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Convergence Details About k-DPP Monte-Carlo Sampling for Large Graphs

Author

Listed:
  • Diala Wehbe

    (University of Lille
    Lebanese University)

  • Nicolas Wicker

    (University of Lille)

Abstract

This paper aims at making explicit the mixing time found by Anari et al. (2016) for k-DPP Monte-Carlo sampling when it is applied on large graphs. This yields a polynomial bound on the mixing time of the associated Markov chain under mild conditions on the eigenvalues of the Laplacian matrix when the number of edges grows.

Suggested Citation

  • Diala Wehbe & Nicolas Wicker, 2022. "Convergence Details About k-DPP Monte-Carlo Sampling for Large Graphs," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(1), pages 188-203, May.
    • Handle: RePEc:spr:sankhb:v:84:y:2022:i:1:d:10.1007_s13571-021-00258-x
    DOI: 10.1007/s13571-021-00258-x
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    References listed on IDEAS

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    1. Göbel, F. & Jagers, A. A., 1974. "Random walks on graphs," Stochastic Processes and their Applications, Elsevier, vol. 2(4), pages 311-336, October.
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