A GRAPH-THEORETIC APPROACH to a CONJECTURE of DIXON and PRESSMAN 3 out the Computations Directly in This Setting; the Matrix L Is Too Complicated

Total Page:16

File Type:pdf, Size:1020Kb

A GRAPH-THEORETIC APPROACH to a CONJECTURE of DIXON and PRESSMAN 3 out the Computations Directly in This Setting; the Matrix L Is Too Complicated A graph-theoretic approach to a conjecture of Dixon and Pressman Matthew Brassil and Zinovy Reichstein Abstract. Given n × n matrices, A1,...,Ak, consider the linear operator L(A1,...,Ak): Matn → Matn given by L(A1,...,Ak)(Ak )= Pσ S sgn(σ)Aσ Aσ ··· Aσ k . The Amitsur- +1 ∈ k+1 (1) (2) ( +1) Levitzki theorem asserts that L(A1,...,Ak) is identically 0 for every k > 2n − 1. Dixon and Pressman conjectured that if k is an even number between 2 and 2n − 2, then the kernel of L(A1,...,Ak) is of dimension k for A1,...,Ak ∈ Matn(R) in general position. We prove this conjecture using graph-theoretic techniques. 1. Introduction Recall that the standard polynomial [A1,...,Am] in m variables is defined as [A1,...,Am]= sgn(σ)Aσ(1) ··· Aσ(m) . σX∈Sm The celebrated theorem of Amitsur and Levitzki [1] asserts that [A1,...,Am] = 0 for any m > 2n and any n × n-matrices A1,...,Am ∈ Matn(Λ) over a commutative ring Λ. The original proof in [1] is quite involved. Simpler proofs have since been given by Swan [7, 8], Razmyslov [5], Rosset [6] and, most recently, Procesi [4]. Let F be a field. For a k-tuple of matrices (A1,...,Ak) with Ai ∈ Matn(F ), let L(A1,...,Ak): Matn(F ) → Matn(F ) be the linear operator given by L(A1,...,Ak)(X) := [A1,...,Ak,X] . arXiv:2010.04679v1 [math.RA] 9 Oct 2020 Dixon and Pressman investigated the kernel of this operator in [3]. When k = 1, the kernel of L(A1) is the centralizer of A1. When k > 2n − 1, L(A1,...,Ak) is identically zero, by the Amitsur-Levitzki theorem. Conjecture 1.1 (Dixon, Pressman [3]). Suppose that 2 6 k 6 2n − 2. Then for A1,...,Ak ∈ Matn(R) in general position, the nullity d of L(A1,...,Ak) is given by (i) d = k, if k is even, 2010 Mathematics Subject Classification. 05C38, 15A24, 15A54. Key words and phrases. Amitsur-Levitsky theorem, Dixon-Pressman conjecture, directed graph, Euler- ian path, monomial order. Matthew Brassil was partially supported by a Graduate Research Fellowship from the University of British Columbia. Zinovy Reichstein was partially supported by National Sciences and Engineering Research Council of Canada Discovery grant 253424-2017. 1 2 MATTHEWBRASSILANDZINOVYREICHSTEIN (ii) d = k + 1, if k is odd and n is even, and (iii) d = k + 2, if both k and n are odd. Here, as usual, R is the field of real numbers and the nullity of a linear transformation is the dimension of its kernel. Dixon and Pressman showed that d > k, k + 1 and k +2 in cases (i), (ii), and (iii), respectively, and verified computationally that equality holds for small values of n and k. Note that one may view Conjecture 1.1 and the Amitsur-Levitsky theorem as pointing in opposite directions: Conjecture 1.1 gives an upper bound on the generic nullity of L(A1,...,Ak) for 2 6 k 6 2n − 2, whereas the Amitsur-Levitsky theorem gives a lower bound for k > 2n − 1. The purpose of this paper is to prove Conjecture 1.1 in case (i). Our main result is the following. Theorem 1.2. Let k = 2r be a positive even integer and F be an infinite field whose characteristic does not divide 2(2r + 1)r!. Assume that n > r. Then for A1, A2,...,Ak ∈ Matn(F ) in general position, the nullity of L(A1, A2,...,Ak) is k. Our proof will rely on graph-theoretic techniques. To motivate it, let us briefly recall Swan’s proof of the Amitsur-Levitsky theorem. Since the standard polynomial [A1,...,Am] is multi-linear in A1,...,Am, it suffices to show that [Ea1b1 ,...,Eambm ] = 0 for any choice of a1,b1,...,am,bm ∈ {1,...,n}, as long as m > 2n. Here Eab denotes the elementary matrix with 1 in the (a,b)-position and 0s elsewhere. As we expand [Ea1b1 ,...,Eambm ], the term sgn(σ)Eaσ(1) bσ(1) ...Eaσ(m)bσ(m) contributes sgn(σ)Eaσ(1) bσ(m) to the sum if (1.1) bσ(1) = aσ(2), bσ(2) = aσ(3), ... ,bσ(m−1) = aσ(m), and 0 otherwise. Conditions (1.1) can be conveniently rephrased in graph-theoretic terms. Let G be the directed graph with n vertices, 1,...,n and m edges, e1 = (a1,b1),...,em = (am,bm). Then conditions (1.1) hold if and only if eσ(1),...,eσ(m) form an Eulerian path on G. We will say that this Eulerian path is even if σ is an even permutation and odd otherwise. This way the Amitsur-Levitsky theorem reduces to the following graph-theoretic assertion. Theorem 1.3. (Swan [7, 8]) Let G be a directed graph with n vertices and m edges. Let a and b be two of the vertices (not necessarily distinct). If m > 2n, then the number of even Eulerian paths from a to b equals the number of odd Eulerian paths from a to b. If one were to use a similar approach to prove Conjecture 1.1, one would set n (ℓ) (1.2) Aℓ = xab Eab, a,bX=1 (ℓ) 2 2 2 for ℓ =1,...,k. Here xab are kn independent variables. Each entry of the n × n matrix of L = L(A1,...,Ak) is then a multilinear polynomial of degree k in the groups of variables, (1) (k) {xab },..., {xab }. (Here we identify the linear transformation L(A1,...,Ak): Matn → Matn with its matrix in the standard basis Eab of Matn.) The coefficient of the monomial x(1) · ... · x(k) in a given position in L can again be computed as the signed sum of a1b1 akbk Eulerian paths on a certain graph. However, for k 6 2n − 2, these signed sums will no longer be identically 0. To prove Conjecture 1.1(i) in this way, one would need to assemble these coefficients into the n2 × n2 matrix L with polynomial entries, then show that the (ℓ) nullity of L over the field F (xab ) is k (or equivalently, is 6 k). We are not able to carry A GRAPH-THEORETIC APPROACH TO A CONJECTURE OF DIXON AND PRESSMAN 3 out the computations directly in this setting; the matrix L is too complicated. To prove Theorem 1.2 we will modify this approach in the following ways. (ℓ) (1) We will specialize the matrices Aℓ by setting some of the variables xab equal to 0. For the purpose of showing that null(L) 6 k, this is sufficient. In fact, we will set n2 − n entries of each Al equal to 0; the other n entries will remain independent variables. (2) We will choose A1,...,Ak so that L(A1,...,Ak) decomposes as a direct sum L(A1,...,Ak)= L0 ⊕ L1 ⊕ ... ⊕ Ln−1, where each Li is represented by an n × n matrix. This simplifies our analysis of L and reduces the problem to showing that null(L0)+null(L1)+...