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In this note we examine the volume of the convex hull of two congruent copies of a convex body in Euclidean $n$-space, under some subsets of the isometry group of the space. We prove inequalities for this volume if the two bodies are…

Metric Geometry · Mathematics 2013-06-19 Ákos G. Horváth , Z. Lángi

Invariance (defined in a general sense) has been one of the most effective priors for representation learning. Direct factorization of parametric models is feasible only for a small range of invariances, while regularization approaches,…

Machine Learning · Computer Science 2020-07-28 Yingyi Ma , Vignesh Ganapathiraman , Yaoliang Yu , Xinhua Zhang

In this paper we prove the strong $q$-log-convexity of the Eulerian polynomials of Coxeter groups using their exponential generating functions. Our proof is based on the theory of exponential Riordan arraya and a criterion for determining…

Combinatorics · Mathematics 2014-09-03 Lily Li Liu , Bao-Xuan Zhu

As established by R T. Rockafellar, real valued convex-concave functions are generically differentiable. It this paper we shall show that for a convex-concave function defined on an open convex set $C \times D,$ there exist dense subsets…

Functional Analysis · Mathematics 2013-01-17 Abbas Moameni

Coarse data arise when learners observe only partial information about samples; namely, a set containing the sample rather than its exact value. This occurs naturally through measurement rounding, sensor limitations, and lag in economic…

Machine Learning · Computer Science 2026-02-27 Alkis Kalavasis , Anay Mehrotra , Manolis Zampetakis , Felix Zhou , Ziyu Zhu

An Euler discretization of the Langevin diffusion is known to converge to the global minimizers of certain convex and non-convex optimization problems. We show that this property holds for any suitably smooth diffusion and that different…

Machine Learning · Statistics 2019-12-30 Murat A. Erdogdu , Lester Mackey , Ohad Shamir

The Euclidean space notion of convex sets (and functions) generalizes to Riemannian manifolds in a natural sense and is called geodesic convexity. Extensively studied computational problems such as convex optimization and sampling in convex…

Optimization and Control · Mathematics 2020-02-10 Navin Goyal , Abhishek Shetty

The Gauss-Bonnet curvature of order $2k$ is a generalization to higher dimensions of the Gauss-Bonnet integrand in dimension $2k$, as the usual scalar curvature generalizes the two dimensional Gauss-Bonnet integrand. In this paper, we…

Differential Geometry · Mathematics 2007-05-23 Mohammed-Larbi Labbi

The theory of intrinsic volumes of convex cones has recently found striking applications in areas such as convex optimization and compressive sensing. This article provides a self-contained account of the combinatorial theory of intrinsic…

Combinatorics · Mathematics 2017-08-23 Dennis Amelunxen , Martin Lotz

In this work, we define a family of probability densities involving the generalized trigonometric functions defined by Dr\'abek and Man\'asevich [1], which we name Generalized Trigonometric Densities. We show their relationship with the…

Mathematical Physics · Physics 2025-04-01 David Puertas-Centeno , Steeve Zozor

We show that many important convex matrix functions can be represented as the partial infimal projection of the generalized matrix fractional (GMF) and a relatively simple convex function. This representation provides conditions under which…

Optimization and Control · Mathematics 2019-05-13 James V. Burke , Yuan Gao , Tim Hoheisel

In this work we study the estimation of the density of a totally positive random vector. Total positivity of the distribution of a random vector implies a strong form of positive dependence between its coordinates and, in particular, it…

Statistics Theory · Mathematics 2023-05-10 Ali Zartash , Elina Robeva

This study leverages the basic insight that the gradient-flow equation associated with the relative Boltzmann entropy, in relation to a Gaussian reference measure within the Hellinger-Kantorovich (HK) geometry, preserves the class of…

Analysis of PDEs · Mathematics 2025-04-30 Matthias Liero , Alexander Mielke , Oliver Tse , Jia-Jie Zhu

The present paper is concerned with Lipschitz properties of convex mappings. One considers the general context of mappings defined on an open convex subset $\Omega$ of a locally convex space $X$ and taking values in a locally convex space…

Functional Analysis · Mathematics 2017-01-12 S. Cobzaş

Tensor products of convex cones have recently come up in different areas, ranging from functional analysis and operator theory to approximation theory and theoretical physics. However, most of the existing literature focuses either on…

Functional Analysis · Mathematics 2022-12-08 Josse van Dobben de Bruyn

The (delta-) normal cone to an arbitrary intersection of sublevel sets of proper, lower semicontinuous, and convex functions is characterized, using either epsilon-subdifferentials at the nominal point or exact subdifferentials at nearby…

Optimization and Control · Mathematics 2017-10-30 Abderrahim Hantoute , Anton Svensson

In this paper we show the following result: if C is an n-dimensional 0-symmetric convex compact set, $f:C\rightarrow[0,1)$ is concave, and $g:[0,1)\rightarrow[0,1)$ is not identically zero, convex, with g(0)=0, then \[ \frac{1}{|C|}\int_C…

Functional Analysis · Mathematics 2020-04-29 Bernardo González Merino

Under suitable conditions on the range of the Gauss map of a complete submanifold of Euclidean space with parallel mean curvature, we construct a strongly subharmonic function and derive a-priori estimates for the harmonic Gauss map. The…

Differential Geometry · Mathematics 2010-09-21 J. Jost , Y. L. Xin , Ling Yang

We show that the expected value of the mean width of a random polytope generated by $N$ random vectors ($n\leq N\leq e^{\sqrt n}$) uniformly distributed in an isotropic convex body in $\R^n$ is of the order $\sqrt{\log N} L_K$. This…

Functional Analysis · Mathematics 2012-05-29 David Alonso-Gutierrez , Joscha Prochno

We prove, using the Brascamp-Lieb inequality, that the Gaussian measure is the only strong log-concave measure having a strong log-concavity parameter equal to its covariance matrix. We also give a similar characterization of the Poisson…

Probability · Mathematics 2019-05-09 Erwan Hillion , Oliver Johnson , Adrien Saumard