Eulerian and Hamiltonian Paths
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1 Hamiltonian Path
6.S078 Fine-Grained Algorithms and Complexity MIT Lecture 17: Algorithms for Finding Long Paths (Part 1) November 2, 2020 In this lecture and the next, we will introduce a number of algorithmic techniques used in exponential-time and FPT algorithms, through the lens of one parametric problem: Definition 0.1 (k-Path) Given a directed graph G = (V; E) and parameter k, is there a simple path1 in G of length ≥ k? Already for this simple-to-state problem, there are quite a few radically different approaches to solving it faster; we will show you some of them. We’ll see algorithms for the case of k = n (Hamiltonian Path) and then we’ll turn to “parameterizing” these algorithms so they work for all k. A number of papers in bioinformatics have used quick algorithms for k-Path and related problems to analyze various networks that arise in biology (some references are [SIKS05, ADH+08, YLRS+09]). In the following, we always denote the number of vertices jV j in our given graph G = (V; E) by n, and the number of edges jEj by m. We often associate the set of vertices V with the set [n] := f1; : : : ; ng. 1 Hamiltonian Path Before discussing k-Path, it will be useful to first discuss algorithms for the famous NP-complete Hamiltonian path problem, which is the special case where k = n. Essentially all algorithms we discuss here can be adapted to obtain algorithms for k-Path! The naive algorithm for Hamiltonian Path takes time about n! = 2Θ(n log n) to try all possible permutations of the nodes (which can also be adapted to get an O?(k!)-time algorithm for k-Path, as we’ll see). -
Tutorial 13 Travelling Salesman Problem1
MTAT.03.286: Design and Analysis of Algorithms University of Tartu Tutorial 13 Instructor: Vitaly Skachek; Assistants: Behzad Abdolmaleki, Oliver-Matis Lill, Eldho Thomas. Travelling Salesman Problem1 Basic algorithm Definition 1 A Hamiltonian cycle in a graph is a simple circuit that passes through each vertex exactly once. + Problem. Let G(V; E) be a complete undirected graph, and c : E 7! R be a cost function defined on the edges. Find a minimum cost Hamiltonian cycle in G. This problem is called a travelling salesman problem. To illustrate the motivation, imagine that a salesman has a list of towns it should visit (and at the end to return to his home city), and a map, where there is a certain distance between any two towns. The salesman wants to minimize the total distance traveled. Definition 2 We say that the cost function defined on the edges satisfies a triangle inequality if for any three edges fu; vg, fu; wg and fv; wg in E it holds: c (fu; vg) + c (fv; wg) ≥ c (fu; wg) : In what follows we assume that the cost function c satisfies the triangle inequality. The main idea of the algorithm: Observe that removing an edge from the optimal travelling salesman tour leaves a path through all vertices, which contains a spanning tree. We have the following relations: cost(TSP) ≥ cost(Hamiltonian path without an edge) ≥ cost(MST) : 1Additional reading: Section 3 in the book of V.V. Vazirani \Approximation Algorithms". 1 Next, we describe how to build a travelling salesman path. Find a minimum spanning tree in the graph. -
Graph Theory 1 Introduction
6.042/18.062J Mathematics for Computer Science March 1, 2005 Srini Devadas and Eric Lehman Lecture Notes Graph Theory 1 Introduction Informally, a graph is a bunch of dots connected by lines. Here is an example of a graph: B H D A F G I C E Sadly, this definition is not precise enough for mathematical discussion. Formally, a graph is a pair of sets (V, E), where: • Vis a nonempty set whose elements are called vertices. • Eis a collection of twoelement subsets of Vcalled edges. The vertices correspond to the dots in the picture, and the edges correspond to the lines. Thus, the dotsandlines diagram above is a pictorial representation of the graph (V, E) where: V={A, B, C, D, E, F, G, H, I} E={{A, B} , {A, C} , {B, D} , {C, D} , {C, E} , {E, F } , {E, G} , {H, I}} . 1.1 Definitions A nuisance in first learning graph theory is that there are so many definitions. They all correspond to intuitive ideas, but can take a while to absorb. Some ideas have multi ple names. For example, graphs are sometimes called networks, vertices are sometimes called nodes, and edges are sometimes called arcs. Even worse, no one can agree on the exact meanings of terms. For example, in our definition, every graph must have at least one vertex. However, other authors permit graphs with no vertices. (The graph with 2 Graph Theory no vertices is the single, stupid counterexample to many wouldbe theorems— so we’re banning it!) This is typical; everyone agrees moreorless what each term means, but dis agrees about weird special cases. -
HABILITATION THESIS Petr Gregor Combinatorial Structures In
