Maximum Matching in Bipartite Graphs
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On the Archimedean Or Semiregular Polyhedra
ON THE ARCHIMEDEAN OR SEMIREGULAR POLYHEDRA Mark B. Villarino Depto. de Matem´atica, Universidad de Costa Rica, 2060 San Jos´e, Costa Rica May 11, 2005 Abstract We prove that there are thirteen Archimedean/semiregular polyhedra by using Euler’s polyhedral formula. Contents 1 Introduction 2 1.1 RegularPolyhedra .............................. 2 1.2 Archimedean/semiregular polyhedra . ..... 2 2 Proof techniques 3 2.1 Euclid’s proof for regular polyhedra . ..... 3 2.2 Euler’s polyhedral formula for regular polyhedra . ......... 4 2.3 ProofsofArchimedes’theorem. .. 4 3 Three lemmas 5 3.1 Lemma1.................................... 5 3.2 Lemma2.................................... 6 3.3 Lemma3.................................... 7 4 Topological Proof of Archimedes’ theorem 8 arXiv:math/0505488v1 [math.GT] 24 May 2005 4.1 Case1: fivefacesmeetatavertex: r=5. .. 8 4.1.1 At least one face is a triangle: p1 =3................ 8 4.1.2 All faces have at least four sides: p1 > 4 .............. 9 4.2 Case2: fourfacesmeetatavertex: r=4 . .. 10 4.2.1 At least one face is a triangle: p1 =3................ 10 4.2.2 All faces have at least four sides: p1 > 4 .............. 11 4.3 Case3: threefacesmeetatavertes: r=3 . ... 11 4.3.1 At least one face is a triangle: p1 =3................ 11 4.3.2 All faces have at least four sides and one exactly four sides: p1 =4 6 p2 6 p3. 12 4.3.3 All faces have at least five sides and one exactly five sides: p1 =5 6 p2 6 p3 13 1 5 Summary of our results 13 6 Final remarks 14 1 Introduction 1.1 Regular Polyhedra A polyhedron may be intuitively conceived as a “solid figure” bounded by plane faces and straight line edges so arranged that every edge joins exactly two (no more, no less) vertices and is a common side of two faces. -
Vertex Cover Might Be Hard to Approximate to Within 2 Ε − Subhash Khot ∗ Oded Regev †
Vertex Cover Might be Hard to Approximate to within 2 ε − Subhash Khot ∗ Oded Regev † Abstract Based on a conjecture regarding the power of unique 2-prover-1-round games presented in [Khot02], we show that vertex cover is hard to approximate within any constant factor better than 2. We actually show a stronger result, namely, based on the same conjecture, vertex cover on k-uniform hypergraphs is hard to approximate within any constant factor better than k. 1 Introduction Minimum vertex cover is the problem of finding the smallest set of vertices that touches all the edges in a given graph. This is one of the most fundamental NP-complete problems. A simple 2- approximation algorithm exists for this problem: construct a maximal matching by greedily adding edges and then let the vertex cover contain both endpoints of each edge in the matching. It can be seen that the resulting set of vertices indeed touches all the edges and that its size is at most twice the size of the minimum vertex cover. However, despite considerable efforts, state of the art techniques can only achieve an approximation ratio of 2 o(1) [16, 21]. − Given this state of affairs, one might strongly suspect that vertex cover is NP-hard to approxi- mate within 2 ε for any ε> 0. This is one of the major open questions in the field of approximation − algorithms. In [18], H˚astad showed that approximating vertex cover within constant factors less 7 than 6 is NP-hard. This factor was recently improved by Dinur and Safra [10] to 1.36. -
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. -
Extended Branch Decomposition Graphs: Structural Comparison of Scalar Data
Eurographics Conference on Visualization (EuroVis) 2014 Volume 33 (2014), Number 3 H. Carr, P. Rheingans, and H. Schumann (Guest Editors) Extended Branch Decomposition Graphs: Structural Comparison of Scalar Data Himangshu Saikia, Hans-Peter Seidel, Tino Weinkauf Max Planck Institute for Informatics, Saarbrücken, Germany Abstract We present a method to find repeating topological structures in scalar data sets. More precisely, we compare all subtrees of two merge trees against each other – in an efficient manner exploiting redundancy. This provides pair-wise distances between the topological structures defined by sub/superlevel sets, which can be exploited in several applications such as finding similar structures in the same data set, assessing periodic behavior in time-dependent data, and comparing the topology of two different data sets. To do so, we introduce a novel data structure called the extended branch decomposition graph, which is composed of the branch decompositions of all subtrees of the merge tree. Based on dynamic programming, we provide two highly efficient algorithms for computing and comparing extended branch decomposition graphs. Several applications attest to the utility of our method and its robustness against noise. 