DOCSLIB.ORG

Graph Theory, an Antiprism Graph Is a Graph That Has One of the Antiprisms As Its Skeleton

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Graph Theory, an Antiprism Graph Is a Graph That Has One of the Antiprisms As Its Skeleton Graph families From Wikipedia, the free encyclopedia Chapter 1 Antiprism graph In the mathematical field of graph theory, an antiprism graph is a graph that has one of the antiprisms as its skeleton. An n-sided antiprism has 2n vertices and 4n edges. They are regular, polyhedral (and therefore by necessity also 3- vertex-connected, vertex-transitive, and planar graphs), and also Hamiltonian graphs.[1] 1.1 Examples The first graph in the sequence, the octahedral graph, has 6 vertices and 12 edges. Later graphs in the sequence may be named after the type of antiprism they correspond to: • Octahedral graph – 6 vertices, 12 edges • square antiprismatic graph – 8 vertices, 16 edges • Pentagonal antiprismatic graph – 10 vertices, 20 edges • Hexagonal antiprismatic graph – 12 vertices, 24 edges • Heptagonal antiprismatic graph – 14 vertices, 28 edges • Octagonal antiprismatic graph– 16 vertices, 32 edges • ... Although geometrically the star polygons also form the faces of a different sequence of (self-intersecting) antiprisms, the star antiprisms, they do not form a different sequence of graphs. 1.2 Related graphs An antiprism graph is a special case of a circulant graph, Ci₂n(2,1). Other infinite sequences of polyhedral graph formed in a similar way from polyhedra with regular-polygon bases include the prism graphs (graphs of prisms) and wheel graphs (graphs of pyramids). Other vertex-transitive polyhedral graphs include the Archimedean graphs. 1.3 References [1] Read, R. C. and Wilson, R. J. An Atlas of Graphs, Oxford, England: Oxford University Press, 2004 reprint, Chapter 6 special graphs pp. 261, 270. 2 1.4. EXTERNAL LINKS 3 1.4 External links • Weisstein, Eric W., “Antiprism graph”, MathWorld. Chapter 2 Aperiodic graph In the mathematical area of graph theory, a directed graph is said to be aperiodic if there is no integer k > 1 that divides the length of every cycle of the graph. Equivalently, a graph is aperiodic if the greatest common divisor of the lengths of its cycles is one; this greatest common divisor for a graph G is called the period of G. 2.1 Graphs that cannot be aperiodic In any directed bipartite graph, all cycles have a length that is divisible by two. Therefore, no directed bipartite graph can be aperiodic. In any directed acyclic graph, it is a vacuous truth that every k divides all cycles (because there are no directed cycles to divide) so no directed acyclic graph can be aperiodic. And in any directed cycle graph, there is only one cycle, so every cycle’s length is divisible by n, the length of that cycle. 2.2 Testing for aperiodicity Suppose that G is strongly connected and that k divides the lengths of all cycles in G. Consider the results of performing a depth-first search of G, starting at any vertex, and assigning each vertex v to a set Vi where i is the length (taken mod k) of the path in the depth-first search tree from the root to v. It can be shown (Jarvis & Shier 1996) that this partition into sets Vi has the property that each edge in the graph goes from a set Vi to another set V₍i ₊ ₁₎ ₒ k. Conversely, if a partition with this property exists for a strongly connected graph G, k must divide the lengths of all cycles in G. Thus, we may find the period of a strongly connected graph G by the following steps: • Perform a depth-first search of G • For each e in G that connects a vertex on level i of the depth-first search tree to a vertex on level j, let ke = j - i - 1. • Compute the greatest common divisor of the set of numbers ke. The graph is aperiodic if and only if the period computed in this fashion is 1. If G is not strongly connected, we may perform a similar computation in each strongly connected component of G, ignoring the edges that pass from one strongly connected component to another. Jarvis and Shier describe a very similar algorithm using breadth first search in place of depth-first search; the advantage of depth-first search is that the strong connectivity analysis can be incorporated into the same search. 2.3 Applications In a strongly connected graph, if one defines a Markov chain on the vertices, in which the probability of transitioning from v to w is nonzero if and only if there is an edge from v to w, then this chain is aperiodic if and only if the graph 4 2.3. APPLICATIONS 5 An aperiodic graph. The cycles in this graph have lengths 5 and 6; therefore, there is no k > 1 that divides all cycle lengths. 