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In the present paper we prove several results concerning the existence of low-discrepancy point sets with respect to an arbitrary non-uniform measure $\mu$ on the $d$-dimensional unit cube. We improve a theorem of Beck, by showing that for…

Number Theory · Mathematics 2013-08-26 Christoph Aistleitner , Josef Dick

The scattering of waves by obstacles in a 2D setting is considered, in particular the computation of the scattered field via the collocation or the least-squares methods. In the case of multiple scattering by smooth obstacles, we prove that…

Numerical Analysis · Mathematics 2014-01-15 Gilles Chardon

The discrepancy between two independent samples \(X_1,\dots,X_n\) and \(Y_1,\dots,Y_n\) drawn from the same distribution on $\mathbb{R}^d$ typically has order \(O(\sqrt{n})\) even in one dimension. We give a simple online algorithm that…

Probability · Mathematics 2026-01-23 Gleb Smirnov , Roman Vershynin

Gr\"unbaum's equipartition problem asked if for any measure $\mu$ on $\mathbb{R}^d$ there are always $d$ hyperplanes which divide $\mathbb{R}^d$ into $2^d$ $\mu$-equal parts. This problem is known to have a positive answer for $d\le 3$ and…

Combinatorics · Mathematics 2024-10-04 Gerardo L. Maldonado , Edgardo Roldán-Pensado

Phenomena with a constrained sample space appear frequently in practice. This is the case e.g. with strictly positive data and with compositional data, like percentages and the like. If the natural measure of difference is not the absolute…

Methodology · Statistics 2008-02-20 G. Mateu-Figueras , V. Pawlowsky-Glahn , J. J. Egozcue

Nested sampling was employed to investigate adsorption equilibria on the truncated-octahedral Lennard-Jones nanocluster LJ$_{38}$ while systematically varying adsorbate-surface well depth and Lennard-Jones size parameters. Evaluation of the…

Materials Science · Physics 2025-12-16 Thanawitch Chatbipho , Ray Yang , Robert B. Wexler , Livia B. Pártay

For a probability measure on a real separable Hilbert space, we are interested in "volume-based" approximations of the d-dimensional least squares error of it, i.e., least squares error with respect to a best fit d-dimensional affine…

Functional Analysis · Mathematics 2012-10-08 Gilad Lerman , J. Tyler Whitehouse

We show that any second order linear ordinary diffrential equation with constant coefficients (including the damped and undumped harmonic oscillator equation) admits an exact discretization, i.e., there exists a difference equation whose…

Popular Physics · Physics 2007-05-23 Jan L. Cieslinski , Boguslaw Ratkiewicz

We consider quantizing an $Ld$-dimensional sample, which is obtained by concatenating $L$ vectors from datasets of $d$-dimensional vectors, to a $d$-dimensional cluster center. The distortion measure is the weighted sum of $r$th powers of…

Machine Learning · Computer Science 2020-10-26 Erdem Koyuncu

Dyadic shifts of point distributions in the multi-dimensional unit cube are considered as a randomization. Explicit formulas for the discrepancies of such randomized distributions are given in the paper in terms of Rademacher functions.…

Combinatorics · Mathematics 2014-11-18 M. M. Skriganov

We consider a system of classical particles confined in a box $\Lambda\subset\mathbb{R}^d$ with zero boundary conditions interacting via a stable and regular pair potential. Based on the validity of the cluster expansion for the canonical…

Probability · Mathematics 2021-03-30 Giuseppe Scola

The dissipated spaces form a class of compacta which contains both the scattered compacta and the compact LOTSes (linearly ordered topological spaces), and a number of theorems true for these latter two classes are true more generally for…

General Topology · Mathematics 2007-05-23 Kenneth Kunen

In this paper, we delve deeper into the Kullback-Leibler (KL) Divergence loss and mathematically prove that it is equivalent to the Decoupled Kullback-Leibler (DKL) Divergence loss that consists of (1) a weighted Mean Square Error (wMSE)…

Machine Learning · Computer Science 2025-03-12 Jiequan Cui , Beier Zhu , Qingshan Xu , Zhuotao Tian , Xiaojuan Qi , Bei Yu , Hanwang Zhang , Richang Hong

We study the extreme $L_p$ discrepancy of infinite sequences in the $d$-dimensional unit cube, which uses arbitrary sub-intervals of the unit cube as test sets. This is in contrast to the classical star $L_p$ discrepancy, which uses…

Number Theory · Mathematics 2021-09-15 Ralph Kritzinger , Friedrich Pillichshammer

The $L_p$-discrepancy is a classical quantitative measure for the irregularity of distribution of an $N$-element point set in the $d$-dimensional unit cube. Its inverse for dimension $d$ and error threshold $\varepsilon \in (0,1)$ is the…

Numerical Analysis · Mathematics 2024-03-28 Erich Novak , Friedrich Pillichshammer

Diffusion models, which convert noise into new data instances by learning to reverse a diffusion process, have become a cornerstone in contemporary generative modeling. In this work, we develop non-asymptotic convergence theory for a…

Machine Learning · Computer Science 2024-08-06 Gen Li , Yuting Wei , Yuejie Chi , Yuxin Chen

In this paper we propose an acceptance-rejection sampler using stratified inputs as diver sequence. We estimate the discrepancy of the points generated by this algorithm. First we show an upper bound on the star discrepancy of order…

Computation · Statistics 2014-08-11 Houying Zhu , Josef Dick

The dispersion of a point set in $[0,1]^d$ is the volume of the largest axis parallel box inside the unit cube that does not intersect with the point set. We study the expected dispersion with respect to a random set of $n$ points…

Probability · Mathematics 2020-03-27 Aicke Hinrichs , David Krieg , Robert J. Kunsch , Daniel Rudolf

We perform Monte Carlo simulations to determine the average excluded volume <V_{ex}> of randomly oriented rectangular prisms, randomly oriented ellipsoids and randomly oriented capped cylinders in 3-D. There is agreement between the…

Disordered Systems and Neural Networks · Physics 2007-05-23 Sameet Sreenivasan , Don R. Baker , Gerald Paul , H. Eugene Stanley

Diffusion models generate samples by iteratively querying learned score estimates. A rapidly growing literature focuses on accelerating sampling by minimizing the number of score evaluations, yet the information-theoretic limits of such…

Machine Learning · Computer Science 2026-04-14 Zhiyang Xun , Eric Price