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This paper has two agendas. Firstly, we exhibit new results for coverage processes. Let $p(n,m,\alpha)$ be the probability that $n$ spherical caps of angular radius $\alpha$ in $S^m$ do not cover the whole sphere $S^m$. We give an exact…

Probability · Mathematics 2011-06-17 Peter Bürgisser , Felipe Cucker , Martin Lotz

Let ${\mathbf P}^2$ denote the projective plane over a finite field ${\mathbb F}_q$. A pair of nonsingular conics $({\mathcal A}, {\mathcal B})$ in the plane is said to satisfy the Poncelet triangle condition if, considered as conics in…

Algebraic Geometry · Mathematics 2016-04-05 Jaydeep Chipalkatti

We present a simple sublinear time algorithm for testing the following geometric property. Let $P_1, ..., P_n$ be $n$ convex sets in $\mathbb{R}^d$ ($n \gg d$), such as polytopes, balls, etc. We assume that the complexity of each set…

Data Structures and Algorithms · Computer Science 2016-12-13 Israela Solomon

We study the approximability of general convex sets in $\mathbb{R}^n$ by intersections of halfspaces, where the approximation quality is measured with respect to the standard Gaussian distribution $N(0,I_n)$ and the complexity of an…

Computational Complexity · Computer Science 2023-11-16 Anindya De , Shivam Nadimpalli , Rocco A. Servedio

Let $\mathbb{P}_{\kappa}(n)$ be the probability that $n$ points $z_1,\ldots,z_n$ picked uniformly and independently in $\mathfrak{C}_\kappa$, a regular $\kappa$-gon with area $1$, are in convex position, that is, form the vertex set of a…

Probability · Mathematics 2024-10-16 Ludovic Morin

The Grassmannian of affine subspaces is a natural generalization of both the Euclidean space, points being zero-dimensional affine subspaces, and the usual Grassmannian, linear subspaces being special cases of affine subspaces. We show…

Differential Geometry · Mathematics 2018-07-31 Lek-Heng Lim , Ken Sze-Wai Wong , Ke Ye

We show strong (and surprisingly simple) lower bounds for weakly learning intersections of halfspaces in the improper setting. Strikingly little is known about this problem. For instance, it is not even known if there is a polynomial-time…

Computational Complexity · Computer Science 2026-05-06 Stefan Tiegel

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

We consider the problem of finding the probability that a random triangle is obtuse, which was first raised by Lewis Caroll. Our investigation leads us to a natural correspondence between plane polygons and the Grassmann manifold of…

Metric Geometry · Mathematics 2019-10-23 Jason Cantarella , Tom Needham , Clayton Shonkwiler , Gavin Stewart

Small regularizers can preserve linear programming solutions exactly. This paper provides the first average-case analysis of exact regularization: with a standard Gaussian cost vector and fixed constraint set, bounds are established for the…

Optimization and Control · Mathematics 2025-10-16 Michael P. Friedlander , Sharvaj Kubal , Yaniv Plan , Matthew S. Scott

I revisit the condition number of computing left and right singular subspaces from [J.-G. Sun, Perturbation analysis of singular subspaces and deflating subspaces, Numer. Math. 73(2), pp. 235--263, 1996]. For real and complex matrices, I…

Numerical Analysis · Mathematics 2024-07-02 Nick Vannieuwenhoven

We study the maximum likelihood degree of linear concentration models in algebraic statistics. We relate the geometry of the reciprocal variety to that of semidefinite programming. We show that the Zariski closure in the Grassmanian of the…

Algebraic Geometry · Mathematics 2020-12-02 Kathlen Kohn , Rosa Winter , Yuhan Jiang

Let $(\Omega,\mathcal{F},P)$ be a probability space and $\mathcal{N}$ the class of those $F\in\mathcal{F}$ satisfying $P(F)\in\{0,1\}$. For each $\mathcal{G}\subset\mathcal{F}$, define $\overline{\mathcal{G}}=\sigma(\mathcal{G}\cup\mathcal…

Probability · Mathematics 2009-01-20 Patrizia Berti , Luca Pratelli , Pietro Rigo

For polyhedral convex cones in ${\mathbb R}^d$, we give a proof for the conic kinematic formula for conic curvature measures, which avoids the use of characterization theorems. For the random cones defined as typical cones of an isotropic…

Metric Geometry · Mathematics 2017-06-13 Rolf Schneider

We prove a pointwise version of the multi-dimensional central limit theorem for convex bodies. Namely, let X be an isotropic random vector in R^n with a log-concave density. For a typical subspace E in R^n of dimension n^c, consider the…

Metric Geometry · Mathematics 2007-08-21 Ronen Eldan , Bo'az Klartag

This paper examines the feasible region of a standard conic program represented as the intersection of a closed convex cone and a set of linear equalities. It is recently shown that when Slater constraint qualification (strict feasibility)…

Optimization and Control · Mathematics 2025-04-22 Haesol Im

We derive bounds relating Renegar's condition number to quantities that govern the statistical performance of convex regularization in settings that include the $\ell_1$-analysis setting. Using results from conic integral geometry, we show…

Numerical Analysis · Mathematics 2019-09-30 Dennis Amelunxen , Martin Lotz , Jake Walvin

We present an analysis of sets of matrices with rank less than or equal to a specified number $s$. We provide a simple formula for the normal cone to such sets, and use this to show that these sets are prox-regular at all points with rank…

Optimization and Control · Mathematics 2018-09-24 D. Russell Luke

The Grassmann angle improves upon similar angles between subspaces that measure volume contraction in orthogonal projections. It works in real or complex spaces, with important differences, and is asymmetric, what makes it more efficient…

Metric Geometry · Mathematics 2021-01-13 André L. G. Mandolesi

Random intersection graphs are characterized by three parameters: $n$, $m$ and $p$, where $n$ is the number of vertices, $m$ is the number of objects, and $p$ is the probability that a given object is associated with a given vertex. Two…

Combinatorics · Mathematics 2016-09-07 Katarzyna Rybarczyk , Dudley Stark