1. Introduction • We introduce the extended branch decomposition graph: a novel data structure that describes the hierarchical decom- Structures repeat in both nature and engineering. An example position of all subtrees of a join/split tree. We abbreviate it is the symmetric arrangement of the atoms in molecules such with ‘eBDG’. as Benzene. A prime example for a periodic process is the • We provide a fast algorithm for computing an eBDG. Typ- combustion in a car engine where the gas concentration in a ical runtimes are in the order of milliseconds. -
Archimedean Solids
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln MAT Exam Expository Papers Math in the Middle Institute Partnership 7-2008 Archimedean Solids Anna Anderson University of Nebraska-Lincoln Follow this and additional works at: https://digitalcommons.unl.edu/mathmidexppap Part of the Science and Mathematics Education Commons Anderson, Anna, "Archimedean Solids" (2008). MAT Exam Expository Papers. 4. https://digitalcommons.unl.edu/mathmidexppap/4 This Article is brought to you for free and open access by the Math in the Middle Institute Partnership at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in MAT Exam Expository Papers by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. Archimedean Solids Anna Anderson In partial fulfillment of the requirements for the Master of Arts in Teaching with a Specialization in the Teaching of Middle Level Mathematics in the Department of Mathematics. Jim Lewis, Advisor July 2008 2 Archimedean Solids A polygon is a simple, closed, planar figure with sides formed by joining line segments, where each line segment intersects exactly two others. If all of the sides have the same length and all of the angles are congruent, the polygon is called regular. The sum of the angles of a regular polygon with n sides, where n is 3 or more, is 180° x (n – 2) degrees. If a regular polygon were connected with other regular polygons in three dimensional space, a polyhedron could be created. In geometry, a polyhedron is a three- dimensional solid which consists of a collection of polygons joined at their edges. The word polyhedron is derived from the Greek word poly (many) and the Indo-European term hedron (seat). -
Exploiting C-Closure in Kernelization Algorithms for Graph Problems
Exploiting c-Closure in Kernelization Algorithms for Graph Problems Tomohiro Koana Technische Universität Berlin, Algorithmics and Computational Complexity, Germany [email protected] Christian Komusiewicz Philipps-Universität Marburg, Fachbereich Mathematik und Informatik, Marburg, Germany [email protected] Frank Sommer Philipps-Universität Marburg, Fachbereich Mathematik und Informatik, Marburg, Germany [email protected] Abstract A graph is c-closed if every pair of vertices with at least c common neighbors is adjacent. The c-closure of a graph G is the smallest number such that G is c-closed. Fox et al. [ICALP ’18] defined c-closure and investigated it in the context of clique enumeration. We show that c-closure can be applied in kernelization algorithms for several classic graph problems. We show that Dominating Set admits a kernel of size kO(c), that Induced Matching admits a kernel with O(c7k8) vertices, and that Irredundant Set admits a kernel with O(c5/2k3) vertices. Our kernelization exploits the fact that c-closed graphs have polynomially-bounded Ramsey numbers, as we show. 2012 ACM Subject Classification Theory of computation → Parameterized complexity and exact algorithms; Theory of computation → Graph algorithms analysis Keywords and phrases Fixed-parameter tractability, kernelization, c-closure, Dominating Set, In- duced Matching, Irredundant Set, Ramsey numbers Funding Frank Sommer: Supported by the Deutsche Forschungsgemeinschaft (DFG), project MAGZ, KO 3669/4-1. 1 Introduction Parameterized complexity [9, 14] aims at understanding which properties of input data can be used in the design of efficient algorithms for problems that are hard in general. The properties of input data are encapsulated in the notion of a parameter, a numerical value that can be attributed to each input instance I. -
3.1 Matchings and Factors: Matchings and Covers
