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We consider the problem of designing sublinear time algorithms for estimating the cost of a minimum metric traveling salesman (TSP) tour. Specifically, given access to a $n \times n$ distance matrix $D$ that specifies pairwise distances…

Data Structures and Algorithms · Computer Science 2020-06-11 Yu Chen , Sampath Kannan , Sanjeev Khanna

We study an abstract optimization problem arising from biomolecular sequence analysis. For a sequence A of pairs (a_i,w_i) for i = 1,..,n and w_i>0, a segment A(i,j) is a consecutive subsequence of A starting with index i and ending with…

Data Structures and Algorithms · Computer Science 2007-05-23 Michael H. Goldwasser , Ming-Yang Kao , Hsueh-I Lu

In order to build a large scale quantum computer, one must be able to correct errors extremely fast. We design a fast decoding algorithm for topological codes to correct for Pauli errors and erasure and combination of both errors and…

Quantum Physics · Physics 2021-12-08 Nicolas Delfosse , Naomi H. Nickerson

We consider the problem of computing compact routing tables for a (weighted) planar graph $G:= (V, E,w)$ in the PRAM, CONGEST, and the novel HYBRID communication model. We present algorithms with polylogarithmic work and communication that…

Data Structures and Algorithms · Computer Science 2023-05-15 Jinfeng Dou , Thorsten Götte , Henning Hillebrandt , Christian Scheideler , Julian Werthmann

In this note, we study the expander decomposition problem in a more general setting where the input graph has positively weighted edges and nonnegative demands on its vertices. We show how to extend the techniques of Chuzhoy et al. (FOCS…

Data Structures and Algorithms · Computer Science 2021-06-04 Jason Li , Thatchaphol Saranurak

We show a linear-size reduction from gap Max-2-Lin(2) (a generalization of the gap $\mathrm{Max}$-$\mathrm{Cut}$ problem) to $\gamma\text{-}\mathrm{CVP}_p$ for $\gamma = \mathrm{O}(1)$ and finite $p\geq 1$, as well as a no-go theorem…

Computational Complexity · Computer Science 2026-03-03 Jeremy Ahrens Huang , Young Kun Ko , Chunhao Wang

We study the Multiple Cluster Scheduling problem and the Multiple Strip Packing problem. For both problems, there is no algorithm with approximation ratio better than $2$ unless $P = NP$. In this paper, we present an algorithm with…

Data Structures and Algorithms · Computer Science 2019-02-12 Klaus Jansen , Malin Rau

In this paper, we obtain a number of new simple pseudo-polynomial time algorithms on the well-known knapsack problem, focusing on the running time dependency on the number of items $n$, the maximum item weight $w_\mathrm{max}$, and the…

Data Structures and Algorithms · Computer Science 2024-01-30 Qizheng He , Zhean Xu

We study the influence of a graph parameter called modular-width on the time complexity for optimally solving well-known polynomial problems such as Maximum Matching, Triangle Counting, and Maximum $s$-$t$ Vertex-Capacitated Flow. The…

Data Structures and Algorithms · Computer Science 2018-04-27 Stefan Kratsch , Florian Nelles

It was recently found that there are very close connections between the existence of additive spanners (subgraphs where all distances are preserved up to an additive stretch), distance preservers (subgraphs in which demand pairs have their…

Data Structures and Algorithms · Computer Science 2016-07-21 Eden Chlamtáč , Michael Dinitz , Guy Kortsarz , Bundit Laekhanukit

An approximate sparse recovery system in ell_1 norm formally consists of parameters N, k, epsilon an m-by-N measurement matrix, Phi, and a decoding algorithm, D. Given a vector, x, where x_k denotes the optimal k-term approximation to x,…

Data Structures and Algorithms · Computer Science 2011-07-15 Ely Porat , Martin J. Strauss

We study the space complexity of sketching cuts and Laplacian quadratic forms of graphs. We show that any data structure which approximately stores the sizes of all cuts in an undirected graph on $n$ vertices up to a $1+\epsilon$ error must…

Data Structures and Algorithms · Computer Science 2018-01-01 Charles Carlson , Alexandra Kolla , Nikhil Srivastava , Luca Trevisan

We investigate the high-dimensional regression problem using adjacency matrices of unbalanced expander graphs. In this frame, we prove that the $\ell_{2}$-prediction error and the $\ell_{1}$-risk of the lasso and the Dantzig selector are…

Statistics Theory · Mathematics 2015-03-17 Yohann de Castro

Change-point problems have appeared in a great many applications for example cancer genetics, econometrics and climate change. Modern multiscale type segmentation methods are considered to be a statistically efficient approach for multiple…

Computation · Statistics 2018-05-04 Chengcheng Huang , Housen Li , Lizhi Cheng , Wei Peng

We show that Set Cover on instances with $N$ elements cannot be approximated within $(1-\gamma)\ln N$-factor in time exp($N^{\gamma-\delta})$, for any $0 < \gamma < 1$ and any $\delta > 0$, assuming the Exponential Time Hypothesis. This…

Data Structures and Algorithms · Computer Science 2020-08-13 Marek Cygan , Magnús M. Halldórsson , Guy Kortsarz

We present a polynomial-time $(\alpha_{GW} + \varepsilon)$-approximation algorithm for the Maximum Cut problem on interval graphs and split graphs, where $\alpha_{GW} \approx 0.878$ is the approximation guarantee of the Goemans-Williamson…

Data Structures and Algorithms · Computer Science 2025-07-15 Jungho Ahn , Ian DeHaan , Eun Jung Kim , Euiwoong Lee

We consider the problem of approximating the arboricity of a graph $G= (V,E)$, which we denote by $\mathsf{arb}(G)$, in sublinear time, where the arboricity of a graph is the minimal number of forests required to cover its edges. An…

Data Structures and Algorithms · Computer Science 2021-10-29 Talya Eden , Saleet Mossel , Dana Ron

Let $\epsilon \in (0, 1)$ and $n, \Delta \in \mathbb N$ be such that $\Delta = \Omega\left(\max\left\{\frac{\log n}{\epsilon},\, \left(\frac{1}{\epsilon}\log \frac{1}{\epsilon}\right)^2\right\}\right)$. Given an $n$-vertex $m$-edge simple…

Data Structures and Algorithms · Computer Science 2025-02-14 Abhishek Dhawan

Effective Resistance (ER) is a fundamental tool in various graph learning tasks. In this paper, we address the problem of efficiently approximating ER on a graph $\mathcal{G}=(\mathcal{V},\mathcal{E})$ with $n$ vertices and $m$ edges.…

Data Structures and Algorithms · Computer Science 2025-07-08 Yichun Yang , Rong-Hua Li , Meihao Liao , Guoren Wang

We provide an algorithm for properly learning mixtures of two single-dimensional Gaussians without any separability assumptions. Given $\tilde{O}(1/\varepsilon^2)$ samples from an unknown mixture, our algorithm outputs a mixture that is…

Data Structures and Algorithms · Computer Science 2014-05-20 Constantinos Daskalakis , Gautam Kamath