SK converges linearly at rate 1-σ2 once near the optimum, with explicit burn-in, and can be accelerated by gradient methods on the semi-dual.
Katyusha: The first direct acceleration of stochastic gradient methods.Journal of Machine Learning Research, 18(221):1–51, 2018
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Tight Nonasymptotic Local Convergence of Sinkhorn-Knopp
SK converges linearly at rate 1-σ2 once near the optimum, with explicit burn-in, and can be accelerated by gradient methods on the semi-dual.