An operator splitting analysis of Wasserstein--Fisher--Rao gradient flows
stat.ML, cs.LG, stat.ME
Submitted: 2025-11-22
Updated: 2026-09-16
Comments: Changes from v2: content from section 4 and part of section 5 has been moved to another arXiv submission
License: http://creativecommons.org/licenses/by/4.0/
The gist: Wasserstein-Fisher-Rao (WFR) gradient flows have been recently proposed as a powerful sampling tool that combines the advantages of pure Wasserstein (W) and pure Fisher-Rao (FR) gradient flows.
Terminology
Abstract
Wasserstein-Fisher-Rao (WFR) gradient flows have been recently proposed as a powerful sampling tool that combines the advantages of pure Wasserstein (W) and pure Fisher-Rao (FR) gradient flows. Existing algorithmic developments implicitly make use of operator splitting techniques to numerically approximate the WFR partial differential equation, whereby the W flow is evaluated over a given step size and then the FR flow (or vice versa). This works investigates the impact of the order in which the W and FR operator are evaluated and aims to provide a quantitative analysis. Somewhat surprisingly, we show that with a judicious choice of step size and operator ordering, the split scheme can converge to the target distribution faster than the exact WFR flow (in terms of model time). We obtain variational formulae describing the evolution over one time step of both splitting schemes and investigate in which settings the W-FR split should be preferred to the FR-W split. As a step towards this goal we show that the WFR gradient flow preserves log-concavity and obtain the first sharp decay bound for WFR flow.
Sources
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- Unbiased Kinetic Langevin Monte Carlo with Inexact Gradients
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- Gradient Flows in Filtering and Fisher-Rao Geometry
- Accelerating Langevin Sampling with Birth-death
- Evolution of Gaussians in the Hellinger-Kantorovich-Boltzmann gradient flow
- Characterizing Dependence of Samples along the Langevin Dynamics and Algorithms via Contraction of $\Phi$-Mutual Information
- Stein transport for Bayesian inference
- Optimised Annealed Sequential Monte Carlo Samplers
- Tuning-Free Sampling via Optimization on the Space of Probability Measures
- Measure transport with kernel mean embeddings
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