6 CHAPTER 2. APERIODIC GRAPH A strongly connected graph with period three. is aperiodic. A Markov chain in which all states are recurrent has a strongly connected state transition graph, and the Markov chain is aperiodic if and only if this graph is aperiodic. Thus, aperiodicity of graphs is a useful concept in analyzing the aperiodicity of Markov chains. Aperiodicity is also an important necessary condition for solving the road coloring problem. According to the solution of this problem (Trahtman 2009), a strongly connected directed graph in which all vertices have the same outdegree has a synchronizable edge coloring if and only if it is aperiodic. 2.4 References • Jarvis, J. P.; Shier, D. R. (1996), “Graph-theoretic analysis of finite Markov chains”, in Shier, D. R.; Wallenius, K. T., Applied Mathematical Modeling: A Multidisciplinary Approach (PDF), CRC Press. • Trahtman, Avraham N. (2009), “The road coloring problem”, Israel Journal of Mathematics 172 (1): 51–60, arXiv:0709.0099, doi:10.1007/s11856-009-0062-5. Chapter 3 Apex graph An apex graph. The subgraph formed by removing the red vertex is planar. 7 8 CHAPTER 3. APEX GRAPH In graph theory, a branch of mathematics, an apex graph is a graph that can be made planar by the removal of a single vertex. The deleted vertex is called an apex of the graph. We say an apex, not the apex because an apex graph may have more than one apex (for example, in the minimal nonplanar graphs K5 or K₃,₃, every vertex is an apex). The apex graphs include graphs that are themselves planar, in which case again every vertex is an apex. The null graph is also counted as an apex graph even though it has no vertex to remove. Apex graphs are closed under the operation of taking minors and play a role in several other aspects of graph minor theory: linkless embedding,[1] Hadwiger’s conjecture,[2] YΔY-reducible graphs,[3] and relations between treewidth and graph diameter.[4] 3.1 Characterization and recognition Apex graphs are closed under the operation of taking minors: contracting any edge, or removing any edge or vertex, leads to another apex graph. For, if G is an apex graph with apex v, then any contraction or removal that does not involve v preserves the planarity of the remaining graph, as does any edge removal of an edge incident to v. If an edge incident to v is contracted, the effect on the remaining graph is equivalent to the removal of the other endpoint of the edge. And if v itself is removed, any other vertex may be chosen as the apex.[5] Because they form a minor-closed family of graphs, the apex graphs have a forbidden graph characterization: there exists a finite set A of minor-minimal non-apex graphs such that a graph is an apex graph if and only if it does not contain as a minor any graph in A. The forbidden minors for the apex graphs include the seven graphs of the Petersen family, three disconnected graphs formed from the disjoint unions of two of K5 and K₃,₃, and many other graphs. However, a complete description of the graphs in A remains unknown.[5][6] Despite the unknown set of forbidden minors, it is possible to test whether a given graph is an apex graph, and if so, to find an apex for the graph, in linear time. More generally, for any fixed constant k, it is possible to recognize in linear time the k-apex graphs, the graphs in which the removal of some carefully chosen set of at most k vertices leads to a planar graph.[7] If k is variable, however, the problem is NP-complete.[8] 3.2 Chromatic number Every apex graph has chromatic number at most five: the underlying planar graph requires at most four colors by the four color theorem, and the remaining vertex needs at most one additional color. Robertson, Seymour & Thomas (1993a) used this fact in their proof of the case k = 6 of the Hadwiger conjecture, the statement that every 6-chromatic graph has the complete graph K6 as a minor: they showed that any minimal counterexample to the conjecture would have to be an apex graph, but since there are no 6-chromatic apex graphs such a counterexample cannot exist. Jørgensen (1994) conjectured that every 6-vertex-connected graph that does not have K6 as a minor must be an apex graph. If this were proved, the Robertson–Seymour–Thomas result on the Hadwiger conjecture would be an immediate consequence.[2] 3.3 Local treewidth A graph family F has bounded local treewidth if the graphs in F obey a functional relationship between diameter and treewidth: there exists a function ƒ such that the treewidth of a diameter-d graph in F is at most ƒ(d). The apex graphs do not have bounded local treewidth: the apex graphs formed by connecting an apex vertex to every vertex of an n × n grid graph have treewidth n and diameter 2, so the treewidth is not bounded by a function of diameter for these graphs.