1 3.1 Matchings and Factors: Matchings and Covers This copyrighted material is taken from Introduction to Graph Theory, 2nd Ed., by Doug West; and is not for further distribution beyond this course. These slides will be stored in a limited-access location on an IIT server and are not for distribution or use beyond Math 454/553. 2 Matchings 3.1.1 Definition A matching in a graph G is a set of non-loop edges with no shared endpoints. The vertices incident to the edges of a matching M are saturated by M (M-saturated); the others are unsaturated (M-unsaturated). A perfect matching in a graph is a matching that saturates every vertex. perfect matching M-unsaturated M-saturated M Contains copyrighted material from Introduction to Graph Theory by Doug West, 2nd Ed. Not for distribution beyond IIT’s Math 454/553. 3 Perfect Matchings in Complete Bipartite Graphs a 1 The perfect matchings in a complete b 2 X,Y-bigraph with |X|=|Y| exactly c 3 correspond to the bijections d 4 f: X -> Y e 5 Therefore Kn,n has n! perfect f 6 matchings. g 7 Kn,n The complete graph Kn has a perfect matching iff… Contains copyrighted material from Introduction to Graph Theory by Doug West, 2nd Ed. Not for distribution beyond IIT’s Math 454/553. 4 Perfect Matchings in Complete Graphs The complete graph Kn has a perfect matching iff n is even. So instead of Kn consider K2n. We count the perfect matchings in K2n by: (1) Selecting a vertex v (e.g., with the highest label) one choice u v (2) Selecting a vertex u to match to v K2n-2 2n-1 choices (3) Selecting a perfect matching on the rest of the vertices. -
Approximation Algorithms
Lecture 21 Approximation Algorithms 21.1 Overview Suppose we are given an NP-complete problem to solve. Even though (assuming P = NP) we 6 can’t hope for a polynomial-time algorithm that always gets the best solution, can we develop polynomial-time algorithms that always produce a “pretty good” solution? In this lecture we consider such approximation algorithms, for several important problems. Specific topics in this lecture include: 2-approximation for vertex cover via greedy matchings. • 2-approximation for vertex cover via LP rounding. • Greedy O(log n) approximation for set-cover. • Approximation algorithms for MAX-SAT. • 21.2 Introduction Suppose we are given a problem for which (perhaps because it is NP-complete) we can’t hope for a fast algorithm that always gets the best solution. Can we hope for a fast algorithm that guarantees to get at least a “pretty good” solution? E.g., can we guarantee to find a solution that’s within 10% of optimal? If not that, then how about within a factor of 2 of optimal? Or, anything non-trivial? As seen in the last two lectures, the class of NP-complete problems are all equivalent in the sense that a polynomial-time algorithm to solve any one of them would imply a polynomial-time algorithm to solve all of them (and, moreover, to solve any problem in NP). However, the difficulty of getting a good approximation to these problems varies quite a bit. In this lecture we will examine several important NP-complete problems and look at to what extent we can guarantee to get approximately optimal solutions, and by what algorithms. -
Graph Mining: Social Network Analysis and Information Diffusion
Graph Mining: Social network analysis and Information Diffusion Davide Mottin, Konstantina Lazaridou Hasso Plattner Institute Graph Mining course Winter Semester 2016 Lecture road Introduction to social networks Real word networks’ characteristics Information diffusion GRAPH MINING WS 2016 2 Paper presentations ▪ Link to form: https://goo.gl/ivRhKO ▪ Choose the date(s) you are available ▪ Choose three papers according to preference (there are three lists) ▪ Choose at least one paper for each course part ▪ Register to the mailing list: https://lists.hpi.uni-potsdam.de/listinfo/graphmining-ws1617 GRAPH MINING WS 2016 3 What is a social network? ▪Oxford dictionary A social network is a network of social interactions and personal relationships GRAPH MINING WS 2016 4 What is an online social network? ▪ Oxford dictionary An online social network (OSN) is a dedicated website or other application, which enables users to communicate with each other by posting information, comments, messages, images, etc. ▪ Computer science : A network that consists of a finite number of users (nodes) that interact with each other (edges) by sharing information (labels, signs, edge directions etc.) GRAPH MINING WS 2016 5 Definitions ▪Vertex/Node : a user of the social network (Twitter user, an author in a co-authorship network, an actor etc.) ▪Edge/Link/Tie : the connection between two vertices referring to their relationship or interaction (friend-of relationship, re-tweeting activity, etc.) Graph G=(V,E) symbols meaning V users E connections deg(u) node degree: -
