Quantitative stability in optimal transport for general power costs

Fuente: arXiv
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Autori principali: Mischler, Octave, Trevisan, Dario
Natura: Preprint
Pubblicazione: 2024
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author Mischler, Octave
Trevisan, Dario
author_facet Mischler, Octave
Trevisan, Dario
contents We establish novel quantitative stability results for optimal transport problems with respect to perturbations in the target measure. We provide explicit bounds on the stability of optimal transport potentials and maps, which are relevant for both theoretical and practical applications. Our results apply to a wide range of costs, including all Wasserstein distances with power cost exponent strictly larger than $1$ and leverage mostly assumptions on the source measure, such as log-concavity and bounded support. Our work provides a significant step forward in the understanding of stability of optimal transport problems, as previous results where mostly limited to the case of the quadratic cost.
format Preprint
id arxiv_https___arxiv_org_abs_2407_19337
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Quantitative stability in optimal transport for general power costs
Mischler, Octave
Trevisan, Dario
Functional Analysis
Optimization and Control
49Q22, 49K40
We establish novel quantitative stability results for optimal transport problems with respect to perturbations in the target measure. We provide explicit bounds on the stability of optimal transport potentials and maps, which are relevant for both theoretical and practical applications. Our results apply to a wide range of costs, including all Wasserstein distances with power cost exponent strictly larger than $1$ and leverage mostly assumptions on the source measure, such as log-concavity and bounded support. Our work provides a significant step forward in the understanding of stability of optimal transport problems, as previous results where mostly limited to the case of the quadratic cost.
title Quantitative stability in optimal transport for general power costs
topic Functional Analysis
Optimization and Control
49Q22, 49K40
url https://arxiv.org/abs/2407.19337