Load more

Recommended publications
  • The Number of Spanning Trees in Apollonian Networks
    The Number of Spanning Trees in Apollonian Networks Zhongzhi Zhang, Bin Wu School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai 200433, China ([email protected]). Francesc Comellas Dep. Matem`atica Aplicada IV, EETAC, Universitat Polit`ecnica de Catalunya, c/ Esteve Terradas 5, Castelldefels (Barcelona), Catalonia, Spain ([email protected]). Abstract In this paper we find an exact analytical expression for the number of span- ning trees in Apollonian networks. This parameter can be related to signif- icant topological and dynamic properties of the networks, including perco- lation, epidemic spreading, synchronization, and random walks. As Apol- lonian networks constitute an interesting family of maximal planar graphs which are simultaneously small-world, scale-free, Euclidean and space fill- ing, modular and highly clustered, the study of their spanning trees is of particular relevance. Our results allow also the calculation of the spanning tree entropy of Apollonian networks, which we compare with those of other graphs with the same average degree. Key words: Apollonian networks, spanning trees, small-world graphs, complex networks, self-similar, maximally planar, scale-free 1. Apollonian networks In the process known as Apollonian packing [9], which dates back to Apollonius of Perga (c262{c190 BC), we start with three mutually tangent circles, and draw their inner Soddy circle (tangent to the three circles). Next we draw the inner Soddy circles of this circle with each pair of the original three, and the process is iterated, see Fig. 1. An Apollonian packing can be used to design a graph, when each circle is associated to a vertex of the graph and vertices are connected if their corresponding circles are tangent.
    [Show full text]
  • Pdf 143.41 K
    ISSN: 1017-060X (Print) ISSN: 1735-8515 (Online) Bulletin of the Iranian Mathematical Society Vol. 43 (2017), No. 7, pp. 2281{2292 . Title: On the fixed number of graphs Author(s): I. Javaid, M. Murtaza, M. Asif and F. Iftikhar Published by the Iranian Mathematical Society http://bims.ims.ir Bull. Iranian Math. Soc. Vol. 43 (2017), No. 7, pp. 2281{2292 Online ISSN: 1735-8515 ON THE FIXED NUMBER OF GRAPHS I. JAVAID∗, M. MURTAZA, M. ASIF AND F. IFTIKHAR (Communicated by Ali Reza Ashrafi) Abstract. A set of vertices S of a graph G is called a fixing set of G, if only the trivial automorphism of G fixes every vertex in S. The fixing number of a graph is the smallest cardinality of a fixing set. The fixed number of a graph G is the minimum k, such that every k-set of vertices of G is a fixing set of G. A graph G is called a k-fixed graph, if its fixing number and fixed number are both k. In this paper, we study the fixed number of a graph and give a construction of a graph of higher fixed number from a graph of lower fixed number. We find the bound on k in terms of the diameter d of a distance-transitive k-fixed graph. Keywords: Fixing set, stabilizer, fixing number, fixed number. MSC(2010): Primary: 05C25; Secondary: 05C60. 1. Introduction Let G = (V (G);E(G)) be a connected graph of order n. The degree of a vertex v in G, denoted by degG(v), is the number of edges that are incident to v in G.