Multi-Budgeted Matchings and Matroid Intersection Via Dependent Rounding
Multi-budgeted Matchings and Matroid Intersection via Dependent Rounding Chandra Chekuri∗ Jan Vondrak´ y Rico Zenklusenz Abstract ing: given a fractional point x in a polytope P ⊂ Rn, ran- Motivated by multi-budgeted optimization and other applications, domly round x to a solution R corresponding to a vertex we consider the problem of randomly rounding a fractional solution of P . Here P captures the deterministic constraints that we x in the (non-bipartite graph) matching and matroid intersection wish the rounding to satisfy, and it is natural to assume that polytopes. We show that for any fixed δ > 0, a given point P is an integer polytope (typically a f0; 1g polytope). Of course the important issue is what properties we need R to x can be rounded to a random solution R such that E[1R] = (1 − δ)x and any linear function of x satisfies dimension-free satisfy, and this is dictated by the application at hand. A Chernoff-Hoeffding concentration bounds (the bounds depend on property that is useful in several applications is that R sat- δ and the expectation µ). We build on and adapt the swap isfies concentration properties for linear functions of x: that n rounding scheme in our recent work [9] to achieve this result. is, for any vector a 2 [0; 1] , we want the linear function P 1 Our main contribution is a non-trivial martingale based analysis a(R) = i2R ai to be concentrated around its expectation . framework to prove the desired concentration bounds. In this Ideally, we would like to have E[1R] = x, which would P paper we describe two applications. -
Complexity and Approximation Results for the Connected Vertex Cover Problem in Graphs and Hypergraphs Bruno Escoffier, Laurent Gourvès, Jérôme Monnot
Complexity and approximation results for the connected vertex cover problem in graphs and hypergraphs Bruno Escoffier, Laurent Gourvès, Jérôme Monnot To cite this version: Bruno Escoffier, Laurent Gourvès, Jérôme Monnot. Complexity and approximation results forthe connected vertex cover problem in graphs and hypergraphs. 2007. hal-00178912 HAL Id: hal-00178912 https://hal.archives-ouvertes.fr/hal-00178912 Preprint submitted on 12 Oct 2007 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Laboratoire d'Analyse et Modélisation de Systèmes pour l'Aide à la Décision CNRS UMR 7024 CAHIER DU LAMSADE 262 Juillet 2007 Complexity and approximation results for the connected vertex cover problem in graphs and hypergraphs Bruno Escoffier, Laurent Gourvès, Jérôme Monnot Complexity and approximation results for the connected vertex cover problem in graphs and hypergraphs Bruno Escoffier∗ Laurent Gourv`es∗ J´erˆome Monnot∗ Abstract We study a variation of the vertex cover problem where it is required that the graph induced by the vertex cover is connected. We prove that this problem is polynomial in chordal graphs, has a PTAS in planar graphs, is APX-hard in bipartite graphs and is 5/3-approximable in any class of graphs where the vertex cover problem is polynomial (in particular in bipartite graphs). -
Approximation Algorithms Instructor: Richard Peng Nov 21, 2016
CS 3510 Design & Analysis of Algorithms Section B, Lecture #34 Approximation Algorithms Instructor: Richard Peng Nov 21, 2016 • DISCLAIMER: These notes are not necessarily an accurate representation of what I said during the class. They are mostly what I intend to say, and have not been carefully edited. Last week we formalized ways of showing that a problem is hard, specifically the definition of NP , NP -completeness, and NP -hardness. We now discuss ways of saying something useful about these hard problems. Specifi- cally, we show that the answer produced by our algorithm is within a certain fraction of the optimum. For a maximization problem, suppose now that we have an algorithm A for our prob- lem which, given an instance I, returns a solution with value A(I). The approximation ratio of algorithm A is dened to be OP T (I) max : I A(I) This is not restricted to NP-complete problems: we can apply this to matching to get a 2-approximation. Lemma 0.1. The maximal matching has size at least 1=2 of the optimum. Proof. Let the maximum matching be M ∗. Consider the process of taking a maximal matching: an edge in M ∗ can't be chosen only after one (or both) of its endpoints are picked. Each edge we add to the maximal matching only has 2 endpoints, so it takes at least M ∗ 2 edges to make all edges in M ∗ ineligible. This ratio is indeed close to tight: consider a graph that has many length 3 paths, and the maximal matching keeps on taking the middle edges.