HABILITATION THESIS Petr Gregor Combinatorial Structures in Hypercubes Computer Science - Theoretical Computer Science Prague, Czech Republic April 2019 Contents Synopsis of the thesis4 List of publications in the thesis7 1 Introduction9 1.1 Hypercubes...................................9 1.2 Queue layouts.................................. 14 1.3 Level-disjoint partitions............................ 19 1.4 Incidence colorings............................... 24 1.5 Distance magic labelings............................ 27 1.6 Parity vertex colorings............................. 29 1.7 Gray codes................................... 30 1.8 Linear extension diameter........................... 36 Summary....................................... 38 2 Queue layouts 51 3 Level-disjoint partitions 63 4 Incidence colorings 125 5 Distance magic labelings 143 6 Parity vertex colorings 149 7 Gray codes 155 8 Linear extension diameter 235 3 Synopsis of the thesis The thesis is compiled as a collection of 12 selected publications on various combinatorial structures in hypercubes accompanied with a commentary in the introduction. In these publications from years between 2012 and 2018 we solve, in some cases at least partially, several open problems or we significantly improve previously known results. The list of publications follows after the synopsis. The thesis is organized into 8 chapters. Chapter 1 is an umbrella introduction that contains background, motivation, and summary of the most interesting results. Chapter 2 studies queue layouts of hypercubes. A queue layout is a linear ordering of vertices together with a partition of edges into sets, called queues, such that in each set no two edges are nested with respect to the ordering. The results in this chapter significantly improve previously known upper and lower bounds on the queue-number of hypercubes associated with these layouts. -
HAMILTONICITY in CAYLEY GRAPHS and DIGRAPHS of FINITE ABELIAN GROUPS. Contents 1. Introduction. 1 2. Graph Theory: Introductory
HAMILTONICITY IN CAYLEY GRAPHS AND DIGRAPHS OF FINITE ABELIAN GROUPS. MARY STELOW Abstract. Cayley graphs and digraphs are introduced, and their importance and utility in group theory is formally shown. Several results are then pre- sented: firstly, it is shown that if G is an abelian group, then G has a Cayley digraph with a Hamiltonian cycle. It is then proven that every Cayley di- graph of a Dedekind group has a Hamiltonian path. Finally, we show that all Cayley graphs of Abelian groups have Hamiltonian cycles. Further results, applications, and significance of the study of Hamiltonicity of Cayley graphs and digraphs are then discussed. Contents 1. Introduction. 1 2. Graph Theory: Introductory Definitions. 2 3. Cayley Graphs and Digraphs. 2 4. Hamiltonian Cycles in Cayley Digraphs of Finite Abelian Groups 5 5. Hamiltonian Paths in Cayley Digraphs of Dedekind Groups. 7 6. Cayley Graphs of Finite Abelian Groups are Guaranteed a Hamiltonian Cycle. 8 7. Conclusion; Further Applications and Research. 9 8. Acknowledgements. 9 References 10 1. Introduction. The topic of Cayley digraphs and graphs exhibits an interesting and important intersection between the world of groups, group theory, and abstract algebra and the world of graph theory and combinatorics. In this paper, I aim to highlight this intersection and to introduce an area in the field for which many basic problems re- main open.The theorems presented are taken from various discrete math journals. Here, these theorems are analyzed and given lengthier treatment in order to be more accessible to those without much background in group or graph theory. -
Lecture 9. Basic Problems of Graph Theory
Lecture 9. Basic Problems of Graph Theory. 1 / 28 Problem of the Seven Bridges of Königsberg Denition An Eulerian path in a multigraph G is a path which contains each edge of G exactly once. If the Eulerian path is closed, then it is called an Euler cycle. If a multigraph contains an Euler cycle, then is called a Eulerian graph. Example Consider graphs z z z y y y w w w t t x x t x a) b) c) in case a the multigraph has an Eulerian cycle, in case b the multigraph has an Eulerian trail, in case c the multigraph hasn't an Eulerian cycle or an Eulerian trail. 2 / 28 Problem of the Seven Bridges of Königsberg In Königsberg, there are two islands A and B on the river Pregola. They are connected each other and with shores C and D by seven bridges. You must set o from any part of the city land: A; B; C or D, go through each of the bridges exactly once and return to the starting point. In 1736 this problem had been solved by the Swiss mathematician Leonhard Euler (1707-1783). He built a graph representing this problem. Shore C River Island A Island B Shore D Leonard Euler answered the important question for citizens "has the resulting graph a closed walk, which contains all edges only once?" Of course the answer was negative. 3 / 28 Problem of the Seven Bridges of Königsberg Theorem Let G be a connected multigraph. G is Eulerian if and only if each vertex of G has even degree. -
582670 Algorithms for Bioinformatics
