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Smoothed Complexity Theory

Published: 11 May 2015 Publication History

Abstract

Smoothed analysis is a new way of analyzing algorithms introduced by Spielman and Teng. Classical methods like worst-case or average-case analysis have accompanying complexity classes, such as P and Avg-P, respectively. Whereas worst-case or average-case analysis give us a means to talk about the running time of a particular algorithm, complexity classes allow us to talk about the inherent difficulty of problems.
Smoothed analysis is a hybrid of worst-case and average-case analysis and compensates some of their drawbacks. Despite its success for the analysis of single algorithms and problems, there is no embedding of smoothed analysis into computational complexity theory, which is necessary to classify problems according to their intrinsic difficulty.
We propose a framework for smoothed complexity theory, define the relevant classes, and prove some first hardness results (of bounded halting and tiling) and tractability results (binary optimization problems, graph coloring, satisfiability) within this framework.

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cover image ACM Transactions on Computation Theory
ACM Transactions on Computation Theory  Volume 7, Issue 2
May 2015
101 pages
ISSN:1942-3454
EISSN:1942-3462
DOI:10.1145/2775140
Issue’s Table of Contents
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Publication History

Published: 11 May 2015
Accepted: 01 November 2014
Revised: 01 March 2014
Received: 01 March 2013
Published in TOCT Volume 7, Issue 2

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Author Tags

  1. Smoothed analysis
  2. average-case complexity
  3. computational complexity

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