Coupling matrix manifolds assisted optimization for optimal transport problems


Optimal transport (OT) is a powerful tool for measuring the distance between two probability distributions. In this paper, we introduce a new manifold named as the coupling matrix manifold (CMM), where each point on this novel manifold can be regarded as a transportation plan of the optimal transport problem. We firstly explore the Riemannian geometry of CMM with the metric expressed by the Fisher information. These geometrical features can be exploited in many essential optimization methods as a framework solving all types of OT problems via incorporating numerical Riemannian optimization algorithms such as gradient descent and trust region algorithms in CMM manifold. The proposed approach is validated using several OT problems in comparison with recent state-of-the-art related works. For the classic OT problem and its entropy regularized variant, it is shown that our method is comparable with the classic algorithms such as linear programming and Sinkhorn algorithms. For other types of non-entropy regularized OT problems, our proposed method has shown superior performance to other works, whereby the geometric information of the OT feasible space was not incorporated within.

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  1. 1.

    For a matrix \({\mathbf{M}}\), \(\text {diag}({\mathbf{M}})\) is the vector formed by \({\mathbf{M}}\)’s diagonal elements. For a vector \({\mathbf{v}}\), the result of \(\text {diag}({\mathbf{v}})\) is the matrix whose diagonal elements come from \({\mathbf{v}}\).

  2. 2.

    We sincerely thanks to the authors of Courty et al. (2016) for providing us the complete simulated two moon datasets.

  3. 3.

    Both datasets can be found at

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This project is partially supported by the University of Sydney Business School ARC Bridging grant. The authors are graeteful to the anonymous reviewers for their constructive comments to improve this work.

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Correspondence to Junbin Gao.

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Shi, D., Gao, J., Hong, X. et al. Coupling matrix manifolds assisted optimization for optimal transport problems. Mach Learn 110, 533–558 (2021).

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  • Optimal transport
  • Doubly stochastic matrices
  • Coupling matrix manifold
  • Sinkhorn algorithm
  • Wasserstein distance
  • Entropy regularized optimal transport