Exploitation of sensitivity derivatives via randomized quasi-Monte Carlo methods

Cao Y., Chi H., Milton C. and Zhao W.
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Cao Y.: Department of Mathematics and Statistics, Auburn University, AL 36830, USA. Email: caoy@scs.fsu.edu
Chi H.: Department of Computer and Information Sciences, Florida A&M University, Tallahassee, FL 32307-5100, USA. Email: hongmei.chi@famu.edu
Milton C.: Department of Mathematics, Florida A&M University, Tallahassee, FL 32307, USA.
Zhao W.: School of Mathematics and System Sciences, Shandong University, Jinan, Shandong, 250100, China. Email: wdzhao@sdu.edu.cn

Monte Carlo Methods and Applications, 2008, vol. 14, issue 3, 269-279

Abstract: Monte Carlo methods are now widely used in solving various computational fluid dynamics systems. This paper presents an improved scrambled quasi-Monte Carlo method for solving fluid dynamics applications. In our parallel implementation we use an independent scrambled quasirandom sequence for each processor. We explore the use of both scrambled quasi-Monte Carlo and variance reduction methods to improve the accuracy for Monte Carlo schemes. We also present theoretical analyses and numerical experiments to validate our numerical algorithms.

Keywords: Quasi-Monte Carlo; parallel computing; scrambled sequences; sensitivity derivatives; computational fluid dynamics (search for similar items in EconPapers)
Date: 2008
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DOI: 10.1515/MCMA.2008.011

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