On the existence of Monge maps for the Gromov-Wasserstein problem
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Abstract (arXiv)
The Gromov--Wasserstein problem is a non-convex optimization problem over the polytope of transportation plans between two probability measures supported on two spaces, each equipped with a cost function evaluating similarities between points. Akin to the standard optimal transportation problem, it is natural to ask for conditions guaranteeing some structure on the optimizers, for instance if these are induced by a (Monge) map. We study this question in Euclidean spaces when the cost functions are either given by (i) inner products or (ii) squared distances, two standard choices in the literature. We establish the existence of an optimal map in case (i) and of an optimal 2-map (the union of the graphs of two maps) in case (ii), both under an absolute continuity condition on the source measure. Additionally, in case (ii) and in dimension one, we numerically design situations where optimizers of the Gromov--Wasserstein problem are 2-maps but are not maps. This suggests that our result cannot be improved in general for this cost. Still in dimension one, we additionally establish the optimality of monotone maps under some conditions on the measures, thereby giving insight on why such maps often appear to be optimal in numerical experiments.
Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Oct 2022.
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Citing papers per year: 2023: 3; 2024: 6; 2025: 0; 2026: 7.
16 papers.
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- 2024The Z-Gromov-Wasserstein Distancecited by 12
- 2023Uncovering Challenges of Solving the Continuous Gromov-Wasserstein Problemcited by 6
- 2024On Probabilistic Embeddings in Optimal Dimension Reductioncited by 5
- 2023Monotone Generative Modeling via a Gromov-Monge Embeddingcited by 3
- 2024Disentangled Representation Learning with the Gromov-Monge Gapcited by 3
- 2024Improving Hyperbolic Representations via Gromov-Wasserstein Regularizationcited by 2
- 2023Leveraging Optimal Transport via Projections on Subspaces for Machine Learning Applicationscited by 1
- 2024Linear Partial Gromov-Wasserstein Embeddingcited by 1
- 2026Sliced Inner Product Gromov-Wasserstein Distancescited by 1
- 2024Strongly Isomorphic Neural Optimal Transport Across Incomparable Spacescited by 0
- 2026On sparsity, extremal structure, and monotonicity properties of Wasserstein and Gromov-Wasserstein optimal transport planscited by 0
- 2026Min Generalized Sliced Gromov Wasserstein: A Scalable Path to Gromov Wassersteincited by 0
4 more
- 2026Convergence of empirical subgradients for optimal transport-based objectivescited by 0
- 2026Cluster-Aware Matching via Laplacian Optimal Transportcited by 0
- 2026Gromov-Wasserstein Quantization and Clustering: Structure, Rates, and Algorithmscited by 0
- 2026Optimal Transport for Network Comparison: A Unified Review with New Spectral Bounds and Machine Learning Applicationscited by 0
- 2026Optimal Transport for Network Comparison: A Unified Review with New Spectral Bounds and Machine Learning Applications27 Aug 2026
- 2026Gromov-Wasserstein Quantization and Clustering: Structure, Rates, and Algorithms11 Aug 2026
- 2026Cluster-Aware Matching via Laplacian Optimal Transport17 Jul 2026
- 2026Convergence of empirical subgradients for optimal transport-based objectives27 May 2026
- 2026Min Generalized Sliced Gromov Wasserstein: A Scalable Path to Gromov Wasserstein13 May 2026
- 2026Sliced Inner Product Gromov-Wasserstein Distances8 May 2026
- 2026On sparsity, extremal structure, and monotonicity properties of Wasserstein and Gromov-Wasserstein optimal transport plans18 Feb 2026
- 2024Linear Partial Gromov-Wasserstein Embedding22 Oct 2024
- 2024The Z-Gromov-Wasserstein Distance15 Aug 2024
- 2024On Probabilistic Embeddings in Optimal Dimension Reduction5 Aug 2024
- 2024Strongly Isomorphic Neural Optimal Transport Across Incomparable Spaces20 Jul 2024
- 2024Improving Hyperbolic Representations via Gromov-Wasserstein Regularization15 Jul 2024
4 more
- 2024Disentangled Representation Learning with the Gromov-Monge Gap10 Jul 2024
- 2023Leveraging Optimal Transport via Projections on Subspaces for Machine Learning Applications23 Nov 2023
- 2023Monotone Generative Modeling via a Gromov-Monge Embedding2 Nov 2023
- 2023Uncovering Challenges of Solving the Continuous Gromov-Wasserstein Problem10 Mar 2023
- 2024The Z-Gromov-Wasserstein Distancecited by 12
- 2023Uncovering Challenges of Solving the Continuous Gromov-Wasserstein Problemcited by 6
- 2024On Probabilistic Embeddings in Optimal Dimension Reductioncited by 5
- 2023Monotone Generative Modeling via a Gromov-Monge Embeddingcited by 3
- 2024Disentangled Representation Learning with the Gromov-Monge Gapcited by 3
- 2024Improving Hyperbolic Representations via Gromov-Wasserstein Regularizationcited by 2
- 2023Leveraging Optimal Transport via Projections on Subspaces for Machine Learning Applicationscited by 1
- 2024Linear Partial Gromov-Wasserstein Embeddingcited by 1
- 2026Sliced Inner Product Gromov-Wasserstein Distancescited by 1
- 2024Strongly Isomorphic Neural Optimal Transport Across Incomparable Spacescited by 0
- 2026On sparsity, extremal structure, and monotonicity properties of Wasserstein and Gromov-Wasserstein optimal transport planscited by 0
- 2026Min Generalized Sliced Gromov Wasserstein: A Scalable Path to Gromov Wassersteincited by 0
12 most cited of 16 All 16 (JSON)
- 2026Optimal Transport for Network Comparison: A Unified Review with New Spectral Bounds and Machine Learning Applications27 Aug 2026
- 2026Gromov-Wasserstein Quantization and Clustering: Structure, Rates, and Algorithms11 Aug 2026
- 2026Cluster-Aware Matching via Laplacian Optimal Transport17 Jul 2026
- 2026Convergence of empirical subgradients for optimal transport-based objectives27 May 2026
- 2026Min Generalized Sliced Gromov Wasserstein: A Scalable Path to Gromov Wasserstein13 May 2026
- 2026Sliced Inner Product Gromov-Wasserstein Distances8 May 2026
- 2026On sparsity, extremal structure, and monotonicity properties of Wasserstein and Gromov-Wasserstein optimal transport plans18 Feb 2026
- 2024Linear Partial Gromov-Wasserstein Embedding22 Oct 2024
- 2024The Z-Gromov-Wasserstein Distance15 Aug 2024
- 2024On Probabilistic Embeddings in Optimal Dimension Reduction5 Aug 2024
- 2024Strongly Isomorphic Neural Optimal Transport Across Incomparable Spaces20 Jul 2024
- 2024Improving Hyperbolic Representations via Gromov-Wasserstein Regularization15 Jul 2024
12 newest of 16 All 16 (JSON)