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arXiv 1903.04522math.PRmath.FA
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对数Sobolev不等式通过Föllmer过程的稳定性

Stability of the logarithmic Sobolev inequality via the Föllmer Process

Ronen Eldan, Joseph Lehec, Yair Shenfeld

摘要:

利用协方差、Wasserstein距离和Fisher信息研究了高斯对数Sobolev不等式的稳定性和不稳定性,解决了文献中的几个公开问题.首先建立了一个改进的对数Sobolev不等式,它具有时间尺度不变性和无量纲性.作为一个推论,我们表明,如果协方差的措施是有界的身份,可以得到一个尖锐的和无量纲的稳定界的Fisher信息矩阵。然后,我们调查在什么条件下稳定性估计控制协方差,当这样的控制是不可能的。对于协方差阵受恒等式支配的测度类,在固定协方差约束下,得到了对数Sobolev不等式中亏损最小化的最优无量纲稳定界.另一方面,我们构造的例子表明,没有协方差的有界性,不等式是不稳定的。最后,我们研究了Wasserstein距离的稳定性,并表明,即使是有界的协方差阵的措施类,这是不可能获得无量纲的稳定性结果。提供的反例激励我们提出一个新的概念的稳定性,在接近混合高斯分布。我们证明了新的估计(一些无量纲)基于这个概念。这些估计是严格强于一些现有的稳定性结果的Wasserstein度量。我们的证明技术在很大程度上依赖于随机方法。

英文摘要:

We study the stability and instability of the Gaussian logarithmic Sobolev inequality, in terms of covariance, Wasserstein distance and Fisher information, addressing several open questions in the literature. We first establish an improved logarithmic Sobolev inequality which is at the same time scale invariant and dimension free. As a corollary, we show that if the covariance of the measure is bounded by the identity, one may obtain a sharp and dimension-free stability bound in terms of the Fisher information matrix. We then investigate under what conditions stability estimates control the covariance, and when such control is impossible. For the class of measures whose covariance matrix is dominated by the identity, we obtain optimal dimension-free stability bounds which show that the deficit in the logarithmic Sobolev inequality is minimized by Gaussian measures, under a fixed covariance constraint. On the other hand, we construct examples showing that without the boundedness of the covariance, the inequality is not stable. Finally, we study stability in terms of the Wasserstein distance, and show that even for the class of measures with a bounded covariance matrix, it is hopeless to obtain a dimension-free stability result. The counterexamples provided motivate us to put forth a new notion of stability, in terms of proximity to mixtures of the Gaussian distribution. We prove new estimates (some dimension-free) based on this notion. These estimates are strictly stronger than some of the existing stability results in terms of the Wasserstein metric. Our proof techniques rely heavily on stochastic methods.

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