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Lineare Optimierung und Approximationsalgorithmen

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In Abschnitt 6.2 hatten wir Max-SAT durch ein ganzzahliges Lineares Optimierungsproblem (ILP) beschrieben, dann eine Relaxierung dieses ILP gelöst und schließlich durch die Technik des Randomized Rounding eine zulässige Lösung bestimmt. Ganz ähnlich waren wir in Abschnitt 6.4 bei der Lösung von Max-CUT vorgegangen.

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7.6 Literatur zu Kapitel 7

  1. [AL96]
    S. Arora and C. Lund. Hardness of approximations. In D. S. Hochbaum, editor, Approximation Algorithms for NP-Hard Problems, pages 399–46. PWS, 1996.Google Scholar
  2. [FS02]
    U. Feige and G. Schechtman. On the optimality of the random hyperplane rounding technique for MAX CUT. Random Structures & Algorithms, 20:403–140, 2002.zbMATHCrossRefMathSciNetGoogle Scholar
  3. [GJ79]
    M. R. Garey and D. S. Johnson. Computers and Intractability — A Guide to the Theory of NP-Completeness. Freeman, New York, 1979.zbMATHGoogle Scholar
  4. [GW96]
    M. X. Goemans and D. P. Williamson. The primai-duai method for approximation algorithms and its application to network design methods. In D. S. Hochbaum, editor, Approximation Algorithms for NP-Hard Problems, pages 144–191. PWS, 1996.Google Scholar
  5. [Hoc96]
    D. Hochbaum. Approximating covering and packing problems: set cover, vertex cover, independent set, and related problems. In D. S. Hochbaum, editor, Approximation Algorithms for NP-Hard Problems, pages 94–143. PWS, 1996.Google Scholar
  6. [Knu97]
    D. E. Knuth. The Art of Computer Programming, Volume 1: Fundamental Algorithms. Addison-Wesley, Reading, Massachusetts, 3rd edition, 1997.Google Scholar
  7. [PS82]
    C. H. Papadimitriou and K. Steiglitz. Combinatorial Optimization: Algorithms and Complexity. Prentice-Hall, 1982.Google Scholar

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© B.G. Teubner Verlag / GWV Fachverlage GmbH, Wiesbaden 2006

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