582670 Algorithms for Bioinformatics Lecture 5: Graph Algorithms and DNA Sequencing 27.9.2012 Adapted from slides by Veli M¨akinen/ Algorithms for Bioinformatics 2011 which are partly from http://bix.ucsd.edu/bioalgorithms/slides.php DNA Sequencing: History Sanger method (1977): Gilbert method (1977): I Labeled ddNTPs terminate I Chemical method to cleave DNA copying at random DNA at specific points (G, points. G+A, T+C, C). I Both methods generate labeled fragments of varying lengths that are further measured by electrophoresis. Sanger Method: Generating a Read 1. Divide sample into four. 2. Each sample will have available all normal nucleotides and modified nucleotides of one type (A, C, G or T) that will terminate DNA strand elongation. 3. Start at primer (restriction site). 4. Grow DNA chain. 5. In each sample the reaction will stop at all points ending with the modified nucleotide. 6. Separate products by length using gel electrophoresis. Sanger Method: Generating a Read DNA Sequencing I Shear DNA into millions of small fragments. I Read 500-700 nucleotides at a time from the small fragments (Sanger method) Fragment Assembly I Computational Challenge: assemble individual short fragments (reads) into a single genomic sequence (\superstring") I Until late 1990s the shotgun fragment assembly of human genome was viewed as intractable problem I Now there exists \complete" sequences of human genomes of several individuals I For small and \easy" genomes, such as bacterial genomes, fragment assembly is tractable with many software tools I Remains -
The Cube of Every Connected Graph Is 1-Hamiltonian
JOURNAL OF RESEARCH of the National Bureau of Standards- B. Mathematical Sciences Vol. 73B, No.1, January- March 1969 The Cube of Every Connected Graph is l-Hamiltonian * Gary Chartrand and S. F. Kapoor** (November 15,1968) Let G be any connected graph on 4 or more points. The graph G3 has as its point set that of C, and two distinct pointE U and v are adjacent in G3 if and only if the distance betwee n u and v in G is at most three. It is shown that not only is G" hamiltonian, but the removal of any point from G" still yields a hamiltonian graph. Key Words: C ube of a graph; graph; hamiltonian. Let G be a graph (finite, undirected, with no loops or multiple lines). A waLk of G is a finite alternating sequence of points and lines of G, beginning and ending with a point and where each line is incident with the points immediately preceding and followin g it. A walk in which no point is repeated is called a path; the Length of a path is the number of lines in it. A graph G is connected if between every pair of distinct points there exists a path, and for such a graph, the distance between two points u and v is defined as the length of the shortest path if u 0;1= v and zero if u = v. A walk with at least three points in which the first and last points are the same but all other points are dis tinct is called a cycle. -
Graph Algorithms in Bioinformatics an Introduction to Bioinformatics Algorithms Outline
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info Graph Algorithms in Bioinformatics An Introduction to Bioinformatics Algorithms www.bioalgorithms.info Outline 1. Introduction to Graph Theory 2. The Hamiltonian & Eulerian Cycle Problems 3. Basic Biological Applications of Graph Theory 4. DNA Sequencing 5. Shortest Superstring & Traveling Salesman Problems 6. Sequencing by Hybridization 7. Fragment Assembly & Repeats in DNA 8. Fragment Assembly Algorithms An Introduction to Bioinformatics Algorithms www.bioalgorithms.info Section 1: Introduction to Graph Theory An Introduction to Bioinformatics Algorithms www.bioalgorithms.info Knight Tours • Knight Tour Problem: Given an 8 x 8 chessboard, is it possible to find a path for a knight that visits every square exactly once and returns to its starting square? • Note: In chess, a knight may move only by jumping two spaces in one direction, followed by a jump one space in a perpendicular direction. http://www.chess-poster.com/english/laws_of_chess.htm An Introduction to Bioinformatics Algorithms www.bioalgorithms.info 9th Century: Knight Tours Discovered An Introduction to Bioinformatics Algorithms www.bioalgorithms.info 18th Century: N x N Knight Tour Problem • 1759: Berlin Academy of Sciences proposes a 4000 francs prize for the solution of the more general problem of finding a knight tour on an N x N chessboard. • 1766: The problem is solved by Leonhard Euler (pronounced “Oiler”). • The prize was never awarded since Euler was Director of Mathematics at Berlin Academy and was deemed ineligible. Leonhard Euler http://commons.wikimedia.org/wiki/File:Leonhard_Euler_by_Handmann.png An Introduction to Bioinformatics Algorithms www.bioalgorithms.info Introduction to Graph Theory • A graph is a collection (V, E) of two sets: • V is simply a set of objects, which we call the vertices of G. -
A Survey of Line Graphs and Hamiltonian Paths Meagan Field
