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Tight Bounds for Online Edge Coloring
Tight Bounds for Online Edge Coloring Ilan Reuven Cohen∗1, Binghui Peng†‡2, and David Wajc§¶k3 1Carnegie Mellon University and University of Pittsburgh 2Tsinghua University 3Carnegie Mellon University Abstract Vizing’s celebrated theorem asserts that any graph of maximum degree ∆ admits an edge coloring using at most ∆ + 1 colors. In contrast, Bar-Noy, Motwani and Naor showed over a quarter century ago that the trivial greedy algorithm, which uses 2∆ 1 colors, is optimal among online algorithms. Their lower bound has a caveat, however: it− only applies to low- degree graphs, with ∆ = O(log n), and they conjectured the existence of online algorithms using ∆(1 + o(1)) colors for ∆= ω(log n). Progress towards resolving this conjecture was only made under stochastic arrivals (Aggarwal et al., FOCS’03 and Bahmani et al., SODA’10). We resolve the above conjecture for adversarial vertex arrivals in bipartite graphs, for which we present a (1+ o(1))∆-edge-coloring algorithm for ∆= ω(log n) known a priori. Surprisingly, if ∆ is not known ahead of time, we show that no e Ω(1) ∆-edge-coloring algorithm exists. e−1 − We then provide an optimal, e + o(1) ∆-edge-coloring algorithm for unknown ∆= ω(log n). e−1 Key to our results, and of possible independent interest, is a novel fractional relaxation for edge coloring, for which we present optimal fractional online algorithms and a near-lossless online rounding scheme, yielding our optimal randomized algorithms. ∗Email address: [email protected]. †Email address: [email protected]. ‡Work done in part while the author was visiting Carnegie Mellon University. -
Hamilton Cycle Heuristics in Hard Graphs (Under the Direction of Pro- Fessor Carla D
ABSTRACT SHIELDS, IAN BEAUMONT Hamilton Cycle Heuristics in Hard Graphs (Under the direction of Pro- fessor Carla D. Savage) In this thesis, we use computer methods to investigate Hamilton cycles and paths in several families of graphs where general results are incomplete, including Kneser graphs, cubic Cayley graphs and the middle two levels graph. We describe a novel heuristic which has proven useful in finding Hamilton cycles in these families and compare its performance to that of other algorithms and heuristics. We describe methods for handling very large graphs on personal computers. We also explore issues in reducing the possible number of generating sets for cubic Cayley graphs generated by three involutions. Hamilton Cycle Heuristics in Hard Graphs by Ian Beaumont Shields A dissertation submitted to the Graduate Faculty of North Carolina State University in partial fulfillment of the requirements for the Degree of Doctor of Philosophy Computer Science Raleigh 2004 APPROVED BY: ii Dedication To my wife Pat, and my children, Catherine, Brendan and Michael, for their understanding during these years of study. To the memory of Hanna Neumann who introduced me to combinatorial group theory. iii Biography Ian Shields was born on March 10, 1949 in Melbourne, Australia. He grew up and attended school near the foot of the Dandenong ranges. After a short time in actuarial work he attended the Australian National University in Canberra, where he graduated in 1973 with first class honours in pure mathematics. Ian worked for IBM and other companies in Australia, Canada and the United States, before resuming part-time study for a Masters Degree in Computer Science at North Carolina State University which he received in May 1995. -
Are Highly Connected 1-Planar Graphs Hamiltonian? Arxiv:1911.02153V1 [Cs.DM] 6 Nov 2019
Are highly connected 1-planar graphs Hamiltonian? Therese Biedl Abstract It is well-known that every planar 4-connected graph has a Hamiltonian cycle. In this paper, we study the question whether every 1-planar 4-connected graph has a Hamiltonian cycle. We show that this is false in general, even for 5-connected graphs, but true if the graph has a 1-planar drawing where every region is a triangle. 1 Introduction Planar graphs are graphs that can be drawn without crossings. They have been one of the central areas of study in graph theory and graph algorithms, and there are numerous results both for how to solve problems more easily on planar graphs and how to draw planar graphs (see e.g. [1, 9, 10])). We are here interested in a theorem by Tutte [17] that states that every planar 4-connected graph has a Hamiltonian cycle (definitions are in the next section). This was an improvement over an earlier result by Whitney that proved the existence of a Hamiltonian cycle in a 4-connected triangulated planar graph. There have been many generalizations and improvements since; in particular we can additionally fix the endpoints and one edge that the Hamiltonian cycle must use [16, 12]. Also, 4-connected planar graphs remain Hamiltonian even after deleting 2 vertices [14]. Hamiltonian cycles in planar graphs can be computed in linear time; this is quite straightforward if the graph is triangulated [2] and a bit more involved for general 4-connected planar graphs [5]. There are many graphs that are near-planar, i.e., that are \close" to planar graphs. -
