Abstract
Smoothed analysis is a new way of analyzing algorithms introduced by Spielman and Teng (J. ACM, 2004). Classical methods like worst-case or average-case analysis have accompanying complexity classes, like P and Avg−P, respectively. While worst-case or average-case analysis give us a means to talk about the running time of a particular algorithm, complexity classes allows 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 results.
Original language | Undefined |
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Title of host publication | 37th International Symposium on Mathematical Foundations of Computer Science, MFCS 2012 |
Editors | B. Rovan, V. Sassone, P. Widmayer |
Place of Publication | New York |
Publisher | Springer |
Pages | 198-209 |
Number of pages | 12 |
ISBN (Print) | 978-3-642-32588-5 |
DOIs | |
Publication status | Published - 2012 |
Event | 37th International Symposium on Mathematical Foundations of Computer Science, MFCS 2012 - Bratislava, Slovakia Duration: 27 Aug 2012 → 31 Aug 2012 Conference number: 37 |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer Verlag |
Volume | 7464 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 37th International Symposium on Mathematical Foundations of Computer Science, MFCS 2012 |
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Abbreviated title | MFCS |
Country/Territory | Slovakia |
City | Bratislava |
Period | 27/08/12 → 31/08/12 |
Keywords
- METIS-289632
- IR-80994
- Computational Complexity
- Smoothed Analysis
- EWI-21536
- average-case complexity
- Complexity Theory