The DOI matches its Crossref record.
Barycenters in the Wasserstein Space
Open accessAbstract
In this paper, we introduce a notion of barycenter in the Wasserstein space which generalizes McCann's interpolation to the case of more than two measures. We provide existence, uniqueness, characterizations, and regularity of the barycenter and relate it to the multimarginal optimal transport problem considered by Gangbo and Święch in [Comm. Pure Appl. Math., 51 (1998), pp. 23–45]. We also consider some examples and, in particular, rigorously solve the Gaussian case. We finally discuss convexity of functionals in the Wasserstein space.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Computational Optimal Transport
Peyré, G., & Cuturi, M. (2019) · now publishers, Inc. eBooks · doi:10.1561/9781680835519 · Cited by 847
Convolutional wasserstein distances: efficient optimal transportation on geometric domains
Solomon, J., de Goes, F., Peyré, G., Cuturi, M., Butscher, A., Nguyen, A., Du, T., & Guibas, L. (2015) · ACM Transactions on Graphics · doi:10.1145/2766963 · Cited by 481
Scaling algorithms for unbalanced optimal transport problems
Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F.-X. (2018) · Mathematics of Computation · doi:10.1090/mcom/3303 · Cited by 336
Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain
Hao, J., Yang, T., Tang, H., Bai, C., Liu, J., Meng, Z., Liu, P., & Wang, Z. (2021) · arXiv · doi:10.48550/arxiv.2109.06668 · Cited by 192
Fair Regression with Wasserstein Barycenters
Chzhen, E., Denis, C., Hebiri, M., Oneto, L., & Pontil, M. (2020) · arXiv · doi:10.48550/arxiv.2006.07286 · Cited by 46
“The next result is standard and can be found for instance in [2, Section 6.1] or [38, Section 5.5.5].”
BackgroundMethodInfluential
Metadata
- Authors
- Agueh, M., & Carlier, G.
- Venue
- SIAM Journal on Mathematical Analysis
- Published
- 2011-01
- Volume
- 43
- Issue
- 2
- Pages
- 904-924
- ISSN
- 0036-1410 (ISSN-L)
- 1095-7154
- OpenAlex
- W1968333723
- Cited by
- 749
- References
- 0
- FWCI
- 17.49
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Barycenters in the Wasserstein Space. https://app.cochat.ai/papers/10.1137/100805741
- The paper
- Agueh, M., & Carlier, G. (2011). Barycenters in the Wasserstein Space. SIAM Journal on Mathematical Analysis. https://doi.org/10.1137/100805741
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.