Generalized Line Graphs: Cartesian Products and Complexity of Recognition ∗
Generalized line graphs: Cartesian products and complexity of recognition ∗ Aparna Lakshmanan S. Csilla Bujt´as Department of Mathematics Department of Computer Science and Technology St. Xavier's College for Women University of Pannonia Aluva, India Veszpr´em,Hungary [email protected] [email protected] Zsolt Tuza Alfr´edR´enyi Institute of Mathematics Hungarian Academy of Sciences Budapest, Hungary [email protected] Submitted: Dec 21, 2014; Accepted: Aug 20, 2015; Published: Sep 11, 2015 Mathematics Subject Classifications: 05C75 (primary), 05C62, 68Q17 Abstract Putting the concept of line graph in a more general setting, for a positive integer k the k-line graph Lk(G) of a graph G has the Kk-subgraphs of G as its vertices, and two vertices of Lk(G) are adjacent if the corresponding copies of Kk in G share k − 1 vertices. Then, 2-line graph is just the line graph in usual sense, whilst 3-line graph is also known as triangle graph. The k-anti-Gallai graph 4k(G) of G is a specified subgraph of Lk(G) in which two vertices are adjacent if the corresponding two Kk-subgraphs are contained in a common Kk+1-subgraph in G. We give a unified characterization for nontrivial connected graphs G and F such that the Cartesian product G F is a k-line graph. In particular for k = 3, this answers the question of Bagga (2004), yielding the necessary and sufficient condition that G is the line graph of a triangle-free graph and F is a complete graph (or vice versa). -
ABSTRACTS Multidesigns of the Λ-Fold Complete Graph Atif Abueida
ABSTRACTS Multidesigns of the λ-fold complete graph Atif Abueida, University of Dayton By a graph-pair of order t, we mean two non-isomorphic graphs G and H on t non-isolated vertices ∼ for which G ∪ H = Kt for some integer t ≥ 4. we consider the existence of multidecompositions of λKm for the graph-pairs of order 4 or 5. For those graph-pairs, we will also look for maximum multipackings and minimum multicoverings of λKm. An Upper Bound of Integrity of n-cage Graph Mustafa Atici, Western Kentucky University Integrity, a measure of network to reliability, is defined as I(G) = minS⊂V {|S| + m(G − S)} , where G is a graph with vertex set V and m(G − S) denotes the order of the largest component of G − S. We prove an upper bound of the following form on the integrity of any r-cage graph with n vertices: n+2 n+2 I(G) ≤ + I(P 3n−2 ), if = 4t 4 4 4 n n 0 I(G) ≤ + I(C 3n )if = 4t 4 4 4 where t and t0 are integers. To prove this upper bound we first solve the following problem: Suppose G is a connected cubic graph with g ≥ 2(d − 3) and d ≥ 5, where d is diameter and g is n girth of the graph G. We can remove d 4 e vertices from G in such a way that the remaining graph 3n 3n is a tree or a G( 4 , 4 ) graph. On Rainbow Arithmetic Progressions Maria Axenovich∗, Iowa State University Dmitri Fon-Der-Flaass Consider natural numbers {1, ··· , n} colored in three colors. -
Drawing Outerplanar Graphs Using Three Edge Lengths
Drawing outerplanar graphs using three edge lengths Noga Alon ∗ Ohad N. Feldheim † August 27, 2013 Abstract It is shown that for any outerplanar graph G there is a one to one mapping of the vertices of G to the plane, so that the number of distinct distances between pairs of connected vertices is at most three. This settles a problem of Carmi, Dujmovi´c, Morin and Wood. The proof combines (elementary) geometric, combinatorial, algebraic and probabilistic arguments. Keywords: Outerplanar graphs, distance number of a graph, degenerate drawing of a graph. 1 Introduction A linear embedding of a graph G is a mapping of the vertices of G to points in the plane. The image of every edge uv of the graph is the open interval between the image of u and the image of v. The length of that interval is called the edge-length of uv in the embedding. A degenerate drawing of a graph G is a linear embedding in which the images of all vertices are distinct. A drawing of G is a degenerate drawing in which the image of every edge is disjoint from the image of every vertex. The distance-number of a graph is the minimum number of distinct edge-lengths in a drawing of G, the degenerate distance-number is its counterpart for degenerate drawings. An outerplanar graph is a graph that can be embedded in the plane without crossings in such a way that all the vertices lie in the boundary of the unbounded face of the embedding. In [1], Carmi, Dujmovi´c, Morin and Wood ask if the degenerate distance-number of outerplanar graphs is uniformly bounded. -
