The Maximum Clique and Vertex Coloring

2018 ◽  
pp. 1-31
Author(s):  
Oleksandra Yezerska ◽  
Sergiy Butenko
Keyword(s):  
Maximum Clique ◽  
Vertex Coloring

Solving the Maximum Clique and Vertex Coloring Problems on Very Large Sparse Networks

2015 ◽  
Vol 27 (1) ◽  
pp. 164-177 ◽  
Author(s):  
Anurag Verma ◽  
Austin Buchanan ◽  
Sergiy Butenko
Keyword(s):  
Maximum Clique ◽  
Vertex Coloring ◽  
Sparse Networks

The Maximum Clique and Vertex Coloring

2018 ◽  
pp. 1259-1289
Author(s):  
Oleksandra Yezerska ◽  
Sergiy Butenko
Keyword(s):  
Maximum Clique ◽  
Vertex Coloring

scholarly journals Using Machine Learning for Quantum Annealing Accuracy Prediction

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 187
Author(s):  
Aaron Barbosa ◽  
Elijah Pelofske ◽  
Georg Hahn ◽  
Hristo N. Djidjev
Keyword(s):  
Machine Learning ◽  
Maximum Clique ◽  
Classification Model ◽  
Maximum Clique Problem ◽  
Problem Instance ◽  
Np Hard ◽  
Machine Learning Classification ◽  
Hard Problems ◽  
Problem Instances ◽  
D Wave

Quantum annealers, such as the device built by D-Wave Systems, Inc., offer a way to compute solutions of NP-hard problems that can be expressed in Ising or quadratic unconstrained binary optimization (QUBO) form. Although such solutions are typically of very high quality, problem instances are usually not solved to optimality due to imperfections of the current generations quantum annealers. In this contribution, we aim to understand some of the factors contributing to the hardness of a problem instance, and to use machine learning models to predict the accuracy of the D-Wave 2000Q annealer for solving specific problems. We focus on the maximum clique problem, a classic NP-hard problem with important applications in network analysis, bioinformatics, and computational chemistry. By training a machine learning classification model on basic problem characteristics such as the number of edges in the graph, or annealing parameters, such as the D-Wave’s chain strength, we are able to rank certain features in the order of their contribution to the solution hardness, and present a simple decision tree which allows to predict whether a problem will be solvable to optimality with the D-Wave 2000Q. We extend these results by training a machine learning regression model that predicts the clique size found by D-Wave.

scholarly journals Indirect identification of horizontal gene transfer

2021 ◽  
Vol 83 (1) ◽  
Author(s):  
David Schaller ◽  
Manuel Lafond ◽  
Peter F. Stadler ◽  
Nicolas Wieseke ◽  
Marc Hellmuth
Keyword(s):  
Gene Transfer ◽  
Horizontal Gene Transfer ◽  
Polynomial Time ◽  
Divergence Time ◽  
Recognition Algorithm ◽  
Vertex Coloring ◽  
Implicit Methods ◽  
Colored Graphs ◽  
Wide Range ◽  
Complete Multipartite

AbstractSeveral implicit methods to infer horizontal gene transfer (HGT) focus on pairs of genes that have diverged only after the divergence of the two species in which the genes reside. This situation defines the edge set of a graph, the later-divergence-time (LDT) graph, whose vertices correspond to genes colored by their species. We investigate these graphs in the setting of relaxed scenarios, i.e., evolutionary scenarios that encompass all commonly used variants of duplication-transfer-loss scenarios in the literature. We characterize LDT graphs as a subclass of properly vertex-colored cographs, and provide a polynomial-time recognition algorithm as well as an algorithm to construct a relaxed scenario that explains a given LDT. An edge in an LDT graph implies that the two corresponding genes are separated by at least one HGT event. The converse is not true, however. We show that the complete xenology relation is described by an rs-Fitch graph, i.e., a complete multipartite graph satisfying constraints on the vertex coloring. This class of vertex-colored graphs is also recognizable in polynomial time. We finally address the question “how much information about all HGT events is contained in LDT graphs” with the help of simulations of evolutionary scenarios with a wide range of duplication, loss, and HGT events. In particular, we show that a simple greedy graph editing scheme can be used to efficiently detect HGT events that are implicitly contained in LDT graphs.

scholarly journals EPTAS and Subexponential Algorithm for Maximum Clique on Disk and Unit Ball Graphs

2021 ◽  
Vol 68 (2) ◽  
pp. 1-38
Author(s):  
Marthe Bonamy ◽  
Édouard Bonnet ◽  
Nicolas Bousquet ◽  
Pierre Charbit ◽  
Panos Giannopoulos ◽  
...  
Keyword(s):  
Unit Ball ◽  
Maximum Clique ◽  
Subexponential Algorithm

An efficient resource allocation algorithm based on vertex coloring to mitigate interference among coexisting WBANs

2019 ◽  
Vol 151 ◽  
pp. 132-146 ◽  
Author(s):  
Mohammadhasan Miri ◽  
Kamal Mohamedpour ◽  
Yousef Darmani ◽  
Mahasweta Sarkar ◽  
R. Lal Tummala
Keyword(s):  
Resource Allocation ◽  
Vertex Coloring ◽  
Resource Allocation Algorithm ◽  
Allocation Algorithm ◽  
Efficient Resource

A DNA computer model for solving vertex coloring problem

2006 ◽  
Vol 51 (20) ◽  
pp. 2541-2549 ◽  
Author(s):  
Jin Xu ◽  
Xiaoli Qiang ◽  
Fang Gang ◽  
Kang Zhou
Keyword(s):  
Computer Model ◽  
Vertex Coloring ◽  
Coloring Problem ◽  
Vertex Coloring Problem ◽  
Dna Computer
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