Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/236428 
Year of Publication: 
2021
Series/Report no.: 
IZA Discussion Papers No. 14397
Publisher: 
Institute of Labor Economics (IZA), Bonn
Abstract: 
In this paper, we describe a novel iterative procedure called SISTA to learn the underlying cost in optimal transport problems. SISTA is a hybrid between two classical methods, coordinate descent ("S"-inkhorn) and proximal gradient descent ("ISTA"). It alternates between a phase of exact minimization over the transport potentials and a phase of proximal gradient descent over the parameters of the transport cost. We prove that this method converges linearly, and we illustrate on simulated examples that it is significantly faster than both coordinate descent and ISTA. We apply it to estimating a model of migration, which predicts the flow of migrants using country-specific characteristics and pairwise measures of dissimilarity between countries. This application demonstrates the effectiveness of machine learning in quantitative social sciences.
Subjects: 
inverse optimal transport
coordinate descent
ISTA
JEL: 
C2
Document Type: 
Working Paper

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