<scp>SISTA</scp>: Learning Optimal Transport Costs under Sparsity Constraints

In this paper, we describe a novel iterative procedure called SISTA to learn the underlying cost in optimal transport problems. is hybrid between two classical methods, coordinate descent (“S”-inkhorn) and proximal gradient (“ISTA”). It alternates phase of exact minimization over potentials parameters cost. We prove that method converges linearly, illustrate on simulated examples it significantly faster than both ISTA. apply estimating model migration, which predicts flow migrants using country-specific characteristics pairwise measures dissimilarity countries. This application demonstrates effectiveness machine learning quantitative social sciences. © 2022 Wiley Periodicals LLC.