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James Melbourne

Publications and source records attributed to James Melbourne.

At least 37 records · Page 2Linked to original sources
arXiv2022

Quantitative limit theorems via relative log-concavity

In this paper we develop tools for studying limit theorems by means of convexity. We establish bounds for the discrepancy in total variation between probability measures $μ$ and $ν$ such that $ν$ is log-concave with respect to $μ$. We discuss a variety of applications, which include geometric and binomial approximations to sums of random variables, and discrepancy between Gamma distributions. As special cases we obtain a law of rare events for intrinsic volumes, quantitative bounds on proximity to geometric for infinitely divisible distributions, as well as binomial and Poisson approximation for matroids.

math.PR↗
arXiv2022

Transport-majorization to analytic and geometric inequalities

We introduce a transport-majorization argument that establishes a majorization in the convex order between two densities, based on control of the gradient of a transportation map between them. As applications, we give elementary derivations of some delicate Fourier analytic inequalities, which in turn yield geometric "slicing-inequalities" in both continuous and discrete settings. As a further consequence of our investigation we prove that any strongly log-concave probability density majorizes the Gaussian density and thus the Gaussian density maximizes the Rényi and Tsallis entropies of all orders among all strongly log-concave densities.

math.FA↗
arXiv2021

A discrete complement of Lyapunov's inequality and its information theoretic consequences

We establish a reversal of Lyapunov's inequality for monotone log-concave sequences, settling a conjecture of Havrilla-Tkocz and Melbourne-Tkocz. A strengthened version of the same conjecture is disproved through counter example. We also derive several information theoretic inequalities as consequences. In particular sharp bounds are derived for the varentropy, Rényi entropies, and the concentration of information of monotone log-concave random variables. Moreover, the majorization approach utilized in the proof of the main theorem, is applied to derive analogous information theoretic results in the symmetric setting, where the Lyapunov reversal is known to fail.

cs.IT↗
arXiv2021

Concentration Inequalities for Ultra Log-Concave Distributions

We establish concentration inequalities in the class of ultra log-concave distributions. In particular, we show that ultra log-concave distributions satisfy Poisson concentration bounds. As an application, we derive concentration bounds for the intrinsic volumes of a convex body, which generalizes and improves a result of Lotz, McCoy, Nourdin, Peccati, and Tropp (2019).

math.PR↗
arXiv2020

Strongly Convex Divergences

We consider a sub-class of the $f$-divergences satisfying a stronger convexity property, which we refer to as strongly convex, or $κ$-convex divergences. We derive new and old relationships, based on convexity arguments, between popular $f$-divergences.

cs.IT↗
arXiv2020

Convex Decreasing Algorithms: Distributed Synthesis and Finite-time Termination in Higher Dimension

We introduce a general mathematical framework for distributed algorithms, and a monotonicity property frequently satisfied in application. These properties are leveraged to provide finite-time guarantees for converging algorithms, suited for use in the absence of a central authority. A central application is to consensus algorithms in higher dimension. These pursuits motivate a new peer to peer convex hull algorithm which we demonstrate to be an instantiation of the described theory. To address the diversity of convex sets and the potential computation and communication costs of knowing such sets in high dimension, a lightweight norm based stopping criteria is developed. More explicitly, we give a distributed algorithm that terminates in finite time when applied to consensus problems in higher dimensions and guarantees the convergence of the consensus algorithm in norm, within any given tolerance. Applications to consensus least squared estimation and distributed function determination are developed. The practical utility of the algorithm is illustrated through MATLAB simulations.

eess.SY↗
arXiv2020

Reversals of Rényi Entropy Inequalities under Log-Concavity

We establish a discrete analog of the Rényi entropy comparison due to Bobkov and Madiman. For log-concave variables on the integers, the min entropy is within log e of the usual Shannon entropy. Additionally we investigate the entropic Rogers-Shephard inequality studied by Madiman and Kontoyannis, and establish a sharp Rényi version for certain parameters in both the continuous and discrete cases

math.PR↗
arXiv2020

The Differential Entropy of Mixtures: New Bounds and Applications

Mixture distributions are extensively used as a modeling tool in diverse areas from machine learning to communications engineering to physics, and obtaining bounds on the entropy of probability distributions is of fundamental importance in many of these applications. This article provides sharp bounds on the entropy concavity deficit, which is the difference between the entropy of the mixture and the weighted sum of entropies of constituent components. Toward establishing lower and upper bounds on the concavity deficit, results that are of importance in their own right are obtained. In order to obtain nontrivial upper bounds, properties of the skew-divergence are developed and notions of "skew" $f$-divergences are introduced; a reverse Pinsker inequality and a bound on Jensen-Shannon divergence are obtained along the way. Complementary lower bounds are derived with special attention paid to the case that corresponds to independent summation of a continuous and a discrete random variable. Several applications of the bounds are delineated, including to mutual information of additive noise channels, thermodynamics of computation, and functional inequalities.

