials Vol. 685Effective Method for Constructing Pseudo-Random...

Effective Method for Constructing Pseudo-Random Vectors Uniformly Distributed in Cone

Abstract:

In this paper we propose an algorithm for efficient production of pseudo-random vectors uniformly distributed in an n-dimensional cone. The method shows higher efficiency compared to the process of elimination in the case of large dimensions. The results of computer experiments confirm the theoretical construct.

You might also be interested in these eBooks

High Technology: Research and Applications 2015

View Preview

Info:

Periodical:

Key Engineering Materials (Volume 685)

Pages:

852-856

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.685.852

Citation:

Cite this paper

Online since:

February 2016

Authors:

Valery Reizlin, Anastasiya A. Nefedova

Keywords:

Algorithm Efficiency, Compression Method, Elimination Method, Even Distribution, Pseudo-Random Vector, Rastrigin’s Director Cone Method

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] L.A. Rastrigin, Stohasticheskie metody poiska, Nauka, Moscow, (1968).

Google Scholar

[2] L.A. Rastrigin, Adaptacija slozhnyh system, Zinatne, Riga, (1981).

Google Scholar

[3] P. Stakhiv, I. Strubytska, Yu. Kozak, Parallelization of calculations using GPU in optimization approach for macromodels construction, Przegląd Elektrotechniczny (Electrical Review). 88, 3a (2012) 7-9.

Google Scholar

[4] L. Byczkowska-Lipińska, P. Stakhiv, Yu. Kozak, Acceleration of the parameters identification for dynamic models construction using parallelization, Computational Problems of Electrical Engineering. 2, 2 (2012) 7-12.

Google Scholar

[5] X. Zhu, J. Ding, H. Dong, K. Hu, X. Zhang, Normalized correlation-based quantization modulation for robust watermarking, IEEE Transactions on Multimedia. 16, 7 (2014) 1888-(1904).

DOI: 10.1109/tmm.2014.2340695

Google Scholar

[6] T. G. Kolda, R.M. Lewis, V. Torczon, Optimization by Direct Search: New Perspectives on Some Classical and Modern Methods, Siam Review. 45, 3 (2003) 385-482.

DOI: 10.1137/s003614450242889

Google Scholar

[7] I. Ye. Davydov, Using random search method for the task of modal formation of dynamic properties of the «carrier rocket – stabilization automaton» system, Vestnik Samarskogo Gosudarstvennogo Ajerokosmicheskogo Universiteta. 1 (2007) 31-36.

Google Scholar

[8] S.M. Ermakov, G.A. Mihajlov, Statisticheskoe Modelirovanie, Fizmatlit, Moscow, (1982).

Google Scholar