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Smoothed Analysis

Motivation and Discrete Models
  • Daniel A. Spielman
  • Shang-Hua Teng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2748)

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.

Keywords

Condition Number Random Graph Query Complexity Property Testing Graph Property 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Daniel A. Spielman
    • 1
  • Shang-Hua Teng
    • 2
  1. 1.Department of MathematicsMassachusetts Institute of Technology 
  2. 2.Department of Computer ScienceBoston University 

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