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Linear Convergence of Batch Greenkhorn for Regularized Multimarginal Optimal Transport
Vladimir Kostic · Saverio Salzo · Massimiliano Pontil

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in Optimal Transport and Machine Learning »
Event URL: https://arxiv.org/pdf/2112.00838.pdf »

A prominent class of algorithms for solving regularized optimal transport problems is that of iterative Bregman projections (IBP). Among them, in the classical bi-marginal case, superior performance of Greenkhorn algorithm to Sinkhorn algorithm has been testified in several works. Here we prove global linear convergence of a new batch Greenkhorn algorithm for regularized multimarginal optimal transport, which processes at each iteration only a batch of components of a selected marginal. While the linear convergence is established for the famous example of cyclic IBP - the Sinkhorn algorithm, in general for IBP this question is open. Our framework of Batch Greenkhorn is general enough to cover, as special cases some existing algorithms in optimal transport like Greenkhorn algorithm for bi-marginal, and (greedy) MultiSinkhorn algorithm for multimarginal optimal transport, for which we provide explicit global linear convergence rates. Moreover, our results highlight the critical role played by the batch size in accelerating the convergence of the algorithm.

Author Information

Vladimir Kostic (Istituto Italiano di Tecnologia)
Saverio Salzo (Istituto Italiano di Tecnologia)
Massimiliano Pontil (IIT & UCL)

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