Computational Complexity of Some Maximum Average Weight Problems with Precedence Constraints

Ulrich Faigle () and Walter Kern
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Walter Kern: University of Twente, Enschede, The Netherlands

Operations Research, 1994, vol. 42, issue 4, 688-693

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.

Keywords: analysis of algorithms: computational complexity; networks/graphs: networks arising from precedence constraints; programming: integer programming and dynamic programming (search for similar items in EconPapers)
Date: 1994
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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:42:y:1994:i:4:p:688-693

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