    [Show full text]
  • COMPUTING the TOPOLOGICAL INDICES for CERTAIN FAMILIES of GRAPHS 1Saba Sultan, 2Wajeb Gharibi, 2Ali Ahmad 1Abdus Salam School of Mathematical Sciences, Govt
    Sci.Int.(Lahore),27(6),4957-4961,2015 ISSN 1013-5316; CODEN: SINTE 8 4957 COMPUTING THE TOPOLOGICAL INDICES FOR CERTAIN FAMILIES OF GRAPHS 1Saba Sultan, 2Wajeb Gharibi, 2Ali Ahmad 1Abdus Salam School of Mathematical Sciences, Govt. College University, Lahore, Pakistan. 2College of Computer Science & Information Systems, Jazan University, Jazan, KSA. [email protected], [email protected], [email protected] ABSTRACT. There are certain types of topological indices such as degree based topological indices, distance based topological indices and counting related topological indices etc. Among degree based topological indices, the so-called atom-bond connectivity (ABC), geometric arithmetic (GA) are of vital importance. These topological indices correlate certain physico-chemical properties such as boiling point, stability and strain energy etc. of chemical compounds. In this paper, we compute formulas of General Randi´c index ( ) for different values of α , First zagreb index, atom-bond connectivity (ABC) index, geometric arithmetic GA index, the fourth ABC index ( ABC4 ) , fifth GA index ( GA5 ) for certain families of graphs. Key words: Atom-bond connectivity (ABC) index, Geometric-arithmetic (GA) index, ABC4 index, GA5 index. 1. INTRODUCTION AND PRELIMINARY RESULTS Cheminformatics is a new subject which relates chemistry, is connected graph with vertex set V(G) and edge set E(G), du mathematics and information science in a significant manner. The is the degree of vertex and primary application of cheminformatics is the storage, indexing and search of information relating to compounds. Graph theory has provided a vital role in the aspect of indexing. The study of ∑ Quantitative structure-activity (QSAR) models predict biological activity using as input different types of structural parameters of where .
    [Show full text]
  • Linear K-Arboricities on Trees
    Discrete Applied Mathematics 103 (2000) 281–287 View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Note Linear k-arboricities on trees Gerard J. Changa; 1, Bor-Liang Chenb, Hung-Lin Fua, Kuo-Ching Huangc; ∗;2 aDepartment of Applied Mathematics, National Chiao Tung University, Hsinchu 300, Taiwan bDepartment of Business Administration, National Taichung Institue of Commerce, Taichung 404, Taiwan cDepartment of Applied Mathematics, Providence University, Shalu 433, Taichung, Taiwan Received 31 October 1997; revised 15 November 1999; accepted 22 November 1999 Abstract For a ÿxed positive integer k, the linear k-arboricity lak (G) of a graph G is the minimum number ‘ such that the edge set E(G) can be partitioned into ‘ disjoint sets and that each induces a subgraph whose components are paths of lengths at most k. This paper studies linear k-arboricity from an algorithmic point of view. In particular, we present a linear-time algo- rithm to determine whether a tree T has lak (T)6m. ? 2000 Elsevier Science B.V. All rights reserved. Keywords: Linear forest; Linear k-forest; Linear arboricity; Linear k-arboricity; Tree; Leaf; Penultimate vertex; Algorithm; NP-complete 1. Introduction All graphs in this paper are simple, i.e., ÿnite, undirected, loopless, and without multiple edges. A linear k-forest is a graph whose components are paths of length at most k.Alinear k-forest partition of G is a partition of the edge set E(G) into linear k-forests. The linear k-arboricity of G, denoted by lak (G), is the minimum size of a linear k-forest partition of G.
    [Show full text]
  • Orbit Polynomial of Graphs Versus Polynomial with Integer Coefficients
    S S symmetry Article Orbit Polynomial of Graphs versus Polynomial with Integer Coefficients Modjtaba Ghorbani 1,* , Maryam Jalali-Rad 1 and Matthias Dehmer 2,3,4 1 Department of Mathematics, Faculty of Science, Shahid Rajaee Teacher Training University, Tehran 16785-136, Iran; [email protected] 2 Department of Computer Science, Swiss Distance University of Applied Sciences, 3900 Brig, Switzerland; [email protected] 3 Department of Biomedical Computer Science and Mechatronics, UMIT, A-6060 Hall in Tyrol, Austria 4 College of Artficial Intelligence, Nankai University, Tianjin 300071, China * Correspondence: [email protected]; Tel.: +98-21-22970029 Abstract: Suppose ai indicates the number of orbits of size i in graph G. A new counting polynomial, i namely an orbit polynomial, is defined as OG(x) = ∑i aix . Its modified version is obtained by subtracting the orbit polynomial from 1. In the present paper, we studied the conditions under which an integer polynomial can arise as an orbit polynomial of a graph. Additionally, we surveyed graphs with a small number of orbits and characterized several classes of graphs with respect to their orbit polynomials. Keywords: orbit; group action; polynomial roots; orbit-stabilizer theorem 1. Introduction Citation: Ghorbani, M.; Jalali-Rad, M.; Dehmer, M. Orbit Polynomial of By having the orbits and their structures in a graph, we can infer many algebraic prop- Graphs versus Polynomial with erties about the automorphism group and thus about the similar vertices. For example, the Integer Coefficients. Symmetry 2021, length of orbits of a network gives provides important information about each individual 13, 710. https://doi.org/10.3390/ component in the network.
