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Sharp Convergence of Wasserstein Gradient Flows for Spectrally Nonnegative Interaction Energies

arXiv 2609.27008 ↗First posted 22 Sep 2026math.AP cs.LG math.OCcited by 0

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Abstract (arXiv)

We study the long-time behavior of Wasserstein gradient flows for interaction energies [ mathsf E[μ] = frac12iintM× MK(x,y),mathrm dμ(x),mathrm dμ(y) ] on a closed manifold M. For kernels diagonal in a Laplace eigenbasis with nonnegative spectral coefficients, we prove a differential inequality relating the relative entropy to the energy gap. Consequently, for any nonnegative initial density u0∈ Lp(M), p>1, the energy gap is integrable in time and satisfies [ mathsf E[μt]-mathsf Emin=o(t-1). ] If all spectral coefficients are positive, the flow converges weakly to the constant measure. These interaction energies need not be geodesically convex in Wasserstein space, and the associated flows contain no diffusion; their global convergence therefore does not follow from standard Wasserstein gradient flow theory. The kernels covered by our results include zonal kernels on spheres, kernels arising in transformer models, regularized Riesz kernels, and inverse fractional Laplacian kernels. We also investigate the sharpness of the o(t-1) rate. For any smooth kernel in this class with infinitely many positive spectral coefficients and any δ>0, we construct a solution of the linearized flow whose energy is comparable to t-1-δ along a sequence of times tending to infinity. Moreover, for any δ>0, by choosing a suitable inverse fractional Laplacian kernel on the flat torus, we construct an exact solution of the nonlinear Wasserstein gradient flow whose energy is comparable to t-1-δ. The nonlinear construction is based on uniform-in-time estimates for the evolution of the dyadic Fourier coefficient blocks and a blockwise energy-persistence argument. These estimates also yield a uniform-in-time quantitative comparison between the nonlinear Wasserstein gradient flow and its linearization.

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References · 17

  1. 2015Generative Moment Matching Networkscited by 257
  2. 2017MMD GAN: Towards Deeper Understanding of Moment Matching Networkcited by 254
  3. 2023A mathematical perspective on Transformerscited by 164
  4. 2019Maximum Mean Discrepancy Gradient Flowcited by 127
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  6. 2023Generative Sliced MMD Flows with Riesz Kernelscited by 37
  7. 2025The Mean-Field Dynamics of Transformerscited by 30
  8. 2025Quantitative Clustering in Mean-Field Transformer Modelscited by 26
  9. 2023Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance Kernelcited by 25
  10. 2025A multiscale analysis of mean-field transformers in the moderate interaction regimecited by 25
  11. 2024Deep MMD Gradient Flow without adversarial trainingcited by 19
  12. 2026Quantitative Convergence of Wasserstein Gradient Flows of Kernel Mean Discrepanciescited by 10
5 more
  1. 2023A qualitative difference between gradient flows of convex functions in finite- and infinite-dimensional Hilbert spacescited by 3
  2. 2025Convergence of drift-diffusion PDEs arising as Wasserstein gradient flows of convex functionscited by 3
  3. 2026Propagation of Chaos in Contextual Flow Mapscited by 3
  4. 2026Wasserstein gradient flows for Coulomb discrepanciescited by 2
  5. 2026Wasserstein gradient flows of Maximum Mean Discrepancy with energy kernelscited by 2

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