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The study of quantum circuit simulation using classical computers is a key research topic that helps define the boundary of verifiable quantum advantage, solve quantum many-body problems, and inform development of quantum hardware and…

Quantum Physics · Physics 2026-02-05 Benjamin N. Miller , Peter K. Elgee , Jason R. Pruitt , Kevin C. Cox

We show how to construct $(1+\varepsilon)$-spanner over a set $P$ of $n$ points in $\mathbb{R}^d$ that is resilient to a catastrophic failure of nodes. Specifically, for prescribed parameters $\vartheta,\varepsilon \in (0,1)$, the computed…

Computational Geometry · Computer Science 2020-05-27 Kevin Buchin , Sariel Har-Peled , Daniel Olah

We initiate the study of approximation algorithms and computational barriers for constructing sparse $\alpha$-navigable graphs [IX23, DGM+24], a core primitive underlying recent advances in graph-based nearest neighbor search. Given an…

Data Structures and Algorithms · Computer Science 2025-07-21 Sanjeev Khanna , Ashwin Padaki , Erik Waingarten

Highly connected and yet sparse graphs (such as expanders or graphs of high treewidth) are fundamental, widely applicable and extensively studied combinatorial objects. We initiate the study of such highly connected graphs that are, in…

Computational Geometry · Computer Science 2013-06-17 Prosenjit Bose , Vida Dujmovic , Pat Morin , Michiel Smid

Given an undirected possibly weighted $n$-vertex graph $G=(V,E)$ and a set $\mathcal{P}\subseteq V^2$ of pairs, a subgraph $S=(V,E')$ is called a ${\cal P}$-pairwise $\alpha$-spanner of $G$, if for every pair $(u,v)\in\mathcal{P}$ we have…

Data Structures and Algorithms · Computer Science 2023-11-27 Ofer Neiman , Idan Shabat

Recently, numerous sparse hardware accelerators for Deep Neural Networks (DNNs), Graph Neural Networks (GNNs), and scientific computing applications have been proposed. A common characteristic among all of these accelerators is that they…

We give algorithms for geometric graph problems in the modern parallel models inspired by MapReduce. For example, for the Minimum Spanning Tree (MST) problem over a set of points in the two-dimensional space, our algorithm computes a…

Data Structures and Algorithms · Computer Science 2014-01-07 Alexandr Andoni , Aleksandar Nikolov , Krzysztof Onak , Grigory Yaroslavtsev

A fundamental procedure in the analysis of massive datasets is the construction of similarity graphs. Such graphs play a key role for many downstream tasks, including clustering, classification, graph learning, and nearest neighbor search.…

Machine Learning · Computer Science 2023-01-11 CJ Carey , Jonathan Halcrow , Rajesh Jayaram , Vahab Mirrokni , Warren Schudy , Peilin Zhong

Expander graphs play a central role in graph theory and algorithms. With a number of powerful algorithmic tools developed around them, such as the Cut-Matching game, expander pruning, expander decomposition, and algorithms for decremental…

Data Structures and Algorithms · Computer Science 2022-12-12 Julia Chuzhoy

Over the past decade, there has been increasing interest in distributed/parallel algorithms for processing large-scale graphs. By now, we have quite fast algorithms -- usually sublogarithmic-time and often $poly(\log\log n)$-time, or even…

Data Structures and Algorithms · Computer Science 2021-06-30 Amartya Shankha Biswas , Michal Dory , Mohsen Ghaffari , Slobodan Mitrović , Yasamin Nazari

It has long been known, since the classical work of (Arora, Karger, Karpinski, JCSS~99), that \MC\ admits a PTAS on dense graphs, and more generally, \kCSP\ admits a PTAS on "dense" instances with $\Omega(n^k)$ constraints. In this paper we…

Computational Complexity · Computer Science 2015-07-17 Dimitris Fotakis , Michael Lampis , Vangelis Th. Paschos

Recently [Bhattacharya et al., STOC 2015] provide the first non-trivial algorithm for the densest subgraph problem in the streaming model with additions and deletions to its edges, i.e., for dynamic graph streams. They present a…

Data Structures and Algorithms · Computer Science 2015-07-30 Hossein Esfandiari , MohammadTaghi Hajiaghayi , David P. Woodruff

The computational equivalence between approximate counting and sampling is well established for polynomial-time algorithms. The most efficient general reduction from counting to sampling is achieved via simulated annealing, where the…

Data Structures and Algorithms · Computer Science 2026-04-03 David G. Harris , Vladimir Kolmogorov , Hongyang Liu , Yitong Yin , Yiyao Zhang

We give an approximation algorithm for non-uniform sparsest cut with the following guarantee: For any $\epsilon,\delta \in (0,1)$, given cost and demand graphs with edge weights $C, D$ respectively, we can find a set $T\subseteq V$ with…

Data Structures and Algorithms · Computer Science 2015-03-19 Venkatesan Guruswami , Ali Kemal Sinop

Energy efficiency of hardware accelerators of deep neural networks (DNN) can be improved by introducing approximate arithmetic circuits. In order to quantify the error introduced by using these circuits and avoid the expensive hardware…

Distributed, Parallel, and Cluster Computing · Computer Science 2020-07-03 Filip Vaverka , Vojtech Mrazek , Zdenek Vasicek , Lukas Sekanina

We describe an algorithm that takes as input n points in the plane and a parameter {\epsilon}, and produces as output an embedded planar graph having the given points as a subset of its vertices in which the graph distances are a (1 +…

Computational Geometry · Computer Science 2016-03-22 Glencora Borradaile , David Eppstein

Distributed algorithms are often beset by the straggler effect, where the slowest compute nodes in the system dictate the overall running time. Coding-theoretic techniques have been recently proposed to mitigate stragglers via algorithmic…

Machine Learning · Statistics 2017-11-21 Zachary Charles , Dimitris Papailiopoulos , Jordan Ellenberg

An emulator is a fast-to-evaluate statistical approximation of a detailed mathematical model (simulator). When used in lieu of simulators, emulators can expedite tasks that require many repeated evaluations, such as sensitivity analyses,…

Graph sketching is a powerful paradigm for analyzing graph structure via linear measurements introduced by Ahn, Guha, and McGregor (SODA'12) that has since found numerous applications in streaming, distributed computing, and massively…

Data Structures and Algorithms · Computer Science 2022-09-30 Sepehr Assadi , Michael Kapralov , Huacheng Yu

A $k$-additive spanner of a graph is a subgraph that preserves the distance between any two nodes up to a total additive error of $+k$. Efficient algorithms have been devised for constructing 2 [Aingworth et al. SIAM '99], 6 [Baswana et al.…

Data Structures and Algorithms · Computer Science 2021-09-21 Bandar Al-Dhalaan
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