    [Show full text]
  • Extremal Problems for Convex Polygons ∗
    Extremal Problems for Convex Polygons ∗ Charles Audet Ecole´ Polytechnique de Montreal´ Pierre Hansen HEC Montreal´ Fred´ eric´ Messine ENSEEIHT-IRIT Abstract. Consider a convex polygon Vn with n sides, perimeter Pn, diameter Dn, area An, sum of distances between vertices Sn and width Wn. Minimizing or maximizing any of these quantities while fixing another defines ten pairs of extremal polygon problems (one of which usually has a trivial solution or no solution at all). We survey research on these problems, which uses geometrical reasoning increasingly complemented by global optimization meth- ods. Numerous open problems are mentioned, as well as series of test problems for global optimization and nonlinear programming codes. Keywords: polygon, perimeter, diameter, area, sum of distances, width, isoperimeter problem, isodiametric problem. 1. Introduction Plane geometry is replete with extremal problems, many of which are de- scribed in the book of Croft, Falconer and Guy [12] on Unsolved problems in geometry. Traditionally, such problems have been solved, some since the Greeks, by geometrical reasoning. In the last four decades, this approach has been increasingly complemented by global optimization methods. This allowed solution of larger instances than could be solved by any one of these two approaches alone. Probably the best known type of such problems are circle packing ones: given a geometrical form such as a unit square, a unit-side triangle or a unit- diameter circle, find the maximum radius and configuration of n circles which can be packed in its interior (see [46] for a recent survey and the site [44] for a census of exact and approximate results with up to 300 circles).
    [Show full text]
  • Bijective Proofs for Schur Function Identities Which Imply Dodgson's Condensation Formula and Plücker Relations
    Bijective proofs for Schur function identities which imply Dodgson’s condensation formula and Pl¨ucker relations Markus Fulmek Institut f¨urMathematik der Universit¨atWien Strudlhofgasse 4, A-1090 Wien, Austria [email protected] Michael Kleber Massachusetts Institute of Technology 77 Massachusetts Avenue, Cambridge, MA 02139, USA [email protected] Submitted: July 3, 2000; Accepted: March 7, 2001. MR Subject Classifications: 05E05 05E15 Abstract We present a “method” for bijective proofs for determinant identities, which is based on translating determinants to Schur functions by the Jacobi–Trudi identity. We illustrate this “method” by generalizing a bijective construction (which was first used by Goulden) to a class of Schur function identities, from which we shall obtain bijective proofs for Dodgson’s condensation formula, Pl¨ucker relations and a recent identity of the second author. 1 Introduction Usually, bijective proofs of determinant identities involve the following steps (cf., e.g, [19, Chapter 4] or [23, 24]): Expansion of the determinant as sum over the symmetric group, • Interpretation of this sum as the generating function of some set of combinatorial • objects which are equipped with some signed weight, Construction of an explicit weight– and sign–preserving bijection between the re- • spective combinatorial objects, maybe supported by the construction of a sign– reversing involution for certain objects. the electronic journal of combinatorics 8 (2001), #R16 1 Here, we will present another “method” of bijective proofs for determinant identitities, which involves the following steps: First, we replace the entries ai;j of the determinants by hλ i+j (where hm denotes i− • the m–th complete homogeneous function), Second, by the Jacobi–Trudi identity we transform the original determinant identity • into an equivalent identity for Schur functions, Third, we obtain a bijective proof for this equivalent identity by using the interpre- • tation of Schur functions in terms of nonintersecting lattice paths.
