Solving the Königsberg Bridge Problem
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Strongly Connected Component
Graph IV Ian Things that we would talk about ● DFS ● Tree ● Connectivity Useful website http://codeforces.com/blog/entry/16221 Recommended Practice Sites ● HKOJ ● Codeforces ● Topcoder ● Csacademy ● Atcoder ● USACO ● COCI Term in Directed Tree ● Consider node 4 – Node 2 is its parent – Node 1, 2 is its ancestors – Node 5 is its sibling – Node 6 is its child – Node 6, 7, 8 is its descendants ● Node 1 is the root DFS Forest ● When we do DFS on a graph, we would obtain a DFS forest. Noted that the graph is not necessarily a tree. ● Some of the information we get through the DFS is actually very useful, such as – Starting time of a node – Finishing time of a node – Parent of the node Some Tricks Using DFS Order ● Suppose vertex v is ancestor(not only parent) of u – Starting time of v < starting time of u – Finishing time of v > starting time of u ● st[v] < st[u] <= ft[u] < ft[v] ● O(1) to check if ancestor or not ● Flatten the tree to store subtree information(maybe using segment tree or other data structure to maintain) ● Super useful !!!!!!!!!! Partial Sum on Tree ● Given queries, each time increase all node from node v to node u by 1 ● Assume node v is ancestor of node u ● sum[u]++, sum[par[v]]-- ● Run dfs in root dfs(v) for all child u dfs(u) d[v] = d[v] + d[u] Types of Edges ● Tree edges – Edges that forms a tree ● Forward edges – Edges that go from a node to its descendants but itself is not a tree edge. -
CLRS B.4 Graph Theory Definitions Unit 1: DFS Informally, a Graph
CLRS B.4 Graph Theory Definitions Unit 1: DFS informally, a graph consists of “vertices” joined together by “edges,” e.g.,: example graph G0: 1 ···················•······························· ····························· ····························· ························· ···· ···· ························· ························· ···· ···· ························· ························· ···· ···· ························· ························· ···· ···· ························· ············· ···· ···· ·············· 2•···· ···· ···· ··· •· 3 ···· ···· ···· ···· ···· ··· ···· ···· ···· ···· ···· ···· ···· ···· ···· ···· ······· ······· ······· ······· ···· ···· ···· ··· ···· ···· ···· ···· ···· ··· ···· ···· ···· ···· ···· ···· ···· ···· ···· ···· ··············· ···· ···· ··············· 4•························· ···· ···· ························· • 5 ························· ···· ···· ························· ························· ···· ···· ························· ························· ···· ···· ························· ····························· ····························· ···················•································ 6 formally a graph is a pair (V, E) where V is a finite set of elements, called vertices E is a finite set of pairs of vertices, called edges if H is a graph, we can denote its vertex & edge sets as V (H) & E(H) respectively if the pairs of E are unordered, the graph is undirected if the pairs of E are ordered the graph is directed, or a digraph two vertices joined by an edge -
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 Graph Theory “Begin at the beginning,” the King said, gravely, “and go on till you come to the end; then stop.” — Lewis Carroll, Alice in Wonderland The Pregolya River passes through a city once known as K¨onigsberg. In the 1700s seven bridges were situated across this river in a manner similar to what you see in Figure 1.1. The city’s residents enjoyed strolling on these bridges, but, as hard as they tried, no residentof the city was ever able to walk a route that crossed each of these bridges exactly once. The Swiss mathematician Leonhard Euler learned of this frustrating phenomenon, and in 1736 he wrote an article [98] about it. His work on the “K¨onigsberg Bridge Problem” is considered by many to be the beginning of the field of graph theory. FIGURE 1.1. The bridges in K¨onigsberg. J.M. Harris et al., Combinatorics and Graph Theory , DOI: 10.1007/978-0-387-79711-3 1, °c Springer Science+Business Media, LLC 2008 2 1. Graph Theory At first, the usefulness of Euler’s ideas and of “graph theory” itself was found only in solving puzzles and in analyzing games and other recreations. In the mid 1800s, however, people began to realize that graphs could be used to model many things that were of interest in society. For instance, the “Four Color Map Conjec- ture,” introduced by DeMorgan in 1852, was a famous problem that was seem- ingly unrelated to graph theory. The conjecture stated that four is the maximum number of colors required to color any map where bordering regions are colored differently. -
Vizing's Theorem and Edge-Chromatic Graph
VIZING'S THEOREM AND EDGE-CHROMATIC GRAPH THEORY ROBERT GREEN Abstract. This paper is an expository piece on edge-chromatic graph theory. The central theorem in this subject is that of Vizing. We shall then explore the properties of graphs where Vizing's upper bound on the chromatic index is tight, and graphs where the lower bound is tight. Finally, we will look at a few generalizations of Vizing's Theorem, as well as some related conjectures. Contents 1. Introduction & Some Basic Definitions 1 2. Vizing's Theorem 2 3. General Properties of Class One and Class Two Graphs 3 4. The Petersen Graph and Other Snarks 4 5. Generalizations and Conjectures Regarding Vizing's Theorem 6 Acknowledgments 8 References 8 1. Introduction & Some Basic Definitions Definition 1.1. An edge colouring of a graph G = (V; E) is a map C : E ! S, where S is a set of colours, such that for all e; f 2 E, if e and f share a vertex, then C(e) 6= C(f). Definition 1.2. The chromatic index of a graph χ0(G) is the minimum number of colours needed for a proper colouring of G. Definition 1.3. The degree of a vertex v, denoted by d(v), is the number of edges of G which have v as a vertex. The maximum degree of a graph is denoted by ∆(G) and the minimum degree of a graph is denoted by δ(G). Vizing's Theorem is the central theorem of edge-chromatic graph theory, since it provides an upper and lower bound for the chromatic index χ0(G) of any graph G. -
