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Register a new userDisplacement convexity of entropy and related inequalities on graphs
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Authors
Nathael Gozlan
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We introduce the notion of an interpolating path on the set of probability measures on finite graphs. Using this notion, we first prove a displacement convexity property of entropy along such a path and derive Prekopa-Leindler type inequalities, a Talagrand transport-entropy inequality, certain HWI type as well as log-Sobolev type inequalities in discrete settings. To illustrate through examples, we apply our results to the complete graph and to the hypercube for which our results are optimal -- by passing to the limit, we recover the classical log-Sobolev inequality for the standard Gaussian measure with the optimal constant.
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We study an optimal weak transport cost related to the notion of convex order between probability measures. On the real line, we show that this weak transport cost is reached for a coupling that does not depend on the underlying cost function. As an application, we give a necessary and sufficient condition for weak transport-entropy inequalities in dimension one. In particular, we obtain a weak transport-entropy form of the convex Poincar{e} inequality in dimension one.
In the paper, we investigate the following fundamental question. For a set $mathcal{K}$ in $mathbb{L}^0(mathbb{P})$, when does there exist an equivalent probability measure $mathbb{Q}$ such that $mathcal{K}$ is uniformly integrable in $mathbb{L}^1(mathbb{Q})$. Specifically, let $mathcal{K}$ be a convex bounded positive set in $mathbb{L}^1(mathbb{P})$. Kardaras [6] asked the following two questions: (1) If the relative $mathbb{L}^0(mathbb{P})$-topology is locally convex on $mathcal{K}$, does there exist $mathbb{Q}sim mathbb{P}$ such that the $mathbb{L}^0(mathbb{Q})$- and $mathbb{L}^1(mathbb{Q})$-topologies agree on ${mathcal{K}}$? (2) If $mathcal{K}$ is closed in the $mathbb{L}^0(mathbb{P})$-topology and there exists $mathbb{Q}sim mathbb{P}$ such that the $mathbb{L}^0(mathbb{Q})$- and $mathbb{L}^1(mathbb{Q})$-topologies agree on $mathcal{K}$, does there exist $mathbb{Q}sim mathbb{P}$ such that $mathcal{K}$ is $mathbb{Q}$-uniformly integrable? In the paper, we show that, no matter $mathcal{K}$ is positive or not, the first question has a negative answer in general and the second one has a positive answer. In addition to answering these questions, we establish probabilistic and topological characterizations of existence of $mathbb{Q}simmathbb{P}$ satisfying these desired properties. We also investigate the peculiar effects of $mathcal{K}$ being positive.
Firstly, we derive in dimension one a new covariance inequality of $L_{1}-L_{infty}$ type that characterizes the isoperimetric constant as the best constant achieving the inequality. Secondly, we generalize our result to $L_{p}-L_{q}$ bounds for the covariance. Consequently, we recover Cheegers inequality without using the co-area formula. We also prove a generalized weighted Hardy type inequality that is needed to derive our covariance inequalities and that is of independent interest. Finally, we explore some consequences of our covariance inequalities for $L_{p}$-Poincar{e} inequalities and moment bounds. In particular, we obtain optimal constants in general $L_{p}$-Poincar{e} inequalities for measures with finite isoperimetric constant, thus generalizing in dimension one Cheegers inequality, which is a $L_{p}$-Poincar{e} inequality for $p=2$, to any real $pgeq 1$.
Mixtures are convex combinations of laws. Despite this simple definition, a mixture can be far more subtle than its mixed components. For instance, mixing Gaussian laws may produce a potential with multiple deep wells. We study in the present work fine properties of mixtures with respect to concentration of measure and Sobolev type functional inequalities. We provide sharp Laplace bounds for Lipschitz functions in the case of generic mixtures, involving a transportation cost diameter of the mixed family. Additionally, our analysis of Sobolev type inequalities for two-component mixtures reveals natural relations with some kind of band isoperimetry and support constrained interpolation via mass transportation. We show that the Poincare constant of a two-component mixture may remain bounded as the mixture proportion goes to 0 or 1 while the logarithmic Sobolev constant may surprisingly blow up. This counter-intuitive result is not reducible to support disconnections, and appears as a reminiscence of the variance-entropy comparison on the two-point space. As far as mixtures are concerned, the logarithmic Sobolev inequality is less stable than the Poincare inequality and the sub-Gaussian concentration for Lipschitz functions. We illustrate our results on a gallery of concrete two-component mixtures. This work leads to many open questions.
A central tool in the study of nonhomogeneous random matrices, the noncommutative Khintchine inequality of Lust-Piquard and Pisier, yields a nonasymptotic bound on the spectral norm of general Gaussian random matrices $X=sum_i g_i A_i$ where $g_i$ are independent standard Gaussian variables and $A_i$ are matrix coefficients. This bound exhibits a logarithmic dependence on dimension that is sharp when the matrices $A_i$ commute, but often proves to be suboptimal in the presence of noncommutativity. In this paper, we develop nonasymptotic bounds on the spectrum of arbitrary Gaussian random matrices that can capture noncommutativity. These bounds quantify the degree to which the deterministic matrices $A_i$ behave as though they are freely independent. This intrinsic freeness phenomenon provides a powerful tool for the study of various questions that are outside the reach of classical methods of random matrix theory. Our nonasymptotic bounds are easily applicable in concrete situations, and yield sharp results in examples where the noncommutative Khintchine inequality is suboptimal. When combined with a linearization argument, our bounds imply strong asymptotic freeness (in the sense of Haagerup-Thorbj{o}rnsen) for a remarkably general class of Gaussian random matrix models, including matrices that may be very sparse and that lack any special symmetries. Beyond the Gaussian setting, we develop matrix concentration inequalities that capture noncommutativity for general sums of independent random matrices, which arise in many problems of pure and applied mathematics.
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