    [Show full text]
  • Ζ−1 Using Theorem 1.2
    UC San Diego UC San Diego Electronic Theses and Dissertations Title Ihara zeta functions of irregular graphs Permalink https://escholarship.org/uc/item/3ws358jm Author Horton, Matthew D. Publication Date 2006 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA, SAN DIEGO Ihara zeta functions of irregular graphs A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Mathematics by Matthew D. Horton Committee in charge: Professor Audrey Terras, Chair Professor Mihir Bellare Professor Ron Evans Professor Herbert Levine Professor Harold Stark 2006 Copyright Matthew D. Horton, 2006 All rights reserved. The dissertation of Matthew D. Horton is ap- proved, and it is acceptable in quality and form for publication on micro¯lm: Chair University of California, San Diego 2006 iii To my wife and family Never hold discussions with the monkey when the organ grinder is in the room. |Sir Winston Churchill iv TABLE OF CONTENTS Signature Page . iii Dedication . iv Table of Contents . v List of Figures . vii List of Tables . viii Acknowledgements . ix Vita ...................................... x Abstract of the Dissertation . xi 1 Introduction . 1 1.1 Preliminaries . 1 1.2 Ihara zeta function of a graph . 4 1.3 Simplifying assumptions . 8 2 Poles of the Ihara zeta function . 10 2.1 Bounds on the poles . 10 2.2 Relations among the poles . 13 3 Recovering information . 17 3.1 The hope . 17 3.2 Recovering Girth . 18 3.3 Chromatic polynomials and Ihara zeta functions . 20 4 Relations among Ihara zeta functions .
    [Show full text]
  • Geometric Algorithms for Optimal Airspace Design and Air Traffic
    Geometric Algorithms for Optimal Airspace Design and Air Traffic Controller Workload Balancing AMITABH BASU Carnegie Mellon University JOSEPH S. B. MITCHELL Stony Brook University and GIRISHKUMAR SABHNANI Stony Brook University The National Airspace System (NAS) is designed to accommodate a large number of flights over North America. For purposes of workload limitations for air traffic controllers, the airspace is partitioned into approximately 600 sectors; each sector is observed by one or more controllers. In order to satisfy workload limitations for controllers, it is important that sectors be designed carefully according to the traffic patterns of flights, so that no sector becomes overloaded. We formulate and study the airspace sectorization problem from an algorithmic point of view, model- ing the problem of optimal sectorization as a geometric partition problem with constraints. The novelty of the problem is that it partitions data consisting of trajectories of moving points, rather than static point set partitioning that is commonly studied. First, we formulate and solve the 1D version of the problem, showing how to partition a line into “sectors” (intervals) according to historical trajectory data. Then, we apply the 1D solution framework to design a 2D sectoriza- tion heuristic based on binary space partitions. We also devise partitions based on balanced “pie partitions” of a convex polygon. We evaluate our 2D algorithms experimentally, applying our algorithms to actual historical flight track data for the NAS. We compare the workload balance of our methods to that of the existing set of sectors for the NAS and find that our resectorization yields competitive and improved workload balancing.
