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Efficient tensor bases for pairwise comparisons

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  • Konrad Ku{l}akowski
  • Ryszard Smarzewski

Abstract

In this study, we construct the first orthogonal basis for additively consistent subspace in pairwise comparisons theory. This construction is based on our representation of additively consistent best approximations of skew-symmetric matrices with respect to a tensor basis having minimal support. The orthogonal basis establishes the logarithmic consistent projection for the orthogonal windowing of pairwise comparisons matrices. It is compared with the windowing of the Saaty and SVD types. These comparisons resulted in new composite formulae for logarithmic, Saaty, and SVD projections. The theoretical considerations presented in the paper are accompanied by numerous examples.

Suggested Citation

  • Konrad Ku{l}akowski & Ryszard Smarzewski, 2026. "Efficient tensor bases for pairwise comparisons," Papers 2608.25923, arXiv.org.
  • Handle: RePEc:arx:papers:2608.25923
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    References listed on IDEAS

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    1. S I Gass & S M Standard, 2002. "Characteristics of positive reciprocal matrices in the analytic hierarchy process," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(12), pages 1385-1389, December.
    2. Liu, Fang & Zou, Shu-Cai & Li, Qing, 2020. "Deriving priorities from pairwise comparison matrices with a novel consistency index," Applied Mathematics and Computation, Elsevier, vol. 374(C).
    3. Ryszard Janicki, 2018. "Finding consistent weights assignment with combined pairwise comparisons," International Journal of Management and Decision Making, Inderscience Enterprises Ltd, vol. 17(3), pages 322-347.
    4. Benítez, Julio & Koczkodaj, Waldemar W. & Kowalczyk, Adam, 2024. "Computationally efficient orthogonalization for pairwise comparisons method," Applied Mathematics and Computation, Elsevier, vol. 473(C).
    5. Carl Eckart & Gale Young, 1936. "The approximation of one matrix by another of lower rank," Psychometrika, Springer;The Psychometric Society, vol. 1(3), pages 211-218, September.
    6. Rhonda Aull-Hyde & Kirsten Andrews Davis, 2012. "Military applications of the analytic hierarchy process," International Journal of Multicriteria Decision Making, Inderscience Enterprises Ltd, vol. 2(3), pages 267-281.
    7. Konrad Kułakowski & Jiri Mazurek & Michał Strada, 2022. "On the similarity between ranking vectors in the pairwise comparison method," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 73(9), pages 2080-2089, October.
    8. Ho, William & Ma, Xin, 2018. "The state-of-the-art integrations and applications of the analytic hierarchy process," European Journal of Operational Research, Elsevier, vol. 267(2), pages 399-414.
    9. Jean-Pierre Magnot & Jiří Mazurek & Viera Cernanova, 2021. "A gradient method for inconsistency reduction of pairwise comparisons matrices," Working Papers hal-03313878, HAL.
    10. Golany, B. & Kress, M., 1993. "A multicriteria evaluation of methods for obtaining weights from ratio-scale matrices," European Journal of Operational Research, Elsevier, vol. 69(2), pages 210-220, September.
    11. R. Blanquero & E. Carrizosa & E. Conde, 2006. "Inferring Efficient Weights from Pairwise Comparison Matrices," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 64(2), pages 271-284, October.
    12. Gass, S. I. & Rapcsak, T., 2004. "Singular value decomposition in AHP," European Journal of Operational Research, Elsevier, vol. 154(3), pages 573-584, May.
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