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Computational Complexity of Some Maximum Average Weight Problems with Precedence Constraints

Author

Listed:
  • Ulrich Faigle

    (University of Twente, Enschede, The Netherlands)

  • Walter Kern

    (University of Twente, Enschede, The Netherlands)

Abstract

Maximum average weight ideal problems in ordered sets arise from modeling variants of the investment problem and, in particular, learning problems in the context of concepts with tree-structured attributes in artificial intelligence. Similarly, trying to construct tests with high reliability leads to a nontrivial maximum average weight ideal problem. This paper investigates the computational complexity and shows that the general problem is NP-complete. Important special cases (e.g., finding rooted subtrees of maximal average weight), however, can be handled with efficient algorithms.

Suggested Citation

  • Ulrich Faigle & Walter Kern, 1994. "Computational Complexity of Some Maximum Average Weight Problems with Precedence Constraints," Operations Research, INFORMS, vol. 42(4), pages 688-693, August.
    • Handle: RePEc:inm:oropre:v:42:y:1994:i:4:p:688-693
    DOI: 10.1287/opre.42.4.688
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    Cited by:

    1. Montiel, Luis & Dimitrakopoulos, Roussos, 2015. "Optimizing mining complexes with multiple processing and transportation alternatives: An uncertainty-based approach," European Journal of Operational Research, Elsevier, vol. 247(1), pages 166-178.
    2. Alper Atamtürk & Muhong Zhang, 2007. "Two-Stage Robust Network Flow and Design Under Demand Uncertainty," Operations Research, INFORMS, vol. 55(4), pages 662-673, August.
    3. Corinna Gottschalk & Hendrik Lüthen & Britta Peis & Andreas Wierz, 2018. "Optimization problems with color-induced budget constraints," Journal of Combinatorial Optimization, Springer, vol. 36(3), pages 861-870, October.

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