Derives the cold Sinkhorn limiting dynamics as tau approaches zero, proving finite-time convergence to unregularized OT and improved O(tau^{-1}) iteration complexity for dual suboptimality.
arXiv preprint arXiv:2408.11620 , year=
4 Pith papers cite this work. Polarity classification is still indexing.
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Proves sharp O(1/k) rate for Sinkhorn via local bipartite graph analysis of positive-mass edges, bootstrapped from prior almost-sharp global bound.
Acc-Sinkhorn achieves O(1/k²) convergence for entropy-regularized OT via Hessian-driven Nesterov acceleration on a reduced dual objective, improving unregularized OT approximation to Õ(n²/ε) complexity.
Entropy-adaptive Gumbel-Sinkhorn formulation for unsupervised permutation learning that modulates temperature per assignment to address non-uniform uncertainty.
citing papers explorer
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Effective dynamics of the Sinkhorn algorithm in the regime of low entropy regularization
Derives the cold Sinkhorn limiting dynamics as tau approaches zero, proving finite-time convergence to unregularized OT and improved O(tau^{-1}) iteration complexity for dual suboptimality.
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Sharp $O(1/k)$ convergence rate for the Sinkhorn algorithm via a local analysis
Proves sharp O(1/k) rate for Sinkhorn via local bipartite graph analysis of positive-mass edges, bootstrapped from prior almost-sharp global bound.
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Accelerating Sinkhorn for Entropy-Regularized Optimal Transport
Acc-Sinkhorn achieves O(1/k²) convergence for entropy-regularized OT via Hessian-driven Nesterov acceleration on a reduced dual objective, improving unregularized OT approximation to Õ(n²/ε) complexity.