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Related papers: A $(1+\epsilon)$-Approximation for Ultrametric Emb…

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We present a dynamic algorithm for maintaining $(1+\epsilon)$-approximate maximum eigenvector and eigenvalue of a positive semi-definite matrix $A$ undergoing \emph{decreasing} updates, i.e., updates which may only decrease eigenvalues.…

Data Structures and Algorithms · Computer Science 2025-01-07 Deeksha Adil , Thatchaphol Saranurak

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

Sampling edges from a graph in sublinear time is a fundamental problem and a powerful subroutine for designing sublinear-time algorithms. Suppose we have access to the vertices of the graph and know a constant-factor approximation to the…

Data Structures and Algorithms · Computer Science 2022-11-15 Talya Eden , Shyam Narayanan , Jakub Tětek

We study optimization problems that are neither approximable in polynomial time (at least with a constant factor) nor fixed parameter tractable, under widely believed complexity assumptions. Specifically, we focus on Maximum Independent…

Data Structures and Algorithms · Computer Science 2008-10-29 Marek Cygan , Lukasz Kowalik , Marcin Pilipczuk , Mateusz Wykurz

In this paper, we design new sublinear-time algorithms for solving the gap edit distance problem and for embedding edit distance to Hamming distance. For the gap edit distance problem, we give an $\tilde{O}(\frac{n}{k}+k^2)$-time greedy…

Data Structures and Algorithms · Computer Science 2020-11-17 Tomasz Kociumaka , Barna Saha

Real-world data typically contain repeated and periodic patterns. This suggests that they can be effectively represented and compressed using only a few coefficients of an appropriate basis (e.g., Fourier, Wavelets, etc.). However, distance…

Machine Learning · Statistics 2014-05-26 Michail Vlachos , Nikolaos Freris , Anastasios Kyrillidis

We consider the problem of augmenting an n-vertex graph embedded in a metric space, by inserting one additional edge in order to minimize the diameter of the resulting graph. We present exact algorithms for the cases when (i) the input…

Computational Geometry · Computer Science 2016-07-20 Ulrike Große , Joachim Gudmundsson , Christian Knauer , Michiel Smid , Fabian Stehn

We provide an algorithm that maintains, against an adaptive adversary, a $(1-\varepsilon)$-approximate maximum matching in $n$-node $m$-edge general (not necessarily bipartite) undirected graph undergoing edge deletions with high…

Data Structures and Algorithms · Computer Science 2024-12-05 Jiale Chen , Aaron Sidford , Ta-Wei Tu

We consider the NP-hard problem of minimizing a separable concave quadratic function over the integral points in a polyhedron, and we denote by D the largest absolute value of the subdeterminants of the constraint matrix. In this paper we…

Optimization and Control · Mathematics 2019-08-30 Alberto Del Pia

Let $X$ be a set of $n$ points of norm at most $1$ in the Euclidean space $R^k$, and suppose $\varepsilon>0$. An $\varepsilon$-distance sketch for $X$ is a data structure that, given any two points of $X$ enables one to recover the square…

Metric Geometry · Mathematics 2017-04-04 Noga Alon , Bo'az Klartag

Geometric matching is an important topic in computational geometry and has been extensively studied over decades. In this paper, we study a geometric-matching problem, known as geometric many-to-many matching. In this problem, the input is…

Computational Geometry · Computer Science 2024-03-06 Sayan Bandyapadhyay , Jie Xue

Subspace learning and matrix factorization problems have great many applications in science and engineering, and efficient algorithms are critical as dataset sizes continue to grow. Many relevant problem formulations are non-convex, and in…

Numerical Analysis · Computer Science 2022-02-22 Dejiao Zhang , Laura Balzano

We consider the problem of matching a metric space $(X,d_X)$ of size $k$ with a subspace of a metric space $(Y,d_Y)$ of size $n \geq k$, assuming that these two spaces have constant doubling dimension $\delta$. More precisely, given an…

Data Structures and Algorithms · Computer Science 2020-12-22 Corentin Allair , Antoine Vigneron

We consider the popular $k$-means problem in $d$-dimensional Euclidean space. Recently Friggstad, Rezapour, Salavatipour [FOCS'16] and Cohen-Addad, Klein, Mathieu [FOCS'16] showed that the standard local search algorithm yields a…

Data Structures and Algorithms · Computer Science 2017-08-30 Vincent Cohen-Addad

This paper addresses the problem of finding a B-term wavelet representation of a given discrete function $f \in \real^n$ whose distance from f is minimized. The problem is well understood when we seek to minimize the Euclidean distance…

Data Structures and Algorithms · Computer Science 2007-07-23 Sudipto Guha , Boulos Harb

In this paper, we consider the problem of approximating the densest subgraph in the dynamic graph stream model. In this model of computation, the input graph is defined by an arbitrary sequence of edge insertions and deletions and the goal…

Data Structures and Algorithms · Computer Science 2015-06-16 Andrew McGregor , David Tench , Sofya Vorotnikova , Hoa T. Vu

In the \textsc{2-Dimensional Knapsack} problem (2DK) we are given a square knapsack and a collection of $n$ rectangular items with integer sizes and profits. Our goal is to find the most profitable subset of items that can be packed…

Computational Geometry · Computer Science 2021-03-19 Waldo Gálvez , Fabrizio Grandoni , Arindam Khan , Diego Ramírez-Romero , Andreas Wiese

We initiate the study of diameter computation in geometric intersection graphs from the fine-grained complexity perspective. A geometric intersection graph is a graph whose vertices correspond to some shapes in $d$-dimensional Euclidean…

Computational Geometry · Computer Science 2022-03-11 Karl Bringmann , Sándor Kisfaludi-Bak , Marvin Künnemann , André Nusser , Zahra Parsaeian

Johnson-Lindenstrauss embeddings are widely used to reduce the dimension and thus the processing time of data. To reduce the total complexity, also fast algorithms for applying these embeddings are necessary. To date, such fast algorithms…

Data Structures and Algorithms · Computer Science 2020-04-30 Stefan Bamberger , Felix Krahmer

We show how to compute any symmetric Boolean function on $n$ variables over any field (as well as the integers) with a probabilistic polynomial of degree $O(\sqrt{n \log(1/\epsilon)})$ and error at most $\epsilon$. The degree dependence on…

Data Structures and Algorithms · Computer Science 2016-11-18 Josh Alman , Ryan Williams