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Given a n points in two dimensional space, a Manhattan Network G is a network that connects all n points with either horizontal or vertical edges, with the property that for any two point in G should be connected by a Manhattan path and…

Computational Geometry · Computer Science 2024-03-19 Md. Musfiqur Rahman Sanim , Safrunnesa Saira , Fatin Faiaz Ahsan , Rajon Bardhan , S. M. Ferdous

We study integer linear programs (ILP) of the form $\min\{c^\top x\ \vert\ Ax=b,l\le x\le u,x\in\mathbb Z^n\}$ and analyze their parameterized complexity with respect to their distance to the generalized matching problem, following the…

Computational Complexity · Computer Science 2025-10-20 Alexandra Lassota , Koen Ligthart

We consider the resolution of learning problems involving $\ell_0$-regularization via Branch-and-Bound (BnB) algorithms. These methods explore regions of the feasible space of the problem and check whether they do not contain solutions…

Optimization and Control · Mathematics 2024-06-07 Theo Guyard , Cédric Herzet , Clément Elvira , Ayşe-Nur Arslan

In this paper we present a collection of results pertaining to haplotyping. The first set of results concerns the combinatorial problem of reconstructing haplotypes from incomplete and/or imperfectly sequenced haplotype data. More…

Genomics · Quantitative Biology 2007-05-23 Rudi Cilibrasi , Leo van Iersel , Steven Kelk , John Tromp

We show NP-hardness of the minimum latency scheduling (MLS) problem under the physical model of wireless networking. In this model a transmission is received successfully if the Signal to Interference-plus-Noise Ratio (SINR), is above a…

Networking and Internet Architecture · Computer Science 2012-03-14 Henry Lin , Frans Schalekamp

Score-based algorithms that learn Bayesian Network (BN) structures provide solutions ranging from different levels of approximate learning to exact learning. Approximate solutions exist because exact learning is generally not applicable to…

Artificial Intelligence · Computer Science 2020-12-02 Zhigao Guo , Anthony C. Constantinou

We study the generalized load-balancing (GLB) problem, where we are given $n$ jobs, each of which needs to be assigned to one of $m$ unrelated machines with processing times $\{p_{ij}\}$. Under a job assignment $\sigma$, the load of each…

Data Structures and Algorithms · Computer Science 2022-02-15 Shichuan Deng , Jian Li , Yuval Rabani

Given a pair of graphs with the same number of vertices, the inexact graph matching problem consists in finding a correspondence between the vertices of these graphs that minimizes the total number of induced edge disagreements. We study…

Machine Learning · Statistics 2020-07-06 Jesús Arroyo , Daniel L. Sussman , Carey E. Priebe , Vince Lyzinski

Oligonucleotide arrays are used in a wide range of genomic analyses, such as gene expression profiling, comparative genomic hybridization, chromatin immunoprecipitation, SNP detection, etc. During fabrication, the sites of an…

Emerging Technologies · Computer Science 2011-10-19 Dragos Trinca , Sanguthevar Rajasekaran

The Golomb ruler problem is defined as follows: Given a positive integer n, locate n marks on a ruler such that the distance between any two distinct pair of marks are different from each other and the total length of the ruler is…

Optimization and Control · Mathematics 2019-06-11 Burak Kocuk , Willem-Jan van Hoeve

Locally Checkable Labeling (LCL) problems are graph problems in which a solution is correct if it satisfies some given constraints in the local neighborhood of each node. Example problems in this class include maximal matching, maximal…

Distributed, Parallel, and Cluster Computing · Computer Science 2022-08-22 Alkida Balliu , Sebastian Brandt , Manuela Fischer , Rustam Latypov , Yannic Maus , Dennis Olivetti , Jara Uitto

The perceived advantage of machine learning (ML) models is that they are flexible and can incorporate a large number of features. However, many of these are typically correlated or dependent, and incorporating all of them can hinder model…

Applications · Statistics 2025-03-11 Anwesha Bhattacharyya , Yaqun Wang , Joel Vaughan , Vijayan N. Nair

We give a new framework for solving the fundamental problem of low-rank matrix completion, i.e., approximating a rank-$r$ matrix $\mathbf{M} \in \mathbb{R}^{m \times n}$ (where $m \ge n$) from random observations. First, we provide an…

Machine Learning · Computer Science 2023-08-08 Jonathan A. Kelner , Jerry Li , Allen Liu , Aaron Sidford , Kevin Tian

Graph neural networks (GNNs) have been widely investigated in the field of semi-supervised graph machine learning. Most methods fail to exploit adequate graph information when labeled data is limited, leading to the problem of…

Machine Learning · Computer Science 2023-03-15 Linxuan Song , Wenxuan Tu , Sihang Zhou , Xinwang Liu , En Zhu

We propose a boundary neuron method with random features (BNM-RF) for solving partial differential equations. The method approximates the unknown boundary function by a shallow network within the boundary integral formulation. With randomly…

Numerical Analysis · Mathematics 2026-03-30 Ye Lin , Wentao Liu , Young Ju Lee , Jiwei Jia

The problem of fast items retrieval from a fixed collection is often encountered in most computer science areas, from operating system components to databases and user interfaces. We present an approach based on hash tables that focuses on…

Neural and Evolutionary Computing · Computer Science 2020-07-17 Dan Domnita , Ciprian Oprisa

Exact MLE for generalized linear mixed models (GLMMs) is a long-standing problem unsolved until today. The proposed research solves the problem. In this problem, the main difficulty is caused by intractable integrals in the likelihood…

Methodology · Statistics 2024-10-14 Tonglin Zhang

A matching cut of a graph is a partition of its vertex set in two such that no vertex has more than one neighbor across the cut. The Matching Cut problem asks if a graph has a matching cut. This problem, and its generalization d-cut, has…

Data Structures and Algorithms · Computer Science 2024-07-04 Guilherme C. M. Gomes , Emanuel Juliano , Gabriel Martins , Vinicius F. dos Santos

We consider the problem of solving mixed random linear equations with $k$ components. This is the noiseless setting of mixed linear regression. The goal is to estimate multiple linear models from mixed samples in the case where the labels…

Machine Learning · Computer Science 2016-08-23 Xinyang Yi , Constantine Caramanis , Sujay Sanghavi

Given a finite metric space $(X\cup Y, \mathbf{d})$ the $k$-median problem is to find a set of $k$ centers $C\subseteq Y$ that minimizes $\sum_{p\in X} \min_{c\in C} \mathbf{d}(p,c)$. In general metrics, the best polynomial time algorithm…

Data Structures and Algorithms · Computer Science 2026-03-26 Anne Driemel , Jan Höckendorff , Ioannis Psarros , Christian Sohler , Di Yue