+null(Ln−1) 6 k. The specific matrices we will use are described in Sections 3 and 6. (3) To get a better handle on the nullities of L0,...,Ln−1, we will replace each Lj by its “matrix of initial coefficients” Ic(Lj ) with respect to a certain lexicographic monomial order on the variables xℓ,α; see Section 5. This will further simplify the computations in two ways. First, the entries of Ic(Lj ) will be integers, rather than polynomials. These integers will be obtained by counting Eulerian paths on certain graphs, as in Swan’s argument. Secondly, passing from Lj to Ic(Lj ) will allow us to focus only on the (rather special) graphs corresponding to leading monomials. We will classify these “maximal graphs” in Sections 7 and 8 and complete the proof of Theorem 1.2 in Sections 9 and 10. The last part of the proof will rely on the computations of signed counts of Eulerian paths in Section 2. The overall structure of the paper is shown in the flowchart below. Theorem 1.2 Proposition 3.2 Proposition 6.3 Proposition 7.2 Lemma 7.1 Lemma 7.3 Lemmas 2.3, 2.4 Lemma 8.1 Proposition 9.1 Lemma 8.3 Lemma 8.2 Section 10 Lemmas 2.5, 2.6 2. Preliminaries on graphs and Eulerian paths Throughout this paper our graphs will all be directed with labeled edges and vertices. An Eulerian path in a graph Γ is a path which visits every edge exactly once. We will denote by Eula(Γ) the set of Eulerian paths on Γ which begin at a vertex a. It is easy to see that any two paths in Eula(Γ) terminate at the same vertex. For an edge • → • appearing in a graph Γ we define srcΓ(e)= a and tarΓ(e)= b to be a e b the source and target vertices of the edge e respectively. We define the outdegree outdegΓ(v) to be the number of edges in Γ whose source vertex is v, and the indegree indegΓ(v) to be 4 MATTHEWBRASSILANDZINOVYREICHSTEIN the number of edges in Γ whose target vertex is v. When the graph Γ is clear from the context we will abbreviate these terms as src(e), tar(e), outdeg(v), and indeg(v). An Eulerian path beginning and ending at the same vertex is known as an Eulerian circuit. The following fundamental theorem, due to Euler, is usually stated in terms of Eulerian circuits. In the sequel we will need a variant in terms of Eulerian paths. Theorem 2.1.
Recommended publications
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • Eulerian and Hamiltonian Paths
    Eulerian and Hamiltonian Paths 1. Euler paths and circuits 1.1. The Könisberg Bridge Problem Könisberg was a town in Prussia, divided in four land regions by the river Pregel. The regions were connected with seven bridges as shown in figure 1(a). The problem is to find a tour through the town that crosses each bridge exactly once. Leonhard Euler gave a formal solution for the problem and –as it is believed- established the graph theory field in mathematics. (a) (b) Figure 1: The bridges of Könisberg (a) and corresponding graph (b) 1.2. Defining Euler paths Obviously, the problem is equivalent with that of finding a path in the graph of figure 1(b) such that it crosses each edge exactly one time. Instead of an exhaustive search of every path, Euler found out a very simple criterion for checking the existence of such paths in a graph. As a result, paths with this property took his name. Definition 1: An Euler path is a path that crosses each edge of the graph exactly once. If the path is closed, we have an Euler circuit. In order to proceed to Euler’s theorem for checking the existence of Euler paths, we define the notion of a vertex’s degree. Definition 2: The degree of a vertex u in a graph equals to the number of edges attached to vertex u. A loop contributes 2 to its vertex’s degree. 1.3. Checking the existence of an Euler path The existence of an Euler path in a graph is directly related to the degrees of the graph’s vertices.
