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Volume approximation is an important problem found in many applications of computer graphics, vision, and image processing. The problem is about computing an accurate and compact approximate representation of 3D volumes using some simple…

Graphics · Computer Science 2013-08-20 Feng Sun , Yi-King Choi , Yizhou Yu , Wenping Wang

We study the problem of estimating a multivariate convex function defined on a convex body in a regression setting with random design. We are interested in optimal rates of convergence under a squared global continuous $l_2$ loss in the…

Statistics Theory · Mathematics 2016-01-27 Qiyang Han , Jon A. Wellner

The maximum volume $j$-simplex problem asks to compute the $j$-dimensional simplex of maximum volume inside the convex hull of a given set of $n$ points in $\mathbb{Q}^d$. We give a deterministic approximation algorithm for this problem…

Computational Geometry · Computer Science 2015-04-15 Aleksandar Nikolov

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 $\mu$ and $\nu$ such that $\nu$ is log-concave with respect to $\mu$.…

Probability · Mathematics 2022-10-24 Arturo Jaramillo , James Melbourne

In his paper "On the Schlafli differential equality", J. Milnor conjectured that the volume of n-dimensional hyperbolic and spherical simplices, as a function of the dihedral angles, extends continuously to the closure of the space of…

Geometric Topology · Mathematics 2007-05-23 Igor Rivin

For a Banach space $X$ by $Conv_H(X)$ we denote the space of non-empty closed convex subsets of $X$, endowed with the Hausdorff metric. We prove that for any closed convex set $C\subset X$ and its metric component $H_C=\{A\in…

Functional Analysis · Mathematics 2012-12-19 Taras Banakh , Ivan Hetman

We present an improved algorithm for {\em quasi-properly} learning convex polyhedra in the realizable PAC setting from data with a margin. Our learning algorithm constructs a consistent polyhedron as an intersection of about $t \log t$…

Machine Learning · Computer Science 2021-11-03 Lee-Ad Gottlieb , Eran Kaufman , Aryeh Kontorovich , Gabriel Nivasch

We present and analyze an algorithm for estimating the size, under a Gaussian or uniform measure, of a localized neighborhood in neural network parameter space with behavior similar to an ``anchor'' point. We refer to this as the "local…

Machine Learning · Computer Science 2025-04-09 Adam Scherlis , Nora Belrose

It was shown by G. Pisier that any finite-dimensional normed space admits an $\alpha$-regular $M$-position, guaranteeing not only regular entropy estimates but moreover regular estimates on the diameters of minimal sections of its unit-ball…

Functional Analysis · Mathematics 2021-05-28 Emanuel Milman , Yuval Yifrach

Recently, motivated by the rapid increase of the data size in various applications, Monemizadeh [APPROX'23] and Driemel, Monemizadeh, Oh, Staals, and Woodruff [SoCG'25] studied geometric problems in the setting where the only access to the…

Computational Geometry · Computer Science 2026-03-24 T. Schibler , J. Xue , J. Zhu

We analyse and improve the volume-penalty method, a simple and versatile way to model objects in fluid flows. The volume-penalty method is a kind of fictitious-domain method that approximates no-slip boundary conditions with rapid linear…

Numerical Analysis · Mathematics 2020-12-09 Eric W. Hester , Geoffrey M. Vasil , Keaton J. Burns

In this paper, we give an overview of some results concerning best and random approximation of convex bodies by polytopes. We explain how both are linked and see that random approximation is almost as good as best approximation.

Metric Geometry · Mathematics 2021-11-16 Joscha Prochno , Carsten Schütt , Elisabeth M. Werner

We propose novel randomized optimization methods for high-dimensional convex problems based on restrictions of variables to random subspaces. We consider oblivious and data-adaptive subspaces and study their approximation properties via…

Information Theory · Computer Science 2020-12-15 Jonathan Lacotte , Mert Pilanci

Suppose we choose $N$ points uniformly randomly from a convex body in $d$ dimensions. How large must $N$ be, asymptotically with respect to $d$, so that the convex hull of the points is nearly as large as the convex body itself? It was…

Probability · Mathematics 2020-09-22 Alan Frieze , Wesley Pegden , Tomasz Tkocz

We develop a probabilistic framework for large-scale dimension bounds in metric geometry, based on padded decompositions, randomized ball carving on net graphs, and the Lov\'asz Local Lemma. For metric measure spaces with volume doubling…

Metric Geometry · Mathematics 2026-05-18 Jing Yu , Xingyu Zhu

The volume is an important attribute of a convex body. In general, it is quite difficult to calculate the exact volume. But in many cases, it suffices to have an approximate value. Volume estimation methods for convex bodies have been…

Computational Geometry · Computer Science 2014-01-03 Cunjing Ge , Feifei Ma , Jian Zhang

The aperture angle alpha(x, Q) of a point x not in Q in the plane with respect to a convex polygon Q is the angle of the smallest cone with apex x that contains Q. The aperture angle approximation error of a compact convex set C in the…

Computational Geometry · Computer Science 2010-08-26 Hee-Kap Ahn , Sang Won Bae , Otfried Cheong , Joachim Gudmundsson

In this paper we prove asymptotic upper bounds on the variance of the number of vertices and missed area of inscribed random disc-polygons in smooth convex discs whose boundary is $C^2_+$. We also consider a circumscribed variant of this…

Metric Geometry · Mathematics 2019-07-10 Ferenc Fodor , Viktor Vígh

In this paper, we generalize the chance optimization problems and introduce constrained volume optimization where enables us to obtain convex formulation for challenging problems in systems and control. We show that many different problems…

Optimization and Control · Mathematics 2017-02-01 Ashkan Jasour , Constantino Lagoa

This article investigates the numerical approximation of shape optimization problems with PDE constraint on classes of convex domains. The convexity constraint provides a compactness property which implies well posedness of the problem.…

Optimization and Control · Mathematics 2018-10-26 Sören Bartels , Gerd Wachsmuth