Sinkhorn algorithm converges at O(k^{-1} log k) rate in l1-norm marginal error for asymptotically scalable instances, nearly matching the Omega(k^{-1}) lower bound.
On Sinkhorn’s algorithm and choice modeling
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Almost-sharp $O(k^{-1} \log k)$ convergence rate for the Sinkhorn algorithm in the asymptotically scalable case
Sinkhorn algorithm converges at O(k^{-1} log k) rate in l1-norm marginal error for asymptotically scalable instances, nearly matching the Omega(k^{-1}) lower bound.