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09511 Executive Summary – Parameterized complexity and approximation algorithms

Authors Erik D. Demaine, MohammadTaghi Hajiaghayi, Dániel Marx

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  • Parameterized complexity
  • Approximation algorithms

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Abstract

Many of the computational problems that arise in practice are optimization
problems: the task is to find a solution where the cost, quality, size,
profit, or some other measure is as large or small as possible. The
NP-hardness of an optimization problem implies that, unless P = NP, there is
no polynomial-time algorithm that finds the exact value of the optimum.
Various approaches have been proposed in the literature to cope with NP-hard
problems.  When designing approximation algorithms, we relax the requirement
that the algorithm produces an optimum solution, and our aim is to devise a
polynomial-time algorithm such that the solution it produces is not
necessarily optimal, but there is some worst-case bound on the solution
quality.

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Erik D. Demaine, MohammadTaghi Hajiaghayi, and Dániel Marx. 09511 Executive Summary – Parameterized complexity and approximation algorithms. In Parameterized complexity and approximation algorithms. Dagstuhl Seminar Proceedings, Volume 9511, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010) https://doi.org/10.4230/DagSemProc.09511.2

Author Details

Erik D. Demaine
MohammadTaghi Hajiaghayi
Dániel Marx
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