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Estimating the density of a distribution from its samples is a fundamental problem in statistics. Hypothesis selection addresses the setting where, in addition to a sample set, we are given $n$ candidate distributions -- referred to as…

Data Structures and Algorithms · Computer Science 2025-10-23 Maryam Aliakbarpour , Zhan Shi , Ria Stevens , Vincent X. Wang

Consider the geometric range space $(X, \mathcal{H}_d)$ where $X \subset \mathbb{R}^d$ and $\mathcal{H}_d$ is the set of ranges defined by $d$-dimensional halfspaces. In this setting we consider that $X$ is the disjoint union of a red and…

Computational Geometry · Computer Science 2021-06-29 Michael Matheny , Jeff M. Phillips

In this paper, we propose a method to approximate the Gaussian function on ${\mathbb R}$ by a short cosine sum. We generalise and extend the differential approximation method proposed in [4, 40] to approximate $\mathrm{e}^{-t^{2}/2\sigma}$…

Numerical Analysis · Mathematics 2025-05-23 Nadiia Derevianko , Gerlind Plonka

A* is a classic and popular method for graphs search and path finding. It assumes the existence of a heuristic function $h(u,t)$ that estimates the shortest distance from any input node $u$ to the destination $t$. Traditionally, heuristics…

Computational Geometry · Computer Science 2021-11-22 Talya Eden , Piotr Indyk , Haike Xu

We consider a class of pattern matching problems where a normalising transformation is applied at every alignment. Normalised pattern matching plays a key role in fields as diverse as image processing and musical information processing…

Data Structures and Algorithms · Computer Science 2015-03-19 Ayelet Butman , Peter Clifford , Raphael Clifford , Markus Jalsenius , Noa Lewenstein , Benny Porat , Ely Porat , Benjamin Sach

We study fundamental problems in linear algebra, such as finding a maximal linearly independent subset of rows or columns (a basis), solving linear regression, or computing a subspace embedding. For these problems, we consider input…

Data Structures and Algorithms · Computer Science 2023-01-20 Yeshwanth Cherapanamjeri , Sandeep Silwal , David P. Woodruff , Samson Zhou

We consider the problem of linear regression where the $\ell_2^n$ norm loss (i.e., the usual least squares loss) is replaced by the $\ell_p^n$ norm. We show how to solve such problems up to machine precision in $O^*(n^{|1/2 - 1/p|})$…

Optimization and Control · Mathematics 2018-06-26 Sébastien Bubeck , Michael B. Cohen , Yin Tat Lee , Yuanzhi Li

Identifying palindromes in sequences has been an interesting line of research in combinatorics on words and also in computational biology, after the discovery of the relation of palindromes in the DNA sequence with the HIV virus. Efficient…

Data Structures and Algorithms · Computer Science 2017-03-28 Michał Adamczyk , Mai Alzamel , Panagiotis Charalampopoulos , Costas S. Iliopoulos , Jakub Radoszewski

Let $G_n$ be an $n \times n$ matrix with real i.i.d. $N(0,1/n)$ entries, let $A$ be a real $n \times n$ matrix with $\Vert A \Vert \le 1$, and let $\gamma \in (0,1)$. We show that with probability $0.99$, $A + \gamma G_n$ has all of its…

Probability · Mathematics 2020-05-19 Jess Banks , Jorge Garza Vargas , Archit Kulkarni , Nikhil Srivastava

Let $A$ be an $n\times n$ random matrix whose entries are i.i.d. with mean $0$ and variance $1$. We present a deterministic polynomial time algorithm which, with probability at least $1-2\exp(-\Omega(\epsilon n))$ in the choice of $A$,…

Probability · Mathematics 2020-12-02 Vishesh Jain , Ashwin Sah , Mehtaab Sawhney

Score-based Generative Models (SGMs) aim to sample from a target distribution by learning score functions using samples perturbed by Gaussian noise. Existing convergence bounds for SGMs in the W2-distance rely on stringent assumptions about…

Machine Learning · Statistics 2025-12-12 Marta Gentiloni-Silveri , Antonio Ocello

We present a new sublinear time algorithm for approximating the spectral density (eigenvalue distribution) of an $n\times n$ normalized graph adjacency or Laplacian matrix. The algorithm recovers the spectrum up to $\epsilon$ accuracy in…

Data Structures and Algorithms · Computer Science 2022-04-18 Vladimir Braverman , Aditya Krishnan , Christopher Musco

We study the problem of constructing explicit families of matrices which cannot be expressed as a product of a few sparse matrices. In addition to being a natural mathematical question on its own, this problem appears in various…

Computational Complexity · Computer Science 2019-04-03 Mrinal Kumar , Ben Lee Volk

We present a randomized algorithm for producing a quasi-optimal hierarchically semi-separable (HSS) approximation to an $N\times N$ matrix $A$ using only matrix-vector products with $A$ and $A^T$. We prove that, using $O(k \log(N/k))$…

Numerical Analysis · Mathematics 2025-09-09 Noah Amsel , Tyler Chen , Feyza Duman Keles , Diana Halikias , Cameron Musco , Christopher Musco , David Persson

We prove Hunter's conjecture on complete homogeneous symmetric polynomials. For even $n$ and every integer $k\geq 1$, we show that under the constraint $\sum_{i=1}^n a_i^2=1$ the global minimum of the even-degree polynomial…

Probability · Mathematics 2025-12-16 Silouanos Brazitikos , Christos Pandis

We find sharp bounds for the norm inequality on a Pseudo-hermitian manifold, where the L^2 norm of all second derivatives of the function involving horizontal derivatives is controlled by the L^2 norm of the sub-Laplacian. Perturbation…

Analysis of PDEs · Mathematics 2007-05-23 Sagun Chanillo , Juan J. Manfredi

We consider the problem of approximating the unknown density $u\in L^2(\Omega,\lambda)$ of a measure $\mu$ on $\Omega\subset\R^n$, absolutely continuous with respect to some given reference measure $\lambda$, from the only knowledge of…

Optimization and Control · Mathematics 2012-09-03 Didier Henrion , Jean-Bernard Bernard Lasserre , Martin Mevissen

We prove an optimal $\Omega(n)$ lower bound on the randomized communication complexity of the much-studied Gap-Hamming-Distance problem. As a consequence, we obtain essentially optimal multi-pass space lower bounds in the data stream model…

Computational Complexity · Computer Science 2012-07-02 Amit Chakrabarti , Oded Regev

Subgraph and homomorphism counting are fundamental algorithmic problems. Given a constant-sized pattern graph $H$ and a large input graph $G$, we wish to count the number of $H$-homomorphisms/subgraphs in $G$. Given the massive sizes of…

Data Structures and Algorithms · Computer Science 2023-11-17 Daniel Paul-Pena , C. Seshadhri

Boxicity of a graph G(V, E) is the minimum integer k such that G can be represented as the intersection graph of axis parallel boxes in $\mathbb{R}^k$. Cubicity is a variant of boxicity, where the axis parallel boxes in the intersection…

Discrete Mathematics · Computer Science 2015-06-09 Abhijin Adiga , Jasine Babu , L. Sunil Chandran
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