    [Show full text]
  • Combinatorics
    Combinatorics Introduction to graph theory Misha Lavrov ARML Practice 11/03/2013 Warm-up 1. How many (unordered) poker hands contain 3 or more aces? 2. 10-digit ISBN codes end in a \check digit": a number 0{9 or the letter X, calculated from the other 9 digits. If a typo is made in a single digit of the code, this can be detected by checking this calculation. Prove that a check digit in the range 1{9 couldn't achieve this goal. 16 3. There are 8 ways to go from (0; 0) to (8; 8) in steps of (0; +1) or (+1; 0). (Do you remember why?) How many of these paths avoid (2; 2), (4; 4), and (6; 6)? You don't have to simplify the expression you get. Warm-up Solutions 4 48 4 48 1. There are 3 · 2 hands with 3 aces, and 4 · 1 hands with 4 aces, for a total of 4560. 2. There are 109 possible ISBN codes (9 digits, which uniquely determine the check digit). If the last digit were in the range 1{9, there would be 9 · 108 possibilities for the last 9 digits. By pigeonhole, two ISBN codes would agree on the last 9 digits, so they'd only differ in the first digit, and an error in that digit couldn't be detected. 3. We use the inclusion-exclusion principle. (Note that there are, 4 12 e.g., 2 · 6 paths from (0; 0) to (8; 8) through (2; 2).) This gives us 16 412 82 428 44 − 2 − + 3 − = 3146: 8 2 6 4 2 4 2 Graphs Definitions A graph is a set of vertices, some of which are joined by edges.
    [Show full text]
  • An Eulerian Path Approach to Global Multiple Alignment for DNA
    JOURNAL OFCOMPUTATIONAL BIOLOGY Volume10, Number 6,2003 ©MaryAnn Liebert,Inc. Pp. 803–819 AnEulerian Path Approach to Global Multiple Alignment for DNASequences YU ZHANG1 andMICHAEL S.WATERMAN 2 ABSTRACT With the rapid increase in the datasetof genomesequences, the multiple sequence alignment problemis increasingly importantand frequently involves the alignmentof alargenumber ofsequences. Manyheuristic algorithmshave been proposedto improve the speed ofcom- putationand the qualityof alignment. W eintroduce anovelapproach that is fundamentally different fromall currently availablemethods. Our motivationcomes from the Eulerian methodfor fragment assembly in DNAsequencing thattransforms all DNAfragments into ade Bruijn graphand then reduces sequence assembly toa Eulerian pathproblem. The paperfocuses onglobal multiple alignmentof DNA sequences, whereentire sequences are aligned into onecon guration. Our mainresult is analgorithm with almostlinear compu- tationalspeed with respect tothe totalsize (numberof letters) ofsequences tobe aligned. Fivehundred simulated sequences (averaging500 bases per sequence andas lowas 70% pairwise identity) havebeen aligned within three minutes onapersonal computer,andthe quality ofalignment is satisfactory.As aresult, accurateand simultaneous alignmentof thousandsof long sequences within areasonableamount of time becomespossible. Data froman Arabidopsis sequencing projectis used todemonstrate the performance. Key words: multiple sequencealignment, de Bruijn graph,Eulerian path. 1.INTRODUCTION ultiplesequence
    [Show full text]
  • Graph Theory to Gain a Deeper Understanding of What’S Going On
    Graphs Graphs and trees come up everywhere. I We can view the internet as a graph (in many ways) I who is connected to whom I Web search views web pages as a graph I Who points to whom I Niche graphs (Ecology): I The vertices are species I Two vertices are connected by an edge if they compete (use the same food resources, etc.) Niche graphs describe competitiveness. I Influence Graphs I The vertices are people I There is an edge from a to b if a influences b Influence graphs give a visual representation of power structure. I Social networks: who knows whom There are lots of other examples in all fields . By viewing a situation as a graph, we can apply general results of graph theory to gain a deeper understanding of what's going on. We're going to prove some of those general results. But first we need some notation . Directed and Undirected Graphs A graph G is a pair (V ; E), where V is a set of vertices or nodes and E is a set of edges I We sometimes write G(V ; E) instead of G I We sometimes write V (G) and E(G) to denote the vertices/edges of G. In a directed graph (digraph), the edges are ordered pairs (u; v) 2 V × V : I the edge goes from node u to node v In an undirected simple graphs, the edges are sets fu; vg consisting of two nodes. I there's no direction in the edge from u to v I More general (non-simple) undirected graphs (sometimes called multigraphs) allow self-loops and multiple edges between two nodes.
