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Derivations of large classes of facet defining inequalities of the weak order polytope using ranking structures

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  • Adolfo R. Escobedo

    (North Carolina State University)

  • Romena Yasmin

    (Arizona State University)

Abstract

Ordering polytopes have been instrumental to the study of combinatorial optimization problems arising in a variety of fields including comparative probability, computational social choice, and group decision-making. The weak order polytope is defined as the convex hull of the characteristic vectors of all binary orders on n alternatives that are reflexive, transitive, and total. By and large, facet defining inequalities (FDIs) of this polytope have been obtained through simple enumeration and through connections with other combinatorial polytopes. This paper derives five new large classes of FDIs by utilizing the equivalent representations of a weak order as a ranking of n alternatives that allows ties; this connection simplifies the construction of valid inequalities, and it enables groupings of characteristic vectors into useful structures. We demonstrate that a number of FDIs previously obtained through enumeration are actually special cases of the large classes. This work also introduces novel construction procedures for generating affinely independent members of the identified ranking structures. Additionally, it states two conjectures on how to derive many more large classes of FDIs using the featured techniques.

Suggested Citation

  • Adolfo R. Escobedo & Romena Yasmin, 2023. "Derivations of large classes of facet defining inequalities of the weak order polytope using ranking structures," Journal of Combinatorial Optimization, Springer, vol. 46(3), pages 1-45, October.
    • Handle: RePEc:spr:jcomop:v:46:y:2023:i:3:d:10.1007_s10878-023-01075-w
    DOI: 10.1007/s10878-023-01075-w
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    References listed on IDEAS

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    1. Pablo Coll & Javier Marenco & Isabel Méndez Díaz & Paula Zabala, 2002. "Facets of the Graph Coloring Polytope," Annals of Operations Research, Springer, vol. 116(1), pages 79-90, October.
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    5. Yoo, Yeawon & Escobedo, Adolfo R. & Skolfield, J. Kyle, 2020. "A new correlation coefficient for comparing and aggregating non-strict and incomplete rankings," European Journal of Operational Research, Elsevier, vol. 285(3), pages 1025-1041.
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