Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration

Jason Altschuler, Jonathan Weed, Philippe Rigollet

Research output: Contribution to journalConference articlepeer-review

Abstract

Computing optimal transport distances such as the earth mover's distance is a fundamental problem in machine learning, statistics, and computer vision. Despite the recent introduction of several algorithms with good empirical performance, it is unknown whether general optimal transport distances can be approximated in near-linear time. This paper demonstrates that this ambitious goal is in fact achieved by Cuturi's Sinkhorn Distances. This result relies on a new analysis of Sinkhorn iterations, which also directly suggests a new greedy coordinate descent algorithm GREENKHORN with the same theoretical guarantees. Numerical simulations illustrate that GREENKHORN significantly outperforms the classical SINKHORN algorithm in practice.

Original languageEnglish (US)
Pages (from-to)1965-1975
Number of pages11
JournalAdvances in Neural Information Processing Systems
Volume2017-December
StatePublished - 2017
Event31st Annual Conference on Neural Information Processing Systems, NIPS 2017 - Long Beach, United States
Duration: Dec 4 2017Dec 9 2017

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

Fingerprint Dive into the research topics of 'Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration'. Together they form a unique fingerprint.

Cite this