    [Show full text]
  • Multi-Eulerian Tours of Directed Graphs
    Multi-Eulerian tours of directed graphs Matthew Farrell Lionel Levine∗ Department of Mathematics Cornell University Ithaca, New York, U.S.A. [email protected] http://www.math.cornell.edu/~levine Submitted: 24 September 2015. Revised 4 April 2016; Accepted: ; Published: XX Mathematics Subject Classifications: 05C05, 05C20, 05C30, 05C45, 05C50 Abstract Not every graph has an Eulerian tour. But every finite, strongly connected graph has a multi-Eulerian tour, which we define as a closed path that uses each directed edge at least once, and uses edges e and f the same number of times whenever tail(e) = tail(f). This definition leads to a simple generalization of the BEST Theorem. We then show that the minimal length of a multi-Eulerian tour is bounded in terms of the Pham index, a measure of `Eulerianness'. Keywords: BEST theorem, coEulerian digraph, Eulerian digraph, Eulerian path, Laplacian, Markov chain tree theorem, matrix-tree theorem, oriented spanning tree, period vector, Pham index, rotor walk In the following G = (V; E) denotes a finite directed graph, with loops and multiple edges permitted. An Eulerian tour of G is a closed path that traverses each directed edge exactly once. Such a tour exists only if the indegree of each vertex equals its outdegree; the graphs with this property are called Eulerian. The BEST theorem (named for its discoverers: de Bruijn, Ehrenfest [4], Smith and Tutte [12]) counts the number of such tours. The purpose of this note is to generalize the notion of Eulerian tour and the BEST theorem to any finite, strongly connected graph G.
    [Show full text]
  • Lecture Notes for IEOR 266: Graph Algorithms and Network Flows
    Lecture Notes for IEOR 266: Graph Algorithms and Network Flows Professor Dorit S. Hochbaum Contents 1 Introduction 1 1.1 Assignment problem . 1 1.2 Basic graph definitions . 2 2 Totally unimodular matrices 4 3 Minimum cost network flow problem 5 3.1 Transportation problem . 7 3.2 The maximum flow problem . 8 3.3 The shortest path problem . 8 3.4 The single source shortest paths . 9 3.5 The bipartite matching problem . 9 3.6 Summary of classes of network flow problems . 11 3.7 Negative cost cycles in graphs . 11 4 Other network problems 12 4.1 Eulerian tour . 12 4.1.1 Eulerian walk . 14 4.2 Chinese postman problem . 14 4.2.1 Undirected chinese postman problem . 14 4.2.2 Directed chinese postman problem . 14 4.2.3 Mixed chinese postman problem . 14 4.3 Hamiltonian tour problem . 14 4.4 Traveling salesman problem (TSP) . 14 4.5 Vertex packing problems (Independent Set) . 15 4.6 Maximum clique problem . 15 4.7 Vertex cover problem . 15 4.8 Edge cover problem . 16 4.9 b-factor (b-matching) problem . 16 4.10 Graph colorability problem . 16 4.11 Minimum spanning tree problem . 17 5 Complexity analysis 17 5.1 Measuring quality of an algorithm . 17 5.1.1 Examples . 18 5.2 Growth of functions . 20 i IEOR266 notes: Updated 2014 ii 5.3 Definitions for asymptotic comparisons of functions . 21 5.4 Properties of asymptotic notation . 21 5.5 Caveats of complexity analysis . 21 5.6 A sketch of the ellipsoid method . 22 6 Graph representations 23 6.1 Node-arc adjacency matrix .
    [Show full text]