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Related papers: Bregman distances and Klee sets

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We provide upper bounds of the expected Wasserstein distance between a probability measure and its empirical version, generalizing recent results for finite dimensional Euclidean spaces and bounded functional spaces. Such a generalization…

Statistics Theory · Mathematics 2020-01-29 Jing Lei

This paper proves a conjecture by Solomon about Steiner shallow-light trees (SLT) in Euclidean $d$-space: It is shown that for any finite point set $\mathbb{R}^d$, any root, and any $\epsilon>0$, there is a Euclidean Steiner…

Computational Geometry · Computer Science 2026-05-27 Devin Frost , Kimberly Kokado , Csaba D. Tóth

Every maximally monotone operator can be associated with a family of convex functions, called the Fitzpatrick family or family of representative functions. Surprisingly, in 2017, Burachik and Mart\'inez-Legaz showed that the well-known…

Functional Analysis · Mathematics 2023-08-29 Regina S. Burachik , Minh N. Dao , Scott B. Lindstrom

We introduce a reduction from the distinct distances problem in ${\mathbb R}^d$ to an incidence problem with $(d-1)$-flats in ${\mathbb R}^{2d-1}$. Deriving the conjectured bound for this incidence problem (the bound predicted by the…

Combinatorics · Mathematics 2019-04-09 Sam Bardwell-Evans , Adam Sheffer

The author has recently introduced the class of CNED sets in Euclidean space, generalizing the classical notion of NED sets, and shown that they are quasiconformally removable. A set $E$ is CNED if the conformal modulus of a curve family is…

Complex Variables · Mathematics 2025-02-17 Dimitrios Ntalampekos

Separable Bregman divergences induce Riemannian metric spaces that are isometric to the Euclidean space after monotone embeddings. We investigate fixed rate quantization and its codebook Voronoi diagrams, and report on experimental…

Machine Learning · Computer Science 2018-10-26 Erika Gomes-Gonçalves , Henryk Gzyl , Frank Nielsen

Single-cell omics enable the profiles of cells, which contain large numbers of biological features, to be quantified. Cluster analysis, a dimensionality reduction process, is used to reduce the dimensions of the data to make it…

Genomics · Quantitative Biology 2024-06-06 Okezue Bell , Arthur Lee , Elizabeth Engle

A well-known object in classical Euclidean geometry is the circumcenter of a triangle, i.e., the point that is equidistant from all vertices. The purpose of this paper is to provide a systematic study of the circumcenter of sets containing…

Optimization and Control · Mathematics 2018-07-06 Heinz H. Bauschke , Hui Ouyang , Xianfu Wang

Contraction theory for dynamical systems on Euclidean spaces is well-established. For contractive (resp. semi-contractive) systems, the distance (resp. semi-distance) between any two trajectories decreases exponentially fast. For partially…

Optimization and Control · Mathematics 2021-06-07 Pedro Cisneros-Velarde , Saber Jafarpour , Francesco Bullo

In this paper, we first prove that any interior point of an open interval of the real line can be interpreted as Fr\'echet means with respect to corresponding metric distances, thus extending the result of [Dinh et al., Mathematical…

Computational Geometry · Computer Science 2026-02-04 Frank Nielsen

A finite set of distinct vectors $\mathcal{X}$ in the $d$-dimensional Euclidean space $\mathbb{R}^d$ is called an $s$-distance set if the set of mutual distances between distinct elements of $\mathcal{X}$ has cardinality $s$. In this paper…

Metric Geometry · Mathematics 2018-04-18 Ferenc Szöllősi , Patric R. J. Östergård

On any proper convex domain in real projective space there exists a natural Riemannian metric, the Blaschke metric. On the other hand, distances between points can be measured in the Hilbert metric. Using techniques of optimal control, we…

Differential Geometry · Mathematics 2021-02-23 Roland Hildebrand

A finite set of distinct vectors $\mathcal{X}$ in the $d$-dimensional Euclidean space $\mathbb{R}^d$ is called a $2$-distance set, if the set of mutual distances between distinct elements of $\mathcal{X}$ has cardinality exactly $2$. In…

Metric Geometry · Mathematics 2018-06-21 Ferenc Szöllősi

By using the metric projection onto a closed self-dual cone of the Euclidean space, M. S. Gowda, R. Sznajder and J. Tao have defined generalized lattice operations, which in the particular case of the nonnegative orthant of a Cartesian…

Functional Analysis · Mathematics 2013-01-28 A. B. Németh , S. Z. Németh

Phylogenetic inference-the derivation of a hypothesis for the common evolutionary history of a group of species- is an active area of research at the intersection of biology, computer science, mathematics, and statistics. One assumes the…

Populations and Evolution · Quantitative Biology 2016-06-21 Ruth Davidson , Joseph Rusinko , Zoe Vernon , Jing Xi

The geometry of closed surfaces equipped with a Euclidean metric with finitely many conical points of arbitrary angle is studied. The main result is that the image of a non-closed geodesic has 0 distance from the set of conical points.…

Geometric Topology · Mathematics 2016-03-08 Charalampos Charitos , Ioannis Papadoperakis , Georgios Tsapogas

Let M be a smooth strictly convex closed surface in space and denote by H the set of points x in the exterior of M such that all the tangent segments from x to M have equal lengths. In this note we prove that if H is either a closed surface…

Metric Geometry · Mathematics 2012-05-07 J. Jeronimo-Castro , G. Ruiz-Hernandez , S. Tabachnikov

Let $(\mathcal{M},g)$ be a Riemannian manifold and $\mathcal{N}$ a $\mathcal{C}^2$ submanifold without boundary. If we multiply the metric $g$ by the inverse of the squared distance to $\mathcal{N}$, we obtain a new metric structure on…

Differential Geometry · Mathematics 2015-01-20 Juan G. Criado del Rey

Consider the sum of the first $N$ eigenspaces for the Laplacian on a Riemannian manifold. A basis for this space determines a map to Euclidean space and for $N$ sufficiently large the map is an embedding. In analogy with a fruitful idea of…

Differential Geometry · Mathematics 2014-04-30 Eric Potash

In [3], algorithms to compute the density of the distance to a random variable uniformly distributed in (a) a ball, (b) a disk, (c) a line segment, or (d) a polygone were introduced. For case (d), the algorithm, based on Green's theorem,…

Computational Geometry · Computer Science 2019-06-06 Vincent Guigues
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