Wasserstein barycenters can be computed in polynomial time in fixed dimension
June 14, 2020 Β· Declared Dead Β· π Journal of machine learning research
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Authors
Jason M. Altschuler, Enric Boix-Adsera
arXiv ID
2006.08012
Category
math.OC: Optimization & Control
Cross-listed
cs.CG,
cs.DS,
cs.LG
Citations
45
Venue
Journal of machine learning research
Last Checked
2 months ago
Abstract
Computing Wasserstein barycenters is a fundamental geometric problem with widespread applications in machine learning, statistics, and computer graphics. However, it is unknown whether Wasserstein barycenters can be computed in polynomial time, either exactly or to high precision (i.e., with $\textrm{polylog}(1/\varepsilon)$ runtime dependence). This paper answers these questions in the affirmative for any fixed dimension. Our approach is to solve an exponential-size linear programming formulation by efficiently implementing the corresponding separation oracle using techniques from computational geometry.
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