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Let $ r, s>0 $. For a given probability measure $P$ on $\mathbb{R}^d$, let $(\alpha_n)_{n \geq 1}$ be a sequence of (asymptotically) $L^r(P)$- optimal quantizers. For all $\mu \in \mathbb{R}^d $ and for every $\theta >0$, one defines the…

Probability · Mathematics 2007-11-19 Abass Sagna

We investigate the greedy version of the $L^p$-optimal vector quantization problem for an $\mathbb{R}^d$-valued random vector $X\!\in L^p$. We show the existence of a sequence $(a_N)_{N\ge 1}$ such that $a_N$ minimizes $a\mapsto\big…

Probability · Mathematics 2015-08-24 Harald Luschgy , Gilles Pagès

We elucidate the asymptotics of the L^s-quantization error induced by a sequence of L^r-optimal n-quantizers of a probability distribution P on R^d when s>r. In particular we show that under natural assumptions, the optimal rate is…

Probability · Mathematics 2016-08-16 Siegried Graf , Harald Luschgy , Gilles Pagès

The aim of this paper is to develop greedy algorithms which generate uniformly distributed sequences in the $d$-dimensional unit cube $[0,1]^d$. The figures of merit are three different variants of $L_2$ discrepancy. Theoretical results…

Number Theory · Mathematics 2022-10-19 Ralph Kritzinger

We extend some rate of convergence results of greedy quantization sequences already investigated in arXiv:1409.0732 [math.PR]. We show, for a more general class of distributions satisfying a certain control, that the quantization error of…

Probability · Mathematics 2020-04-01 Rancy El Nmeir , Harald Luschgy , Gilles Pagès

We study the convergence rate of the optimal quantization for a probability measure sequence $(\mu_{n})_{n\in\mathbb{N}^{*}}$ on $\mathbb{R}^{d}$ converging in the Wasserstein distance in two aspects: the first one is the convergence rate…

Statistics Theory · Mathematics 2020-02-20 Yating Liu , Gilles Pagès

In this paper, we investigate the mixed norm estimates for the operator $ T $associated with a dilated plane curve $(ut, u\gamma(t))$, defined by \[ Tf(x, u) := \int_{0}^{1} f(x_1 - ut, x_2 - u\gamma(t)) \, dt, \] where $ x := (x_1, x_2) $…

Analysis of PDEs · Mathematics 2025-12-05 Junfeng Li , Zengjian Lou , Haixia Yu

We investigate the asymptotic behavior of greedy $s$-Riesz and Green energy sequences $\{x_{n}\}_{n=1}^{\infty}$ on the unit sphere $\mathbb{S}^{d} \subset \mathbb{R}^{d+1}$, where each point $x_n$ is defined as the minimizer of the…

Classical Analysis and ODEs · Mathematics 2026-04-17 Dmitriy Bilyk , Liudmyla Kryvonos , Ryan W. Matzke , Edward Saff

Let $P$ be a probability distribution on $\mathbb{R}^d$ (equipped with an Euclidean norm $|\cdot|$). Let $ r> 0 $ and let $(\alpha_n)_{n \geq1}$ be an (asymptotically) $L^r(P)$-optimal sequence of $n$-quantizers. We investigate the…

Probability · Mathematics 2012-03-20 Gilles Pagès , Abass Sagna

We investigate the behavior of a greedy sequence on the sphere $\mathbb{S}^d$ defined so that at each step the point that minimizes the Riesz $s$-energy is added to the existing set of points. We show that for $0<s<d$, the greedy sequence…

Classical Analysis and ODEs · Mathematics 2023-08-01 Dmitriy Bilyk , Michelle Mastrianni , Ryan W. Matzke , Stefan Steinerberger

Next-generation wireless networks, such as edge intelligence and wireless distributed learning, face two critical challenges: communication efficiency and privacy protection. In this work, our focus is on addressing these issues in a…

Distributed, Parallel, and Cluster Computing · Computer Science 2023-12-13 Guangfeng Yan , Tan Li , Tian Lan , Kui Wu , Linqi Song

The approximation of a discrete probability distribution $\mathbf{t}$ by an $M$-type distribution $\mathbf{p}$ is considered. The approximation error is measured by the informational divergence $\mathbb{D}(\mathbf{t}\Vert\mathbf{p})$, which…

Information Theory · Computer Science 2016-07-28 Bernhard C. Geiger , Georg Böcherer

Recent work on exploration in reinforcement learning (RL) has led to a series of increasingly complex solutions to the problem. This increase in complexity often comes at the expense of generality. Recent empirical studies suggest that,…

Machine Learning · Computer Science 2020-06-03 Will Dabney , Georg Ostrovski , André Barreto

We study the problem of distributed mean estimation and optimization under communication constraints. We propose a correlated quantization protocol whose leading term in the error guarantee depends on the mean deviation of data points…

Machine Learning · Computer Science 2022-07-12 Ananda Theertha Suresh , Ziteng Sun , Jae Hun Ro , Felix Yu

We analyse the performance of several iterative algorithms for the quantisation of a probability measure $\mu$, based on the minimisation of a Maximum Mean Discrepancy (MMD). Our analysis includes kernel herding, greedy MMD minimisation and…

Machine Learning · Statistics 2022-04-29 Luc Pronzato

Let S be a planar point set. Krznaric and Levcopoulos proved that given the Delaunay triangulation DT(S) for S, one can find the greedy triangulation GT(S) in linear time. We provide a (partial) converse of this result: given GT(S), it is…

Computational Geometry · Computer Science 2013-02-27 Wolfgang Mulzer , Paul Seiferth

In this work, we study Bayesian quantum parameter estimation given a finite number of uses of the process encoding one or more unknown physical quantities. For multiple uses, it is conventional to classify quantum metrological protocols as…

Quantum Physics · Physics 2026-02-11 Erik L. André , Jessica Bavaresco , Mohammad Mehboudi

This undergraduate thesis focuses on calculating maximum likelihood estimates of parameters in the generalized Gamma distribution using the SeLF algorithm. As an extension of the Gamma distribution, the generalized Gamma distribution can…

Methodology · Statistics 2026-01-01 Yufei Cai

We consider a discrete-time linear quadratic Gaussian networked control setting where the (full information) observer and controller are separated by a fixed-rate noiseless channel. The minimal rate required to stabilize such a system has…

Systems and Control · Computer Science 2018-09-14 Anatoly Khina , Yorie Nakahira , Yu Su , Hikmet Yıldız , Babak Hassibi

We present SimultaneousGreedys, a deterministic algorithm for constrained submodular maximization. At a high level, the algorithm maintains $\ell$ solutions and greedily updates them in a simultaneous fashion. SimultaneousGreedys achieves…

Data Structures and Algorithms · Computer Science 2021-07-15 Moran Feldman , Christopher Harshaw , Amin Karbasi
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