The Graph Isomorphism Problem on Geometric Graphs Ryuhei Uehara
The graph isomorphism problem on geometric graphs Ryuhei Uehara To cite this version: Ryuhei Uehara. The graph isomorphism problem on geometric graphs. Discrete Mathematics and Theoretical Computer Science, DMTCS, 2014, Vol. 16 no. 2 (in progress) (2), pp.87–96. hal- 01185616 HAL Id: hal-01185616 https://hal.inria.fr/hal-01185616 Submitted on 20 Aug 2015 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. Discrete Mathematics and Theoretical Computer Science DMTCS vol. 16:2, 2014, 87–96 The graph isomorphism problem on geometric graphs Ryuhei Uehara∗ School of Information Science, Japan Advanced Institute of Science and Technology (JAIST), Ishikawa, Japan. received 29th Nov. 2013, revised 7th July 2014, accepted 8th July 2014. The graph isomorphism (GI) problem asks whether two given graphs are isomorphic or not. The GI problem is quite basic and simple, however, it’s time complexity is a long standing open problem. The GI problem is clearly in NP, no polynomial time algorithm is known, and the GI problem is not NP-complete unless the polynomial hierarchy collapses. In this paper, we survey the computational complexity of the problem on some graph classes that have geometric characterizations. -
CMSC 27130 Honors Discrete Mathematics
CMSC 27130 Honors Discrete Mathematics Lectures by Alexander Razborov Notes by Justin Lubin The University of Chicago, Autumn 2017 1 Contents I Number Theory 4 1 The Euclidean Algorithm 4 2 Mathematical Induction 4 3 Strong Induction and the Well-Ordering Principle 6 3.1 Strong Induction . 6 3.2 The Well-Ordering Principle . 7 IV Graph Theory 10 21 Introduction to Graph Theory 10 21.1 Graphs . 10 21.2 The Handshaking Lemma . 11 21.3 Paths and Connectedness . 11 21.4 Graph Properties . 12 22 Exploring Graphs 13 22.1 Spectral Graph Theory . 13 22.2 Graph Relationships . 15 23 Some Graph Theorems 16 23.1 Extremal Graph Theory . 16 23.2 Bipartite Graphs . 17 24 More Graph Properties and Trees 18 24.1 Cliques, Coloring, and Independence . 18 24.2 Trees . 20 25 Planar Graphs 21 25.1 Planarity . 21 25.2 Euler's Formula . 22 25.3 Dual Graphs . 23 2 25.4 The Nonplanarity of K5 and K3;3 ........................ 23 Notation and Conventions For this document, I adhere to the following conventions: • The \natural numbers" are represented by N and are equal to f1; 2; 3;:::g. • N0 = f0; 1; 2; 3;:::; g. • The notation [n] represents the set f1; 2; : : : ; ng for any n 2 N. • A definition defines a new term. • A theorem is a main result. • A lemma is a result used to build up to a theorem. • A corollary is an immediate result of a theorem or lemma. • A remark is a side comment or claim (usually unproven) that is interesting but usually not critical for the class. -
Topics in Trivalent Graphs
Topics in Trivalent Graphs Marijke van Gans January sssssssssssssssssssss sssssssssss ss s sss ssssssssss ssssss ss. .......s. .. .s .s sssssss ssssssss .s .... .. ........s.......................... ssssssss ssss ................ .s.. ...s ......... ssss ssss ........ ......... sss sssss ..... ..... ssss ssss.s .... ..... sssss sssss... ....s..... sssss ssss ..... .ssss sss.s . ....sssss ssss.................s .. .... sss ss ... .. .....s.. sss sss .. .. .. ssss ssss .. .. ......s........ sss ssss..s ..s .. ss sss ........... ........ .. .ssss sss ....s ......... ......... s. ...s ........s. ssss ssss .......... .... .......... .... sss ss ........s.......... .. .. ss ssss ...... .. ss ss......s.... .. sss ss....s... ..ssss ss..s.. .. ..s... s........ s sss.s. .... ................. sss ss .. ... .........s.. ss sss . ... .....sss s .. .. .sss s . .. ss s .. .. s s s..................s. s s . .. s s .. .. sss ss . ... sss sss .. .. .s....ss ss . ... .........ssss ss.ss .. ... ..........s.. s ss....s . ...s.......... ..... ssss sss...... .....sss sss.....s. .. .sss ss.. .....s . ss ssss............. .. .. ss ss.s.... .......s s.. ..s ssss ss ............. ......... ..................s..... ss ssss s.... ...... s........ .. ..sss ss......... ... .. sss ss..s .. .. ...s ssss sss .. .. ............sss ss ... .. .sssss sssss ...s. .. ..s.....sss ss.s...s............. ..s...sss ssss .s .. ..s..ssss sss......s ..s.. sssss ss..sss .... ..... sssss ssss ..... ..... ssss ssss .s... ....s sss ssssssss s............ ............. ssss ss.s..s....s...s..s -
Hamiltonian Cycles in Triangle Graphs
