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Quadratically Regularized Optimal Transport: Localization Bounds and Affine Case Analysis

arXiv 2605.24644 ↗First posted 23 May 2026math.OC stat.MLcited by 0

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Abstract (arXiv)

Quadratic regularization has emerged as a potential alternative to the popular entropic regularization in computational optimal transport, offering the theoretical advantage of producing sparse couplings through its hinge density structure. Despite recent progress in one-dimensional settings and general upper bounds, fundamental questions about the localization rate of QOT optimizers around the Monge coupling have remained open. In this work, we establish a general lower bound showing that the support of the QOT optimizer cannot concentrate around the Monge graph faster than order varepsilon^frac1d+2 in the directed Hausdorff distance, matching the conjectured optimal exponent under standard regularity assumptions in citetwiesel2025sparsity. We also show that the QOT value gap controls the mean-squared deviation mathbb Eπ_varepsilon|y-T(x)|2 by the scale of varepsilon^frac2d+2. As a corollary, in the affine Brenier regime, which includes Gaussian-to-Gaussian transport, we derive a sharp pointwise tube bound of order varepsilon^frac1d+2 by reducing the problem to self-transport and applying recent self-transport sparsity results. Finally, we validate our theoretical bound with a synthetic experiment in high-dimensional settings.

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References · 20

  1. 2022Flow Matching for Generative Modelingcited by 5,702
  2. 2013Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distancescited by 2,316
  3. 2015Optimal Transport for Domain Adaptationcited by 463
  4. 2017Learning Generative Models with Sinkhorn Divergencescited by 314
  5. 2022Rectified Flow: A Marginal Preserving Approach to Optimal Transportcited by 283
  6. 2015Learning with a Wasserstein Losscited by 196
  7. 2017Large-Scale Optimal Transport and Mapping Estimationcited by 123
  8. 2021Entropic estimation of optimal transport mapscited by 90
  9. 2017Quadratically-Regularized Optimal Transport on Graphscited by 19
  10. 2019Quadratically regularized optimal transportcited by 15
  11. 2021Near-optimal estimation of smooth transport maps with kernel sums-of-squarescited by 12
  12. 2022Convergence Rates for Regularized Optimal Transport via Quantizationcited by 12
8 more
  1. 2025Stability Bounds for Smooth Optimal Transport Maps and their Statistical Implicationscited by 10
  2. 2024ENOT: Expectile Regularization for Fast and Accurate Training of Neural Optimal Transportcited by 6
  3. 2020Orlicz space regularization of continuous optimal transport problemscited by 5
  4. 2024Sparsity of Quadratically Regularized Optimal Transport: Scalar Casecited by 4
  5. 2022Beyond kNN: Adaptive, Sparse Neighborhood Graphs via Optimal Transportcited by 3
  6. 2024Quadratically Regularized Optimal Transport: Existence and Multiplicity of Potentialscited by 3
  7. 2024Sparsity of Quadratically Regularized Optimal Transport: Bounds on concentration and biascited by 3
  8. 2025Sample Complexity of Quadratically Regularized Optimal Transportcited by 1

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