# SISTA: Learning Optimal Transport Costs Under Sparsity Constraints

@article{Carlier2020SISTALO, title={SISTA: Learning Optimal Transport Costs Under Sparsity Constraints}, author={G. Carlier and A. Dupuy and A. Galichon and Yifei Sun}, journal={IZA Institute of Labor Economics Discussion Paper Series}, year={2020} }

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… Expand

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