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International Symposium on Scientific Computing, Computer Arithmetic, and Validated Numerics

SCAN 2015: Scientific Computing, Computer Arithmetic, and Validated Numerics pp 43–53Cite as

Numerical Probabilistic Approach for Optimization Problems

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9553))

Abstract

In the paper a new approach to optimization problems with random input parameters, which is defined as random programming, is discussed. This approach uses a numerical probability analysis and allows us to construct the set of solutions of an optimization problem based on the joint probability density function.

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References

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Author information

Authors and Affiliations

  1. Siberian Federal University, Krasnoyarsk, Russia

    Boris Dobronets & Olga Popova

Authors
  1. Boris Dobronets
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  2. Olga Popova
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Corresponding author

Correspondence to Boris Dobronets .

Editor information

Editors and Affiliations

  1. Institute of Computer Science, University of Würzburg, Würzburg, Germany

    Marco Nehmeier

  2. Universität Würzburg, Würzburg, Germany

    Jürgen Wolff von Gudenberg

  3. Department of Mathematics, Uppsala University, Uppsala, Sweden

    Warwick Tucker

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Dobronets, B., Popova, O. (2016). Numerical Probabilistic Approach for Optimization Problems. In: Nehmeier, M., Wolff von Gudenberg, J., Tucker, W. (eds) Scientific Computing, Computer Arithmetic, and Validated Numerics. SCAN 2015. Lecture Notes in Computer Science(), vol 9553. Springer, Cham. https://doi.org/10.1007/978-3-319-31769-4_4

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  • DOI: https://doi.org/10.1007/978-3-319-31769-4_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31768-7

  • Online ISBN: 978-3-319-31769-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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