On properties of the Generalized Wasserstein distance
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Abstract (arXiv)
The Wasserstein distances Wp (p≥ 1), defined in terms of solution to the Monge-Kantorovich problem, are known to be a useful tool to investigate transport equations. In particular, the Benamou-Brenier formula characterizes the square of the Wasserstein distance W2 as the infimum of the kinetic energy, or action functional, of all vector fields moving one measure to the other. Another important property of the Wasserstein distances is the Kantorovich-Rubinstein duality stating the equality between the distance W1 and the supremum of the integrals of Lipschitz continuous functions with Lipschitz constant bounded by one. An intrinsic limitation of Wasserstein distances is the fact that they are defined only between measures having the same mass. To overcome such limitation, we recently introduced the generalized Wasserstein distances Wpa,b, defined in terms of both the classical Wasserstein distance Wp and the total variation (or L1) distance. Here p plays the same role as for the classic Wasserstein distance, while a and b are weights for the transport and the total variation term. In this paper we prove two important properties of the generalized Wasserstein distances: 1) a generalized Benamou-Brenier formula providing the equality between W2a,b and the supremum of an action functional, which includes a transport term (kinetic energy) and a source term. 2) a duality `a la Kantorovich-Rubinstein establishing the equality between W11,1 and the flat metric.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Apr 2013.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 3; 2024: 1; 2025: 3.
11 papers.
- 2023The emergence of clusters in self-attention dynamicscited by 133
- 2021Unbalanced minibatch Optimal Transport; applications to Domain Adaptationcited by 123
- 2022Sliced Optimal Partial Transportcited by 19
- 2021Entropy Partial Transport with Tree Metrics: Theory and Practicecited by 13
- 2023Scalable Unbalanced Sobolev Transport for Measures on a Graphcited by 10
- 2025Spectro-Riemannian Graph Neural Networkscited by 5
- 2025CurvGAD: Leveraging Curvature for Enhanced Graph Anomaly Detectioncited by 4
- 2023Law of Large Numbers for Bayesian two-layer Neural Network trained with Variational Inferencecited by 1
- 2024Neural varifolds: an aggregate representation for quantifying the geometry of point cloudscited by 1
- 2022On making optimal transport robust to all outlierscited by 0
- 2025An Efficient Orlicz-Sobolev Approach for Transporting Unbalanced Measures on a Graphcited by 0
- 2025CurvGAD: Leveraging Curvature for Enhanced Graph Anomaly Detection12 Feb 2025
- 2025An Efficient Orlicz-Sobolev Approach for Transporting Unbalanced Measures on a Graph2 Feb 2025
- 2025Spectro-Riemannian Graph Neural Networks1 Feb 2025
- 2024Neural varifolds: an aggregate representation for quantifying the geometry of point clouds5 Jul 2024
- 2023Law of Large Numbers for Bayesian two-layer Neural Network trained with Variational Inference10 Jul 2023
- 2023The emergence of clusters in self-attention dynamics9 May 2023
- 2023Scalable Unbalanced Sobolev Transport for Measures on a Graph24 Feb 2023
- 2022Sliced Optimal Partial Transport15 Dec 2022
- 2022On making optimal transport robust to all outliers23 Jun 2022
- 2021Unbalanced minibatch Optimal Transport; applications to Domain Adaptation5 Mar 2021
- 2021Entropy Partial Transport with Tree Metrics: Theory and Practice24 Jan 2021