Hellinger-Kantorovich Gradient Flows: Global Exponential Decay of Entropy Functionals
- URL: http://arxiv.org/abs/2501.17049v1
- Date: Tue, 28 Jan 2025 16:17:09 GMT
- Title: Hellinger-Kantorovich Gradient Flows: Global Exponential Decay of Entropy Functionals
- Authors: Alexander Mielke, Jia-Jie Zhu,
- Abstract summary: We investigate a family of gradient flows of positive and probability measures, focusing on the Hellinger-Kantorovich (HK) geometry.
A central contribution is a complete characterization of global exponential decay behaviors of entropy functionals under Otto-Wasserstein and Hellinger-type gradient flows.
- Score: 52.154685604660465
- License:
- Abstract: We investigate a family of gradient flows of positive and probability measures, focusing on the Hellinger-Kantorovich (HK) geometry, which unifies transport mechanism of Otto-Wasserstein, and the birth-death mechanism of Hellinger (or Fisher-Rao). A central contribution is a complete characterization of global exponential decay behaviors of entropy functionals (e.g. KL, $\chi^2$) under Otto-Wasserstein and Hellinger-type gradient flows. In particular, for the more challenging analysis of HK gradient flows on positive measures -- where the typical log-Sobolev arguments fail -- we develop a specialized shape-mass decomposition that enables new analysis results. Our approach also leverages the (Polyak-)\L{}ojasiewicz-type functional inequalities and a careful extension of classical dissipation estimates. These findings provide a unified and complete theoretical framework for gradient flows and underpin applications in computational algorithms for statistical inference, optimization, and machine learning.
Related papers
- Designing a Linearized Potential Function in Neural Network Optimization Using Csiszár Type of Tsallis Entropy [0.0]
In this paper, we establish a framework that utilizes a linearized potential function via Csisz'ar type of Tsallis entropy.
We show that our new framework enable us to derive an exponential convergence result.
arXiv Detail & Related papers (2024-11-06T02:12:41Z) - Kernel Approximation of Fisher-Rao Gradient Flows [52.154685604660465]
We present a rigorous investigation of Fisher-Rao and Wasserstein type gradient flows concerning their gradient structures, flow equations, and their kernel approximations.
Specifically, we focus on the Fisher-Rao geometry and its various kernel-based approximations, developing a principled theoretical framework.
arXiv Detail & Related papers (2024-10-27T22:52:08Z) - Fisher-Rao Gradient Flow: Geodesic Convexity and Functional Inequalities [7.099783891532113]
We provide a study on functional inequalities and the relevant geodesic convexity for Fisher-Rao gradient flows under minimal assumptions.
A notable feature of the obtained functional inequalities is that they do not depend on the log-concavity or log-Sobolev constants of the target distribution.
arXiv Detail & Related papers (2024-07-22T15:00:14Z) - Curvature-Independent Last-Iterate Convergence for Games on Riemannian
Manifolds [77.4346324549323]
We show that a step size agnostic to the curvature of the manifold achieves a curvature-independent and linear last-iterate convergence rate.
To the best of our knowledge, the possibility of curvature-independent rates and/or last-iterate convergence has not been considered before.
arXiv Detail & Related papers (2023-06-29T01:20:44Z) - Gradient is All You Need? [0.0]
In this paper we provide a novel analytical perspective on the theoretical understanding of learning algorithms by interpreting consensus-based gradient-based optimization (CBO)
Our results prove the intrinsic power of CBO to alleviate the complexities of the nonlocal landscape function.
arXiv Detail & Related papers (2023-06-16T11:30:55Z) - Large-Scale Wasserstein Gradient Flows [84.73670288608025]
We introduce a scalable scheme to approximate Wasserstein gradient flows.
Our approach relies on input neural networks (ICNNs) to discretize the JKO steps.
As a result, we can sample from the measure at each step of the gradient diffusion and compute its density.
arXiv Detail & Related papers (2021-06-01T19:21:48Z) - On dissipative symplectic integration with applications to
gradient-based optimization [77.34726150561087]
We propose a geometric framework in which discretizations can be realized systematically.
We show that a generalization of symplectic to nonconservative and in particular dissipative Hamiltonian systems is able to preserve rates of convergence up to a controlled error.
arXiv Detail & Related papers (2020-04-15T00:36:49Z) - A Near-Optimal Gradient Flow for Learning Neural Energy-Based Models [93.24030378630175]
We propose a novel numerical scheme to optimize the gradient flows for learning energy-based models (EBMs)
We derive a second-order Wasserstein gradient flow of the global relative entropy from Fokker-Planck equation.
Compared with existing schemes, Wasserstein gradient flow is a smoother and near-optimal numerical scheme to approximate real data densities.
arXiv Detail & Related papers (2019-10-31T02:26:20Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.