Hamiltonian Cycles in Triangle Graphs U Dogrusoz and M S Krishnamo orthy Tom Sawyer Software Hearst Avenue Berkeley CA ugurtomsawyercom Department of Computer Science Rensselaer Polytechnic Institute Troy NY mo orthycsrpiedu Abstract Weprove that the Hamiltonian cycle problem is NPcomplete for a class of planar graphs named triangle graphs that are closely related to innertriangul ated graphs We present a linear time heuristic algorithm that nds a solution at most onethird longer than the optimum solution and use it to obtain a fast rendering algorithm on triangular meshes in computer graphics Keywords Planar graph innertriangulatedgraph triangle graph NPcomplete heuristic algorithm rendering triangular meshes Intro duction The Hamiltonian cycle problem HC is that of determining whether or not a given graph contains a cycle that passes through every vertex exactly once and has numerous applications in dierent areas HC is NPcomplete for various classes of graphs including cubic connected planar graphs and maximal planar graphs and therefore for innertriangulations but a p olynomial time algorithm was presented for connected planar graphs by Chiba and Nishizeki The problem of nding a Hamiltonian cycle in an innertriangulation has applications in computer vision in determining the shap e of an ob ject represented by a cluster of p oints Cimikowski identied a Hamil tonian sub class of innertriangulations namely simply nested innertriangulations and presented a lineartime algorithm for nding a Hamiltonian cycle in suc h graphs -
Cycle Bases of Graphs and Spanning Trees with Many Leaves
Cycle Bases of Graphs and Spanning Trees with Many Leaves Complexity Results on Planar and Regular Graphs Von der Fakultät für Mathematik, Naturwissenschaften und Informatik der Brandenburgischen Technischen Universität Cottbus - Senftenberg zur Erlangung des akademischen Grades Doktor der Naturwissenschaften (Dr. rer. nat.) genehmigte Dissertation vorgelegt von Diplom-Mathematiker Alexander Reich geboren am 29.10.1982 in Lübben Gutachter: Prof. Dr. Ekkehard G. Köhler Gutachter: Prof. Dr. Rolf H. Möhring Gutachter: Prof. Dr. Romeo Rizzi Tag der mündlichen Prüfung: 15. April 2014 Abstract A cycle basis of a graph is a basis of its cycle space, the vector space which is spanned by the cycles of the graph. Practical applications for cycle bases are for example the optimization of periodic timetables, electrical engineering, and chemistry. Often, cycle bases belong to the input of algorithms concerning these fields. In these cases, the running time of the algorithm can depend on the size of the given cycle basis. In this thesis, we study the complexity of finding minimum cycle bases of several types on different graph classes. As a main result, we show that the problem of minimizing strictly fundamental cycle bases on planar graphs is -complete. We also give a similarly structured proof for problem of finding a maximumN P leaf spanning tree on the very restricted class of cubic planar graphs. Additionally, we show that this problem is -complete on k-regular graphs for odd k greater than 3. Furthermore, we classify typesAPX of robust cycle bases and study their relationship to fundamental cycle bases. Zusammenfassung Eine Kreisbasis eines Graphen ist eine Basis des Zyklenraumes des Graphen, also des Vektorraumes, der von den Kreisen des Graphen aufgespannt wird. -
Altermundusaltermundus C 1.00 V.V Documentation 2021 Janvier 19 Li Matthes Alain Tkz-Berge
AlterMundusAlterMundus tkz-berge v 1.00 c Alain Matthes 19 janvier 2021 Documentation V.v 1.00 c http://altermundus.fr tkz-berge Alain Matthes The package tkz-berge is a collection of some useful macros if you want to draw some classic graphs of the graph theory or to make others graphs. The kind of graphs that I will present, are sometimes called combinatorial graphs to distinguish them from the graphs of functions. Often, the word graph is short for graph of a function. A combinatorial graph is a very simple structure, a bunch of dots, some of which are connected by lines. Some of graphs have names, sometimes inspired by the graph’s topology, and sometimes after their discoverer. Why tkz-berge.sty? Claude Berge (1926 – 2002) was a French mathematician, recognized as one of the modern founders of AlterMundus combinatorics and graph theory. He played a major role in the renaissance of combinatorics and he is remembered for his famous conjecture on perfect graphs, solved some months after his death. M Firstly, I would like to thank Till Tantau for the beautiful LATEX package, namely TikZ. MI am grateful to Michel Bovani for providing the fourier font. M I received much valuable advice and guidance on Graph Theory from Rafael Villarroel http://graphtheoryinlatex.blogspot.com/. M The names of graphs can be found here MathWorld - SimpleGraphs by E.Weisstein Please report typos or any other comments to this documentation to Alain Matthes This file can be redistributed and/or modified under the terms of the LATEX Project Public License Distributed from CTAN archives in directory CTAN://macros/latex/base/lppl.txt.