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Importance Separation for Solving Integral Equations

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Large-Scale Scientific Computing (LSSC 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2907))

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Abstract

In this paper we describe and study importance separation Monte Carlo method for integral equations. Based on known results for integrals, we extend this method for solving integral equations. The method combines the idea of separation of the domain into uniformly small subdomains (adaptive technique) with the Kahn approach of importance sampling. We analyze the error and compare the results with the crude Monte Carlo method.

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Author information

Authors and Affiliations

  1. CLPP – Bulgarian Academy of Sciences, Acad. G. Bonchev, Bl.25A, 1113, Sofia, Bulgaria

    Rayna Georgieva & Sofiya Ivanovska

Authors
  1. Rayna Georgieva
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  2. Sofiya Ivanovska
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Editor information

Editors and Affiliations

  1. Institute for Parallel Processing, Bulgarian Academy of Sciences, Acad. G. Bonchev, Bl. 25A, 1113, Sofia, Bulgaria

    Ivan Lirkov

  2. Institute for Parallel Processing, Bulgarian Academy of Sciences, Acad. G. Bonchev Str. Bl. 25-A, 1113, Sofia, Bulgaria

    Svetozar Margenov

  3. Informatics & Mathematical Modeling, Technical University of Denmark, DK-2800, Lyngby, Denmark

    Jerzy Waśniewski

  4. Center of Applied Mathematics and Informatics, University of Rousse, 7017, Rousse, Bulgaria

    Plamen Yalamov

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© 2004 Springer-Verlag Berlin Heidelberg

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Georgieva, R., Ivanovska, S. (2004). Importance Separation for Solving Integral Equations. In: Lirkov, I., Margenov, S., Waśniewski, J., Yalamov, P. (eds) Large-Scale Scientific Computing. LSSC 2003. Lecture Notes in Computer Science, vol 2907. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24588-9_15

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  • DOI: https://doi.org/10.1007/978-3-540-24588-9_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-21090-0

  • Online ISBN: 978-3-540-24588-9

  • eBook Packages: Springer Book Archive

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