Superposition and Constructions of Graphs Without Nowhere-Zero K-flows
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Europ. J. Combinatorics (2002) 23, 281–306 doi:10.1006/eujc.2001.0563 Available online at http://www.idealibrary.com on Superposition and Constructions of Graphs Without Nowhere-zero k-flows M ARTIN KOCHOL Using multi-terminal networks we build methods on constructing graphs without nowhere-zero group- and integer-valued flows. In this way we unify known constructions of snarks (nontrivial cubic graphs without edge-3-colorings, or equivalently, without nowhere-zero 4-flows) and provide new ones in the same process. Our methods also imply new complexity results about nowhere-zero flows in graphs and state equivalences of Tutte’s 3- and 5-flow conjectures with formally weaker statements. c 2002 Elsevier Science Ltd. All rights reserved. 1. INTRODUCTION Nowhere-zero flows in graphs have been introduced by Tutte [38–40]. Primarily he showed that a planar graph is face-k-colorable if and only if it admits a nowhere-zero k-flow (its edges can be oriented and assigned values ±1,..., ±(k − 1) so that the sum of the incoming values equals the sum of the outcoming ones for every vertex of the graph). Tutte also proved the classical equivalence result that a graph admits a nowhere-zero k-flow if and only if it admits a flow whose values are the nonzero elements of a finite abelian group of order k. Seymour [35] has proved that every bridgeless graph admits a nowhere-zero 6-flow, thereby improving the 8-flow theorem of Jaeger [16] and Kilpatrick [20]. -
Finding Articulation Points and Bridges Articulation Points Articulation Point
Finding Articulation Points and Bridges Articulation Points Articulation Point Articulation Point A vertex v is an articulation point (also called cut vertex) if removing v increases the number of connected components. A graph with two articulation points. 3 / 1 Articulation Points Given I An undirected, connected graph G = (V; E) I A DFS-tree T with the root r Lemma A DFS on an undirected graph does not produce any cross edges. Conclusion I If a descendant u of a vertex v is adjacent to a vertex w, then w is a descendant or ancestor of v. 4 / 1 Removing a Vertex v Assume, we remove a vertex v 6= r from the graph. Case 1: v is an articulation point. I There is a descendant u of v which is no longer reachable from r. I Thus, there is no edge from the tree containing u to the tree containing r. Case 2: v is not an articulation point. I All descendants of v are still reachable from r. I Thus, for each descendant u, there is an edge connecting the tree containing u with the tree containing r. 5 / 1 Removing a Vertex v Problem I v might have multiple subtrees, some adjacent to ancestors of v, and some not adjacent. Observation I A subtree is not split further (we only remove v). Theorem A vertex v is articulation point if and only if v has a child u such that neither u nor any of u's descendants are adjacent to an ancestor of v. Question I How do we determine this efficiently for all vertices? 6 / 1 Detecting Descendant-Ancestor Adjacency Lowpoint The lowpoint low(v) of a vertex v is the lowest depth of a vertex which is adjacent to v or a descendant of v. -
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 -
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. -
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. -
Path Finding in Graphs
Path Finding in Graphs Problem Set #2 will be posted by tonight 1 From Last Time Two graphs representing 5-mers from the sequence "GACGGCGGCGCACGGCGCAA" Hamiltonian Path: Eulerian Path: Each k-mer is a vertex. Find a path that Each k-mer is an edge. Find a path that passes through every vertex of this graph passes through every edge of this graph exactly once. exactly once. 2 De Bruijn's Problem Nicolaas de Bruijn Minimal Superstring Problem: (1918-2012) k Find the shortest sequence that contains all |Σ| strings of length k from the alphabet Σ as a substring. Example: All strings of length 3 from the alphabet {'0','1'}. A dutch mathematician noted for his many contributions in the fields of graph theory, number theory, combinatorics and logic. He solved this problem by mapping it to a graph. Note, this particular problem leads to cyclic sequence. 3 De Bruijn's Graphs Minimal Superstrings can be constructed by finding a Or, equivalently, a Eulerian cycle of a Hamiltonian path of an k-dimensional De Bruijn (k−1)-dimensional De Bruijn graph. Here edges k graph. Defined as a graph with |Σ| nodes and edges represent the k-length substrings. from nodes whose k − 1 suffix matches a node's k − 1 prefix 4 Solving Graph Problems on a Computer Graph Representations An example graph: An Adjacency Matrix: Adjacency Lists: A B C D E Edge = [(0,1), (0,4), (1,2), (1,3), A 0 1 0 0 1 (2,0), B 0 0 1 1 0 (3,0), C 1 0 0 0 0 (4,1), (4,2), (4,3)] D 1 0 0 0 0 An array or list of vertex pairs (i,j) E 0 1 1 1 0 indicating an edge from the ith vertex to the jth vertex.