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Computational Complexity Of Discrete Optimization Problems

Author

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  • Lenstra, J. K.
  • Rinnooy Kan, A. H. G.

Abstract

Recent developments in the theory of computational complexity as applied to combinatorial problems have revealed the existence of a large class of so-called NP-complete problems, either all or none of which are solvable in polynomial time. Since many infamous combinatorial problems have been proved to be NP-complete, the latter alternative seems far more likely. In that sense, NP-completeness of a problem justifies the use of enumerative optimization methods and of approximation algorithms. In this paper we give an informal introduction to the theory of NP-completeness and derive some fundamental results, in the hope of stimulating further use of this valuable analytical tool.

Suggested Citation

  • Lenstra, J. K. & Rinnooy Kan, A. H. G., 1977. "Computational Complexity Of Discrete Optimization Problems," Econometric Institute Archives 272162, Erasmus University Rotterdam.
    • Handle: RePEc:ags:eureia:272162
    DOI: 10.22004/ag.econ.272162
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    Cited by:

    1. Kailiang Xu & Zuren Feng & Liangjun Ke, 2010. "A branch and bound algorithm for scheduling jobs with controllable processing times on a single machine to meet due dates," Annals of Operations Research, Springer, vol. 181(1), pages 303-324, December.

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