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A new class of costs for optimal transport planning

Part of: Classical measure theory Miscellaneous topics in calculus of variations and optimal control Numerical methods in calculus of variations and optimal control

Published online by Cambridge University Press:  29 November 2018

J.-J. ALIBERT ,
G. BOUCHITTÉ Open the ORCID record for G. BOUCHITTÉ [Opens in a new window]  and
T. CHAMPION
J.-J. ALIBERT
Affiliation:
Laboratoire IMATH, Université de Toulon, 83957 La Garde Cedex, France e-mails: [email protected]; [email protected]; [email protected]
G. BOUCHITTÉ*
Affiliation:
Laboratoire IMATH, Université de Toulon, 83957 La Garde Cedex, France e-mails: [email protected]; [email protected]; [email protected]
T. CHAMPION
Affiliation:
Laboratoire IMATH, Université de Toulon, 83957 La Garde Cedex, France e-mails: [email protected]; [email protected]; [email protected]
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Abstract

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We study a class of optimal transport planning problems where the reference cost involves a non-linear function G(x, p) representing the transport cost between the Dirac measure δx and a target probability p. This allows to consider interesting models which favour multi-valued transport maps in contrast with the classical linear case ($G(x,p)=\int c(x,y)dp$) where finding single-valued optimal transport is a key issue. We present an existence result and a general duality principle which apply to many examples. Moreover, under a suitable subadditivity condition, we derive a Kantorovich–Rubinstein version of the dual problem allowing to show existence in some regular cases. We also consider the well studied case of Martingale transport and present some new perspectives for the existence of dual solutions in connection with Γ-convergence theory.

Keywords

Optimal transportKantorovich-Rubinstein dualityMartingale constraint

MSC classification

Primary: 28A33: Spaces of measures, convergence of measures
Secondary: 49N05: Linear optimal control problems 49M29: Methods involving duality 90C46: Optimality conditions, duality
Type
Papers
Information
European Journal of Applied Mathematics , Volume 30 , Special Issue 6: Applied Optimal Transport , December 2019 , pp. 1229 - 1263
Copyright
© Cambridge University Press 2018 

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