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Smoothed Analysis

Motivation and Discrete Models

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Algorithms and Data Structures (WADS 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2748))

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Abstract

In smoothed analysis, one measures the complexity of algorithms assuming that their inputs are subject to small amounts of random noise. In an earlier work (Spielman and Teng, 2001), we introduced this analysis to explain the good practical behavior of the simplex algorithm. In this paper, we provide further motivation for the smoothed analysis of algorithms, and develop models of noise suitable for analyzing the behavior of discrete algorithms. We then consider the smoothed complexities of testing some simple graph properties in these models.

The first author was supported in part by NSF grant CCR-0112487, and the second author was supported in part by NSF grant 99-72532

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Author information

Authors and Affiliations

  1. Department of Mathematics, Massachusetts Institute of Technology,  

    Daniel A. Spielman

  2. Department of Computer Science, Boston University,  

    Shang-Hua Teng

Authors
  1. Daniel A. Spielman
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  2. Shang-Hua Teng
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Editor information

Editors and Affiliations

  1. Carleton University, Ottawa, Canada

    Frank Dehne

  2. School of Computer Science, Carleton University, 1125 Colonel By Drive, K1S 5B6, Ottawa, Canada

    Jörg-Rüdiger Sack

  3. School of Computer Science, Carleton University, K1S 5B6, Ottawa, ON, Canada

    Michiel Smid

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© 2003 Springer-Verlag Berlin Heidelberg

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Spielman, D.A., Teng, SH. (2003). Smoothed Analysis. In: Dehne, F., Sack, JR., Smid, M. (eds) Algorithms and Data Structures. WADS 2003. Lecture Notes in Computer Science, vol 2748. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45078-8_23

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  • DOI: https://doi.org/10.1007/978-3-540-45078-8_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40545-0

  • Online ISBN: 978-3-540-45078-8

  • eBook Packages: Springer Book Archive

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