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.
Keywords
- Compression Function
- Heuristic Scheme
- Perturbation Model
- Disjoint Support
- Computational Complexity Theory
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.
Supported by DFG research grant BL 511/7-1. A full version of this paper is available at http://arxiv.org/abs/1202.1936
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Bläser, M., Manthey, B. (2012). Smoothed Complexity Theory. In: Rovan, B., Sassone, V., Widmayer, P. (eds) Mathematical Foundations of Computer Science 2012. MFCS 2012. Lecture Notes in Computer Science, vol 7464. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32589-2_20
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DOI: https://doi.org/10.1007/978-3-642-32589-2_20
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