In physics, the poppy-seed bagel theorem concerns interacting particles (e.g., electrons) confined to a bounded surface (or body) when the particles repel each other pairwise with a magnitude that is proportional to the inverse distance between them raised to some positive power . In particular, this includes the Coulomb law observed in Electrostatics and Riesz potentials extensively studied in Potential theory. Other classes of potentials, which not necessarily involve the Riesz kernel, for example nearest neighbor interactions, are also described by this theorem in the macroscopic regime. [1] [2] For such particles, a stable equilibrium state, which depends on the parameter , is attained when the associated potential energy of the system is minimal (the so-called generalized Thomson problem). For large numbers of points, these equilibrium configurations provide a discretization of which may or may not be nearly uniform with respect to the surface area (or volume) of . The poppy-seed bagel theorem asserts that for a large class of sets , the uniformity property holds when the parameter is larger than or equal to the dimension of the set . [3] For example, when the points ("poppy seeds") are confined to the 2-dimensional surface of a torus embedded in 3 dimensions (or "surface of a bagel"), one can create a large number of points that are nearly uniformly spread on the surface by imposing a repulsion proportional to the inverse square distance between the points, or any stronger repulsion (). From a culinary perspective, to create the nearly perfect poppy-seed bagel where bites of equal size anywhere on the bagel would contain essentially the same number of poppy seeds, impose at least an inverse square distance repelling force on the seeds.
For a parameter and an -point set , the -energy of is defined as follows:
For a compact set we define its minimal -point -energy as
where the minimum is taken over all -point subsets of ; i.e., . Configurations that attain this infimum are called -point -equilibrium configurations.
We consider compact sets with the Lebesgue measure and . For every fix an -point -equilibrium configuration . Set
where is a unit point mass at point . Under these assumptions, in the sense of weak convergence of measures,
where is the Lebesgue measure restricted to ; i.e., . Furthermore, it is true that
where the constant does not depend on the set and, therefore,
where is the unit cube in .
Consider a smooth -dimensional manifold embedded in and denote its surface measure by . We assume . Assume As before, for every fix an -point -equilibrium configuration and set
Then, [4] [5] in the sense of weak convergence of measures,
where . If is the -dimensional Hausdorff measure normalized so that , then [4] [6]
where is the volume of a d-ball.
For , it is known [6] that , where is the Riemann zeta function. Using a modular form approach to linear programming, Viazovska together with coauthors established in a 2022 paper that in dimensions and , the values of , , are given by the Epstein zeta function [7] associated with the lattice and Leech lattice, respectively. [8] It is conjectured that for , the value of is similarly determined as the value of the Epstein zeta function for the hexagonal lattice. Finally, in every dimension it is known that when , the scaling of becomes rather than , and the value of can be computed explicitly as the volume of the unit -dimensional ball: [4]
The following connection between the constant and the problem of sphere packing is known: [9]
where is the volume of a p-ball and
where the supremum is taken over all families of non-overlapping unit balls such that the limit
exists.
In mathematics, the Lp spaces are function spaces defined using a natural generalization of the p-norm for finite-dimensional vector spaces. They are sometimes called Lebesgue spaces, named after Henri Lebesgue, although according to the Bourbaki group they were first introduced by Frigyes Riesz.
In the mathematical field of real analysis, the monotone convergence theorem is any of a number of related theorems proving the convergence of monotonic sequences that are also bounded. Informally, the theorems state that if a sequence is increasing and bounded above by a supremum, then the sequence will converge to the supremum; in the same way, if a sequence is decreasing and is bounded below by an infimum, it will converge to the infimum.
In mathematical analysis, Hölder's inequality, named after Otto Hölder, is a fundamental inequality between integrals and an indispensable tool for the study of Lp spaces.
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In mathematics, a Dirichlet series is any series of the form
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In mathematics, a divergent series is an infinite series that is not convergent, meaning that the infinite sequence of the partial sums of the series does not have a finite limit.
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In mathematics, especially measure theory, a set function is a function whose domain is a family of subsets of some given set and that (usually) takes its values in the extended real number line which consists of the real numbers and
In physics, particularly in quantum field theory, the Weyl equation is a relativistic wave equation for describing massless spin-1/2 particles called Weyl fermions. The equation is named after Hermann Weyl. The Weyl fermions are one of the three possible types of elementary fermions, the other two being the Dirac and the Majorana fermions.
In machine learning, the kernel embedding of distributions comprises a class of nonparametric methods in which a probability distribution is represented as an element of a reproducing kernel Hilbert space (RKHS). A generalization of the individual data-point feature mapping done in classical kernel methods, the embedding of distributions into infinite-dimensional feature spaces can preserve all of the statistical features of arbitrary distributions, while allowing one to compare and manipulate distributions using Hilbert space operations such as inner products, distances, projections, linear transformations, and spectral analysis. This learning framework is very general and can be applied to distributions over any space on which a sensible kernel function may be defined. For example, various kernels have been proposed for learning from data which are: vectors in , discrete classes/categories, strings, graphs/networks, images, time series, manifolds, dynamical systems, and other structured objects. The theory behind kernel embeddings of distributions has been primarily developed by Alex Smola, Le Song , Arthur Gretton, and Bernhard Schölkopf. A review of recent works on kernel embedding of distributions can be found in.
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