Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Near-linear time approximation algorithms for optimal transport via
  Sinkhorn iteration   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 iteration, 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.

27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th