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Main Authors: González-Sanz, Alberto, Groppe, Michel, Munk, Axel
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2410.23146
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author González-Sanz, Alberto
Groppe, Michel
Munk, Axel
author_facet González-Sanz, Alberto
Groppe, Michel
Munk, Axel
contents The goal of optimal transport (OT) is to find optimal assignments or matchings between data sets which minimize the total cost for a given cost function. However, sometimes the cost function is unknown but we have access to (parts of) the solution to the OT problem, e.g.\ the OT plan or the value of the objective function. Recovering the cost from such information is called inverse OT and has become recently of certain interest triggered by novel applications, e.g.\ in social science and economics. This raises the issue under which circumstances such cost is identifiable, i.e., it can be uniquely recovered from other OT quantities. In this work we provide sufficient and necessary conditions for the identifiability of the cost function on finite ground spaces. We find that such conditions correspond to the combinatorial structure of the corresponding linear program and discuss its computational complexity and implications for cost estimation in statistical linear models.
format Preprint
id arxiv_https___arxiv_org_abs_2410_23146
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Identifiability and Exact Reconstruction of the Optimal Transport Cost on Finite Spaces
González-Sanz, Alberto
Groppe, Michel
Munk, Axel
Optimization and Control
90C08, 52B12, 15A29 (Primary) 62J07, 62F12 (Secondary)
The goal of optimal transport (OT) is to find optimal assignments or matchings between data sets which minimize the total cost for a given cost function. However, sometimes the cost function is unknown but we have access to (parts of) the solution to the OT problem, e.g.\ the OT plan or the value of the objective function. Recovering the cost from such information is called inverse OT and has become recently of certain interest triggered by novel applications, e.g.\ in social science and economics. This raises the issue under which circumstances such cost is identifiable, i.e., it can be uniquely recovered from other OT quantities. In this work we provide sufficient and necessary conditions for the identifiability of the cost function on finite ground spaces. We find that such conditions correspond to the combinatorial structure of the corresponding linear program and discuss its computational complexity and implications for cost estimation in statistical linear models.
title Identifiability and Exact Reconstruction of the Optimal Transport Cost on Finite Spaces
topic Optimization and Control
90C08, 52B12, 15A29 (Primary) 62J07, 62F12 (Secondary)
url https://arxiv.org/abs/2410.23146