University of Texas at Tyler Scholar Works at UT Tyler Math Theses Math Spring 5-7-2012 A Survey of Line Graphs and Hamiltonian Paths Meagan Field Follow this and additional works at: https://scholarworks.uttyler.edu/math_grad Part of the Mathematics Commons Recommended Citation Field, Meagan, "A Survey of Line Graphs and Hamiltonian Paths" (2012). Math Theses. Paper 3. http://hdl.handle.net/10950/86 This Thesis is brought to you for free and open access by the Math at Scholar Works at UT Tyler. It has been accepted for inclusion in Math Theses by an authorized administrator of Scholar Works at UT Tyler. For more information, please contact [email protected]. A SURVEY OF LINE GRAPHS AND HAMILTONIAN PATHS by MEAGAN FIELD A thesis submitted in partial fulfillment of the requirements for the degree of Master’s of Science Department of Mathematics Christina Graves, Ph.D., Committee Chair College of Arts and Sciences The University of Texas at Tyler May 2012 Contents List of Figures .................................. ii Abstract ...................................... iii 1 Definitions ................................... 1 2 Background for Hamiltonian Paths and Line Graphs ........ 11 3 Proof of Theorem 2.2 ............................ 17 4 Conclusion ................................... 34 References ..................................... 35 i List of Figures 1GraphG..................................1 2GandL(G)................................3 3Clawgraph................................4 4Claw-freegraph..............................4 5Graphwithaclawasaninducedsubgraph...............4 6GraphP .................................. 7 7AgraphCcontainingcomponents....................8 8Asimplegraph,Q............................9 9 First and second step using R2..................... 9 10 Third and fourth step using R2..................... 10 11 Graph D and its reduced graph R(D).................. 10 12 S5 and its line graph, K5 ......................... 13 13 S8 and its line graph, K8 ......................... 14 14 Line graph of graph P ......................... -
On Hamiltonian Line-Graphsq
ON HAMILTONIANLINE-GRAPHSQ BY GARY CHARTRAND Introduction. The line-graph L(G) of a nonempty graph G is the graph whose point set can be put in one-to-one correspondence with the line set of G in such a way that two points of L(G) are adjacent if and only if the corresponding lines of G are adjacent. In this paper graphs whose line-graphs are eulerian or hamiltonian are investigated and characterizations of these graphs are given. Furthermore, neces- sary and sufficient conditions are presented for iterated line-graphs to be eulerian or hamiltonian. It is shown that for any connected graph G which is not a path, there exists an iterated line-graph of G which is hamiltonian. Some elementary results on line-graphs. In the course of the article, it will be necessary to refer to several basic facts concerning line-graphs. In this section these results are presented. All the proofs are straightforward and are therefore omitted. In addition a few definitions are given. If x is a Une of a graph G joining the points u and v, written x=uv, then we define the degree of x by deg zz+deg v—2. We note that if w ' the point of L(G) which corresponds to the line x, then the degree of w in L(G) equals the degree of x in G. A point or line is called odd or even depending on whether it has odd or even degree. If G is a connected graph having at least one line, then L(G) is also a connected graph. -
Matchings Extend to Hamiltonian Cycles in 5-Cube 1
Discussiones Mathematicae Graph Theory 38 (2018) 217–231 doi:10.7151/dmgt.2010 MATCHINGS EXTEND TO HAMILTONIAN CYCLES IN 5-CUBE 1 Fan Wang 2 School of Sciences Nanchang University Nanchang, Jiangxi 330000, P.R. China e-mail: [email protected] and Weisheng Zhao Institute for Interdisciplinary Research Jianghan University Wuhan, Hubei 430056, P.R. China e-mail: [email protected] Abstract Ruskey and Savage asked the following question: Does every matching in a hypercube Qn for n ≥ 2 extend to a Hamiltonian cycle of Qn? Fink con- firmed that every perfect matching can be extended to a Hamiltonian cycle of Qn, thus solved Kreweras’ conjecture. Also, Fink pointed out that every matching can be extended to a Hamiltonian cycle of Qn for n ∈ {2, 3, 4}. In this paper, we prove that every matching in Q5 can be extended to a Hamiltonian cycle of Q5. Keywords: hypercube, Hamiltonian cycle, matching. 2010 Mathematics Subject Classification: 05C38, 05C45. 1This work is supported by NSFC (grant nos. 11501282 and 11261019) and the science and technology project of Jiangxi Provincial Department of Education (grant No. 20161BAB201030). 2Corresponding author. 218 F. Wang and W. Zhao 1. Introduction Let [n] denote the set {1,...,n}. The n-dimensional hypercube Qn is a graph whose vertex set consists of all binary strings of length n, i.e., V (Qn) = {u : u = u1 ··· un and ui ∈ {0, 1} for every i ∈ [n]}, with two vertices being adjacent whenever the corresponding strings differ in just one position. The hypercube Qn is one of the most popular and efficient interconnection networks.