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Riesz-Kernel Stein Variational Gradient Descent: Renormalized Entropy and Long-Time Particle Limits

arXiv 2607.14527 ↗First posted 16 Jul 2026math.AP cs.LGcited by 0

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Abstract (arXiv)

Stein variational gradient descent (SVGD) transports interacting particles toward a target distribution through deterministic kernelized dynamics. Singular Riesz kernels are attractive because they can provide quantitative population-level convergence, but at the finite-particle level the corresponding Stein energy has infinite self-interaction. We study periodic Riesz SVGD with self-interaction removed and prove a many-particle, long-time sampling theorem. Throughout the range in which the singular Stein energy is locally integrable, under a uniform bound on the initial relative entropy per particle, the time-averaged empirical-measure law converges weakly to the point mass (δπ) at the target as the particle number and any diverging averaging horizon tend to infinity. We also show that the empirical-measure laws induced by invariant particle laws of finite relative entropy converge weakly to (δπ), without a uniform entropy bound. Below the logarithmic singularity threshold, we obtain an explicit algebraic finite-particle error bound. These results extend the joint-entropy approach for smooth-kernel SVGD to singular interactions.

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References · 15

  1. 2016Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithmcited by 498
  2. 2017Stein Variational Gradient Descent as Gradient Flowcited by 149
  3. 2016A Kernel Test of Goodness of Fitcited by 143
  4. 2017Measuring Sample Quality with Kernelscited by 119
  5. 2019On the geometry of Stein variational gradient descentcited by 82
  6. 2020SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergencecited by 64
  7. 2020A Non-Asymptotic Analysis for Stein Variational Gradient Descentcited by 61
  8. 2018Scaling limit of the Stein variational gradient descent: the mean field regimecited by 35
  9. 2019Stein Variational Gradient Descent With Matrix-Valued Kernelscited by 32
  10. 2022A Finite-Particle Convergence Rate for Stein Variational Gradient Descentcited by 22
  11. 2021Stein Variational Gradient Descent: many-particle and long-time asymptoticscited by 15
  12. 2024Improved Finite-Particle Convergence Rates for Stein Variational Gradient Descentcited by 13
3 more
  1. 2019A stochastic version of Stein Variational Gradient Descent for efficient samplingcited by 10
  2. 2026Quantitative Local Convergence of Mean-Field Stein Variational Gradient Flowcited by 1
  3. 2026Uniform-in-time Propagation-of-Chaos for Stein Variational Gradient Descentcited by 1

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