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Related papers: Optimality of the Johnson-Lindenstrauss Lemma

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An oblivious subspace embedding is a random $m\times n$ matrix $\Pi$ such that, for any $d$-dimensional subspace, with high probability $\Pi$ preserves the norms of all vectors in that subspace within a $1\pm\epsilon$ factor. In this work,…

Data Structures and Algorithms · Computer Science 2025-04-30 Shabarish Chenakkod , Michał Dereziński , Xiaoyu Dong

An oblivious subspace embedding (OSE) for some eps, delta in (0,1/3) and d <= m <= n is a distribution D over R^{m x n} such that for any linear subspace W of R^n of dimension d, Pr_{Pi ~ D}(for all x in W, (1-eps) |x|_2 <= |Pi x|_2 <=…

Discrete Mathematics · Computer Science 2013-08-19 Jelani Nelson , Huy L. Nguyen

Here is one of the results obtained in this paper: Let $X, Y$ be two convex sets each in a real vector space, let $J:X\times Y\to {\bf R}$ be convex and without global minima in $X$ and concave in $Y$, and let $\Phi:X\to {\bf R}$ be…

Optimization and Control · Mathematics 2019-09-19 Biagio Ricceri

The metric sketching problem is defined as follows. Given a metric on $n$ points, and $\epsilon>0$, we wish to produce a small size data structure (sketch) that, given any pair of point indices, recovers the distance between the points up…

Computational Geometry · Computer Science 2016-11-30 Piotr Indyk , Tal Wagner

Many algorithms in machine learning and computational geometry require, as input, the intrinsic dimension of the manifold that supports the probability distribution of the data. This parameter is rarely known and therefore has to be…

Statistics Theory · Mathematics 2020-01-01 Jisu Kim , Alessandro Rinaldo , Larry Wasserman

Motivated by the problem of filtering candidate pairs in inner product similarity joins we study the following inner product estimation problem: Given parameters $d\in {\bf N}$, $\alpha>\beta\geq 0$ and unit vectors $x,y\in {\bf R}^{d}$…

Data Structures and Algorithms · Computer Science 2020-01-14 Rasmus Pagh , Johan Sivertsen

In this paper, we present a more complete version of the minimax theorem established in [7]. As a consequence, we get, for instance, the following result: Let $X$ be a compact, not singleton subset of a normed space $(E,\|\cdot\|)$ and let…

Functional Analysis · Mathematics 2021-04-13 Biagio Ricceri

We prove that there is a universal constant $C>0$ with the following property. Suppose that $n\in \mathbb{N}$ and that $\mathsf{A}=(a_{ij})\in M_n(\mathbb{R})$ is a symmetric stochastic matrix. Denote the second-largest eigenvalue of…

Metric Geometry · Mathematics 2016-11-29 Assaf Naor

For $\lambda \in (1/2, 1)$ and $\alpha$, we consider sets of numbers $x$ such that for infinitely many $n$, $x$ is $2^{-\alpha n}$-close to some $\sum_{i=1}^n \omega_i \lambda^i$, where $\omega_i \in \{0,1\}$. These sets are in Falconer's…

Number Theory · Mathematics 2014-01-14 Tomas Persson , Henry W. J. Reeve

We consider an optimization problem in a convex space $E$ with an affine objective function, subject to $J$ constraints in the forms of inequalities on some other affine functions, where $J$ is a given nonnegative integer. Under suitable…

Optimization and Control · Mathematics 2023-05-11 Alexey Piunovskiy , Yi Zhang

Let $\mathscr{F}_{n,d}$ be the class of all functions $f:\{-1,1\}^n\to[-1,1]$ on the $n$-dimensional discrete hypercube of degree at most $d$. In the first part of this paper, we prove that any (deterministic or randomized) algorithm which…

Machine Learning · Computer Science 2024-10-23 Alexandros Eskenazis , Paata Ivanisvili , Lauritz Streck

We study the effect of Johnson-Lindenstrauss transforms in various projective clustering problems, generalizing recent results which only applied to center-based clustering [MMR19]. We ask the general question: for a Euclidean optimization…

Data Structures and Algorithms · Computer Science 2023-07-11 Moses Charikar , Erik Waingarten

One important question in the theory of lattices is to detect a shortest vector: given a norm and a lattice, what is the smallest norm attained by a non-zero vector contained in the lattice? We focus on the infinity norm and work with…

Optimization and Control · Mathematics 2026-03-18 Stefan Kuhlmann , Robert Weismantel

We consider the $(1+\epsilon)$-approximate nearest neighbor search problem: given a set $X$ of $n$ points in a $d$-dimensional space, build a data structure that, given any query point $y$, finds a point $x \in X$ whose distance to $y$ is…

Data Structures and Algorithms · Computer Science 2018-07-03 Piotr Indyk , Tal Wagner

Consider a sum $S_n=v_i\varepsilon_1+\cdots+v_n\varepsilon_{n}$, where $(v_i)^{n}_{i=1}$ are non-zero vectors in $\mathbb{R}^{d}$ and $(\varepsilon_i)^{n}_{i=1}$ are independent Rademacher random variables (i.e.,…

Probability · Mathematics 2019-10-23 Dainius Dzindzalieta , Tomas Juškevičius

Let $d$ and $k$ be integers with $1 \leq k \leq d-1$. Let $\Lambda$ be a $d$-dimensional lattice and let $K$ be a $d$-dimensional compact convex body symmetric about the origin. We provide estimates for the minimum number of $k$-dimensional…

Combinatorics · Mathematics 2018-01-04 Martin Balko , Josef Cibulka , Pavel Valtr

An oblivious subspace embedding (OSE), characterized by parameters $m,n,d,\epsilon,\delta$, is a random matrix $\Pi\in \mathbb{R}^{m\times n}$ such that for any $d$-dimensional subspace $T\subseteq \mathbb{R}^n$, $\Pr_\Pi[\forall x\in T,…

Data Structures and Algorithms · Computer Science 2021-12-22 Yi Li , Mingmou Liu

Recently, Gilmer proved the first constant lower bound for the union-closed sets conjecture via an information-theoretic argument. The heart of the argument is an entropic inequality involving the OR function of two i.i.d.\ binary vectors,…

Information Theory · Computer Science 2023-06-16 Jingbo Liu

Let $P$ be a set of $n$ points in $\Re^2$. For a parameter $\varepsilon\in (0,1)$, a subset $C\subseteq P$ is an \emph{$\varepsilon$-kernel} of $P$ if the projection of the convex hull of $C$ approximates that of $P$ within…

Computational Geometry · Computer Science 2023-03-15 Pankaj K. Agarwal , Sariel Har-Peled

We consider the problem of computing the smallest possible distortion for embedding of a given n-point metric space into R^d, where d is fixed (and small). For d=1, it was known that approximating the minimum distortion with a factor better…

Computational Geometry · Computer Science 2009-09-29 Jiri Matousek , Anastasios Sidiropoulos