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Maxima and near-maxima of a Gaussian random assignment field

Author

Listed:
  • Mordant, Gilles

    (Université catholique de Louvain, LIDAM/ISBA, Belgium)

  • Segers, Johan

    (Université catholique de Louvain, LIDAM/ISBA, Belgium)

Abstract

The assumption that the elements of the cost matrix in the classical assignment problem are drawn independently from a standard Gaussian distribution motivates the study of a particular Gaussian field indexed by the symmetric permutation group. The correlation structure of the field is determined by the Hamming distance between two permutations. The expectation of the maximum of the field is shown to go to infinity in the same way as if all variables of the field were independent. However, the variance of the maximum is shown to converge to zero at a rate which is slower than under independence, as the variance cannot be smaller than the one of the cost of the average assignment. Still, the convergence to zero of the variance means that the maximum possesses a property known as superconcentration. Finally, the dimension of the set of near-optimal assignments is shown to converge to zero.

Suggested Citation

  • Mordant, Gilles & Segers, Johan, 2021. "Maxima and near-maxima of a Gaussian random assignment field," LIDAM Discussion Papers ISBA 2021008, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvad:2021008
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    References listed on IDEAS

    as
    1. Krokhmal, Pavlo A. & Pardalos, Panos M., 2009. "Random assignment problems," European Journal of Operational Research, Elsevier, vol. 194(1), pages 1-17, April.
    2. Tanguy, Kevin, 2015. "Some superconcentration inequalities for extrema of stationary Gaussian processes," Statistics & Probability Letters, Elsevier, vol. 106(C), pages 239-246.
    3. Mittal, Y. & Ylvisaker, D., 1975. "Limit distributions for the maxima of stationary Gaussian processes," Stochastic Processes and their Applications, Elsevier, vol. 3(1), pages 1-18, January.
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    Cited by:

    1. Lifshits, M.A. & Tadevosian, A.A., 2022. "On the maximum of random assignment process," Statistics & Probability Letters, Elsevier, vol. 187(C).

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