LATIN HYPERCUBE AND IMPORTANCE SAMPLING ALGORITHMS FOR MULTIDIMENSIONAL INTEGRALS
LATIN HYPERCUBE AND IMPORTANCE SAMPLING ALGORITHMS FOR MULTIDIMENSIONAL INTEGRALS
DOI:
https://doi.org/10.46687/jsar.v10i1.201Keywords:
Monte Carlo algorithms, multidimensional integrals, Latin hypercube sampling, Importance samplingAbstract
Monte Carlo method is the only viable method for high-dimensional problems since its convergence is independent of the dimension. In this paper we implement and analyze the computational complexity of the Latin hypercube sampling algorithm. We compare the results with Importance sampling algorithm which is the most widely used variance reduction Monte Carlo method. We show that the Latin hypercube sampling has some advantageous over the importance sampling technique.
Author Biographies
Venelin Todorov, Bulgarian Academy of Sciences, Institute of Mathematics and Informatics and Institute of Inormation and Communication Technologies: Sofia, BG
Bulgarian Academy of Sciences, Institute of Mathematics and Informatics and Institute of Inormation and Communication Technologies: Sofia, BG
https://orcid.org/0000-0001-7134-5901Valerij Dzurov, ROUSSE UNIVERSITY ”ANGEL KANCHEV”
ROUSSE UNIVERSITY ”ANGEL KANCHEV”
Petar Stojanov, ROUSSE UNIVERSITY ”ANGEL KANCHEV”
ROUSSE UNIVERSITY ”ANGEL KANCHEV”
Angel Angelov, ROUSSE UNIVERSITY ”ANGEL KANCHEV”
ROUSSE UNIVERSITY ”ANGEL KANCHEV”
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Published
18.03.2023
How to Cite
Todorov, V. ., Dzurov, V. ., Stojanov, P. ., & Angelov, A. . (2023). LATIN HYPERCUBE AND IMPORTANCE SAMPLING ALGORITHMS FOR MULTIDIMENSIONAL INTEGRALS: LATIN HYPERCUBE AND IMPORTANCE SAMPLING ALGORITHMS FOR MULTIDIMENSIONAL INTEGRALS. JOURNAL SCIENTIFIC AND APPLIED RESEARCH, 10(1), 17–23. https://doi.org/10.46687/jsar.v10i1.201
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Technical Sciences
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