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A Lower Bound on Approximation Algorithms for the Closest Substring Problem

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4616))

Abstract

The Closest Substring problem (CSP), where a short string is sought that minimizes the number of mismatches between it and each of a given set of strings, is a minimization problem with polynomial time approximation schemes. In this paper, a lower bound on approximation algorithms for the CSP problem is developed. We prove that unless the Exponential Time Hypothesis (ETH Hypothesis, i.e., not all search problems in SNP are solvable in subexponential time) fails, the CSP problem has no polynomial time approximation schemes of running time f(1/ε)|x|O(1/ ∈ ) for any function f, where |x| is the size of input instance. This essentially excludes the possibility that the CSP problem has a practical polynomial time approximation scheme even for moderate values of the error bound ε . As a consequence, it is unlikely that the study of approximation schemes for the CSP problem in the literature would lead to practical approximation algorithms for the CSP problem for small error bound ε.

This work is supported by the National Natural Science Foundation of China (60433020), the Program for New Century Excellent Talents in University (NCET-05-0683) and the Program for Changjiang Scholars and Innovative Research Team in University (IRT0661).

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Andreas Dress Yinfeng Xu Binhai Zhu

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© 2007 Springer-Verlag Berlin Heidelberg

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Wang, J., Huang, M., Chen, J. (2007). A Lower Bound on Approximation Algorithms for the Closest Substring Problem. In: Dress, A., Xu, Y., Zhu, B. (eds) Combinatorial Optimization and Applications. COCOA 2007. Lecture Notes in Computer Science, vol 4616. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73556-4_31

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  • DOI: https://doi.org/10.1007/978-3-540-73556-4_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73555-7

  • Online ISBN: 978-3-540-73556-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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