cs.IT↗
arXiv2019

Further investigations of Rényi entropy power inequalities and an entropic characterization of s-concave densities

We investigate the role of convexity in Rényi entropy power inequalities. After proving that a general Rényi entropy power inequality in the style of Bobkov-Chistyakov (2015) fails when the Rényi parameter $r\in(0,1)$, we show that random vectors with $s$-concave densities do satisfy such a Rényi entropy power inequality. Along the way, we establish the convergence in the Central Limit Theorem for Rényi entropies of order $r\in(0,1)$ for log-concave densities and for compactly supported, spherically symmetric and unimodal densities, complementing a celebrated result of Barron (1986). Additionally, we give an entropic characterization of the class of $s$-concave densities, which extends a classical result of Cover and Zhang (1994).

math.PR↗
arXiv2019

Rearrangement and Prekopa-Leindler type inequalities

We investigate the interactions of functional rearrangements with Prekopa-Leindler type inequalities. It is shown that that a general class of integral inequalities tighten on rearrangement to "isoperimetric" sets with respect to a relevant measure. Applications to the Borell-Brascamp-Lieb, Borell-Ehrhart, and the recent polar Prekopa-Leindler inequalities are demonstrated. It is also proven that an integrated form of the Gaussian log-Sobolev inequality decreases on half-space rearrangement.

math.PR↗
arXiv2018

Realizing Information Erasure in Finite Time

In this article, we focus on erasure of a bit of information in finite time. Landauer's principle states that the average heat dissipation due to erasure of information is k_B T ln 2, which is achievable only in an asymptotic manner. Recent theoretical developments in non-equilibrium thermodynamics and stochastic control, predict a more general bound for finite time erasure dependent on the Wasserstein distances between the initial and final configurations. These predictions suggest improvements to experimental protocol with regards to minimizing average heat dissipation for bit erasure in finite time from a bistable well, under overdamped Langevin dynamics. We present a comparative study of a theoretically optimal protocol with an existing protocol, and highlight the closeness and deviation from optimality

cond-mat.stat-mech↗
arXiv2018

On the entropy power inequality for the Rényi entropy of order [0,1]

Using a sharp version of the reverse Young inequality, and a Rényi entropy comparison result due to Fradelizi, Madiman, and Wang, the authors are able to derive Rényi entropy power inequalities for log-concave random vectors when Rényi parameters belong to $(0,1)$. Furthermore, the estimates are shown to be sharp up to absolute constants.

cs.IT↗
arXiv2018

Error Bounds on a Mixed Entropy Inequality

Motivated by the entropy computations relevant to the evaluation of decrease in entropy in bit reset operations, the authors investigate the deficit in an entropic inequality involving two independent random variables, one continuous and the other discrete. In the case where the continuous random variable is Gaussian, we derive strong quantitative bounds on the deficit in the inequality. More explicitly it is shown that the decay of the deficit is sub-Gaussian with respect to the reciprocal of the standard deviation of the Gaussian variable. What is more, up to rational terms these results are shown to be sharp.

cs.IT↗
arXiv2018

Designing Memory Bits with Dissipation lower than the Landauer's Bound

A Brownian particle in a symmetric double well potential is used as a representation for a single bit memory, where, the location of the particle in either well denotes one of the two states of a single bit memory. This article analyzes the effect of modifications to a symmetric double well potential on the minimum heat dissipation associated with erasure of the information stored in a single bit memory. Two types of modifications are considered, viz., overlap between the two wells and the asymmetry between the two wells of a bit of memory. Moreover, the analysis presented here, takes into account the uncertainty in the success of the erasure process. We quantify the effect of the proposed modifications on the heat dissipation accompanying erasure of a bit of information with a comparison to the Landauer's bound. In particular, we conclude that the proposed modifications could result in the minimum heat dissipation being lower than the Landauer's bound in quasi-static erasure processes. Furthermore, we quantify the physical aspect ratio for designing a memory bit which could improve memory density.

cond-mat.stat-mech↗
arXiv2017

Rogozin's convolution inequality for locally compact groups

General extensions of an inequality due to Rogozin, concerning the essential supremum of a convolution of probability density functions on the real line, are obtained. While a weak version of the inequality is proved in the very general context of Polish $σ$-compact groups, particular attention is paid to the group \(\mathbb{R}^d\), where the result can combined with rearrangement inequalities for certain linear images for a strong generalization. As a consequence, we obtain a unification and sharpening of both the \(\infty\)-Renyi entropy power inequality for sums of independent random vectors, due to Bobkov and Chistyakov, and the bounds on marginals of projections of product measures due to Rudelson and Vershynin (matching and extending the sharp improvement of Livshyts, Paouris and Pivovarov). The proof is elementary and relies on a characterization of extreme points of a class of probability measures in the general setting of Polish measure spaces, as well as the development of a generalization of Ball's cube slicing bounds for products of \(d\)-dimensional Euclidean balls (where the "co-dimension 1" case had been recently settled by Brzezinski).

math.PR↗