# Screening Sinkhorn Algorithm for Regularized Optimal Transport

arXiv: https://arxiv.org/abs/1906.08540 · HTML: https://mlsanity.com/paper/1906.08540/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2019.

## Built on by (most cited first, 20 of 20)

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- [A Novel Skip Orthogonal List for Dynamic Optimal Transport Problem](https://mlsanity.com/paper/2310.18446/) (2023)
- [Optimal Transport with Cyclic Symmetry](https://mlsanity.com/paper/2311.13147/) (2023)
- [Can Circuit Alignment Predict OOD Generalization?](https://mlsanity.com/paper/2609.31996/) (2026)
- [FlashSinkhorn 2: Block-Sparse Entropic Optimal Transport](https://mlsanity.com/paper/2610.02395/) (2026)
- [Exact Optimal Transport by Matching](https://mlsanity.com/paper/2610.04085/) (2026)
- [GPU-Accelerated Bregman Douglas-Rachford Splitting for Discrete Optimal Transport](https://mlsanity.com/paper/2610.04715/) (2026)

## Abstract (arXiv)

We introduce in this paper a novel strategy for efficiently approximating the Sinkhorn distance between two discrete measures. After identifying neglectable components of the dual solution of the regularized Sinkhorn problem, we propose to screen those components by directly setting them at that value before entering the Sinkhorn problem. This allows us to solve a smaller Sinkhorn problem while ensuring approximation with provable guarantees. More formally, the approach is based on a new formulation of dual of Sinkhorn divergence problem and on the KKT optimality conditions of this problem, which enable identification of dual components to be screened. This new analysis leads to the Screenkhorn algorithm. We illustrate the efficiency of Screenkhorn on complex tasks such as dimensionality reduction and domain adaptation involving regularized optimal transport.