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On the computation of Wasserstein barycenters

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  • Puccetti, Giovanni
  • Rüschendorf, Ludger
  • Vanduffel, Steven

Abstract

The Wasserstein barycenter is an important notion in the analysis of high dimensional data with a broad range of applications in applied probability, economics, statistics, and in particular to clustering and image processing. In this paper, we state a general version of the equivalence of the Wasserstein barycenter problem to the n-coupling problem. As a consequence, the coupling to the sum principle (characterizing solutions to the n-coupling problem) provides a novel criterion for the explicit characterization of barycenters. Based on this criterion, we provide as a main contribution the simple to implement iterative swapping algorithm (ISA) for computing barycenters. The ISA is a completely non-parametric algorithm which provides a sharp image of the support of the barycenter and has a quadratic time complexity which is comparable to other well established algorithms designed to compute barycenters. The algorithm can also be applied to more complex optimization problems like the k-barycenter problem.

Suggested Citation

  • Puccetti, Giovanni & Rüschendorf, Ludger & Vanduffel, Steven, 2020. "On the computation of Wasserstein barycenters," Journal of Multivariate Analysis, Elsevier, vol. 176(C).
    • Handle: RePEc:eee:jmvana:v:176:y:2020:i:c:s0047259x19302544
    DOI: 10.1016/j.jmva.2019.104581
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    References listed on IDEAS

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    1. Dowson, D. C. & Landau, B. V., 1982. "The Fréchet distance between multivariate normal distributions," Journal of Multivariate Analysis, Elsevier, vol. 12(3), pages 450-455, September.
    2. Rüschendorf, L. & Rachev, S. T., 1990. "A characterization of random variables with minimum L2-distance," Journal of Multivariate Analysis, Elsevier, vol. 32(1), pages 48-54, January.
    3. Ethan Anderes & Steffen Borgwardt & Jacob Miller, 2016. "Discrete Wasserstein barycenters: optimal transport for discrete data," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 84(2), pages 389-409, October.
    4. Rüschendorf, Ludger & Uckelmann, Ludger, 2002. "On the n-Coupling Problem," Journal of Multivariate Analysis, Elsevier, vol. 81(2), pages 242-258, May.
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    Cited by:

    1. Alessia Benevento & Fabrizio Durante, 2023. "Wasserstein Dissimilarity for Copula-Based Clustering of Time Series with Spatial Information," Mathematics, MDPI, vol. 12(1), pages 1-15, December.
    2. Johannes von Lindheim, 2023. "Simple approximative algorithms for free-support Wasserstein barycenters," Computational Optimization and Applications, Springer, vol. 85(1), pages 213-246, May.

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