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In this paper we examined an algorithm for the All-k-Nearest-Neighbor problem proposed in 1980s, which was claimed to have an $O(n\log{n})$ upper bound on the running time. We find the algorithm actually exceeds the so claimed upper bound,…

Computational Geometry · Computer Science 2019-08-06 Hengzhao Ma , Jianzhong Li

The problem of minimizing the maximum of $N$ convex, Lipschitz functions plays significant roles in optimization and machine learning. It has a series of results, with the most recent one requiring $O(N\epsilon^{-2/3} + \epsilon^{-8/3})$…

Quantum Physics · Physics 2024-02-21 Hao Wang , Chenyi Zhang , Tongyang Li

We construct near-optimal coresets for kernel density estimates for points in $\mathbb{R}^d$ when the kernel is positive definite. Specifically we show a polynomial time construction for a coreset of size $O(\sqrt{d}/\varepsilon\cdot…

Machine Learning · Computer Science 2019-04-15 Jeff M. Phillips , Wai Ming Tai

We study the sublinear multivariate mean estimation problem in $d$-dimensional Euclidean space. Specifically, we aim to find the mean $\mu$ of a ground point set $A$, which minimizes the sum of squared Euclidean distances of the points in…

Data Structures and Algorithms · Computer Science 2025-10-07 Beatrice Bertolotti , Matteo Russo , Chris Schwiegelshohn , Sudarshan Shyam

We consider the complexity for computing the approximate sum $a_1+a_2+...+a_n$ of a sorted list of numbers $a_1\le a_2\le ...\le a_n$. We show an algorithm that computes an $(1+\epsilon)$-approximation for the sum of a sorted list of…

Data Structures and Algorithms · Computer Science 2012-01-24 Bin Fu

We consider the differentially private (DP) facility location problem in the so called super-set output setting proposed by Gupta et al. [SODA 2010]. The current best known expected approximation ratio for an $\epsilon$-DP algorithm is…

Data Structures and Algorithms · Computer Science 2024-03-11 Pasin Manurangsi

In the semi-streaming model for processing massive graphs, an algorithm makes multiple passes over the edges of a given $n$-vertex graph and is tasked with computing the solution to a problem using $O(n \cdot \text{polylog}(n))$ space.…

Data Structures and Algorithms · Computer Science 2023-12-21 Sepehr Assadi , Christian Konrad , Kheeran K. Naidu , Janani Sundaresan

We initiate the study of the algorithmic problem of certifying lower bounds on the discrepancy of random matrices: given an input matrix $A \in \mathbb{R}^{m \times n}$, output a value that is a lower bound on $\mathsf{disc}(A) = \min_{x…

Data Structures and Algorithms · Computer Science 2023-06-02 Prayaag Venkat

A new lower bound for the average probability or error for a two-user discrete memoryless (DM) multiple-access channel (MAC) is derived. This bound has a structure very similar to the well-known sphere packing packing bound derived by…

Information Theory · Computer Science 2010-10-08 Ali Nazari , S. Sandeep Pradhan , Achilleas Anastasopoulos

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

Knapsack is one of the most fundamental problems in theoretical computer science. In the $(1 - \epsilon)$-approximation setting, although there is a fine-grained lower bound of $(n + 1 / \epsilon) ^ {2 - o(1)}$ based on the $(\min,…

Data Structures and Algorithms · Computer Science 2025-08-12 Xiao Mao

We prove that, to compute a Boolean function $f$ on $N$ variables with error probability $\epsilon$, any quantum black-box algorithm has to query at least $\frac{1 - 2\sqrt{\epsilon}}{2} \rho_f N = \frac{1 - 2\sqrt{\epsilon}}{2} \bar{S}_f$…

Quantum Physics · Physics 2007-05-23 Yaoyun Shi

The classical Perceptron algorithm of Rosenblatt can be used to find a linear threshold function to correctly classify $n$ linearly separable data points, assuming the classes are separated by some margin $\gamma > 0$. A foundational result…

Machine Learning · Computer Science 2022-10-19 Guanghui Wang , Rafael Hanashiro , Etash Guha , Jacob Abernethy

Network analysis is becoming one of the most active research areas in statistics. Significant advances have been made recently on developing theories, methodologies and algorithms for analyzing networks. However, there has been little…

Statistics Theory · Mathematics 2015-11-18 Chao Gao , Yu Lu , Harrison H. Zhou

The factorization norms of the lower-triangular all-ones $n \times n$ matrix, $\gamma_2(M_{count})$ and $\gamma_{F}(M_{count})$, play a central role in differential privacy as they are used to give theoretical justification of the accuracy…

Data Structures and Algorithms · Computer Science 2025-09-19 Monika Henzinger , Nikita P. Kalinin , Jalaj Upadhyay

Given an integer array $A[1..n]$, the Range Minimum Query problem (RMQ) asks to preprocess $A$ into a data structure, supporting RMQ queries: given $a,b\in [1,n]$, return the index $i\in[a,b]$ that minimizes $A[i]$, i.e.,…

Data Structures and Algorithms · Computer Science 2021-11-05 Mingmou Liu , Huacheng Yu

We put forward a Quantum Amplitude Estimation algorithm delivering superior performance (lower quantum computational complexity and faster classical computation parts) compared to the approaches available to-date. The algorithm does not…

Quantum Physics · Physics 2024-07-25 Alet Roux , Tomasz Zastawniak

Assume that $X$ is a connected $(q+1)$-regular undirected graph of finite order $n$. Let $A$ denote the adjacency matrix of $X$. Let $\lambda_1=q+1>\lambda_2\geq \lambda_3\geq \ldots \geq \lambda_n$ denote the eigenvalues of $A$. The…

Data Structures and Algorithms · Computer Science 2021-06-10 Hau-Wen Huang

We prove a lower bound of $\Omega(n^{1/2 - c})$, for all $c>0$, on the query complexity of (two-sided error) non-adaptive algorithms for testing whether an $n$-variable Boolean function is monotone versus constant-far from monotone. This…

Computational Complexity · Computer Science 2014-12-19 Xi Chen , Anindya De , Rocco A. Servedio , Li-Yang Tan

The $L_2$-norm, or collision norm, is a core entity in the analysis of distributions and probabilistic algorithms. Batu and Canonne (FOCS 2017) presented an extensive analysis of algorithmic aspects of the $L_2$-norm and its connection to…

Data Structures and Algorithms · Computer Science 2026-03-26 Tomer Adar
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