    [Show full text]
  • Some Families of Convex Polytopes Labeled by 3-Total Edge Product Cordial Labeling
    Punjab University Journal of Mathematics (ISSN 1016-2526) Vol. 49(3)(2017) pp. 119-132 Some Families of Convex Polytopes Labeled by 3-Total Edge Product Cordial Labeling Umer Ali Department of Mathematics, UMT Lahore, Pakistan. Email: [email protected] Muhammad bilal Department of Mathematics, UMT Lahore, Pakistan. Email: [email protected] Sohail Zafar Department of Mathematics, UMT Lahore, Pakistan. Email: [email protected] Zohaib Zahid Department of Mathematics, UMT Lahore, Pakistan. Email: zohaib [email protected] Received: 03 January, 2017 / Accepted: 10 April, 2017 / Published online: 18 August, 2017 Abstract. For a graph G = (VG;EG), consider a mapping h : EG ! ∗ f0; 1; 2; : : : ; k − 1g, 2 ≤ k ≤ jEGj which induces a mapping h : VG ! ∗ Qn f0; 1; 2; : : : ; k − 1g such that h (v) = i=1 h(ei)( mod k), where ei is an edge incident to v. Then h is called k-total edge product cordial ( k- TEPC) labeling of G if js(i) − s(j)j ≤ 1 for all i; j 2 f1; 2; : : : ; k − 1g: Here s(i) is the sum of all vertices and edges labeled by i. In this paper, we study k-TEPC labeling for some families of convex polytopes for k = 3. AMS (MOS) Subject Classification Codes: 05C07 Key Words: 3-TEPC labeling, The graphs of convex polytopes. 119 120 Umer Ali, Muhammad Bilal, Sohail Zafar and Zohaib Zahid 1. INTRODUCTION AND PRELIMINARIES Let G be an undirected, simple and finite graph with vertex-set VG and edge-set EG. Order of a graph G is the number of vertices and size of a graph G is the number of edges.
    [Show full text]
  • Coloring Copoints of a Planar Point Set
    Coloring Copoints of a Planar Point Set Walter Morris Department of Mathematical Sciences George Mason University 4400 University Drive, Fairfax, VA 22030, USA Abstract To a set of n points in the plane, one can associate a graph that has less than n2 vertices and has the property that k-cliques in the graph correspond vertex sets of convex k-gons in the point set. We prove an upper bound of 2k¡1 on the size of a planar point set for which the graph has chromatic number k, matching the bound con- jectured by Szekeres for the clique number. Constructions of Erd}os and Szekeres are shown to yield graphs that have very low chromatic number. The constructions are carried out in the context of pseudoline arrangements. 1 Introduction Let X be a ¯nite set of points in IR2. We will assume that X is in general position, that is, no three points of X are on a line. A subset C of X is called closed if C = K \ X for some convex subset K of IR2. The set C of closed subsets of X, partially ordered by inclusion, is a lattice. If x 2 X and A is a closed subset of X that does not contain x and is inclusion-maximal among all closed subsets of X that do not contain x, then A is called a copoint of X attached to x. In the more general context of antimatroids, or convex geometries, co- points have been studied in [1], [2] and [3]. It is shown in these references that every copoint is attached to a unique point of X, and that the copoints are the meet-irreducible elements of the lattice of closed sets.
    [Show full text]
  • Ethnomathematics and Education in Africa
    Copyright ©2014 by Paulus Gerdes www.lulu.com http://www.lulu.com/spotlight/pgerdes 2 Paulus Gerdes Second edition: ISTEG Belo Horizonte Boane Mozambique 2014 3 First Edition (January 1995): Institutionen för Internationell Pedagogik (Institute of International Education) Stockholms Universitet (University of Stockholm) Report 97 Second Edition (January 2014): Instituto Superior de Tecnologias e Gestão (ISTEG) (Higher Institute for Technology and Management) Av. de Namaacha 188, Belo Horizonte, Boane, Mozambique Distributed by: www.lulu.com http://www.lulu.com/spotlight/pgerdes Author: Paulus Gerdes African Academy of Sciences & ISTEG, Mozambique C.P. 915, Maputo, Mozambique ([email protected]) Photograph on the front cover: Detail of a Tonga basket acquired, in January 2014, by the author in Inhambane, Mozambique 4 CONTENTS page Preface (2014) 11 Chapter 1: Introduction 13 Chapter 2: Ethnomathematical research: preparing a 19 response to a major challenge to mathematics education in Africa Societal and educational background 19 A major challenge to mathematics education 21 Ethnomathematics Research Project in Mozambique 23 Chapter 3: On the concept of ethnomathematics 29 Ethnographers on ethnoscience 29 Genesis of the concept of ethnomathematics among 31 mathematicians and mathematics teachers Concept, accent or movement? 34 Bibliography 39 Chapter 4: How to recognize hidden geometrical thinking: 45 a contribution to the development of an anthropology of mathematics Confrontation 45 Introduction 46 First example 47 Second example
    [Show full text]