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Lecture 20 — March 20, 2013 1 the Maximum Cut Problem 2 a Spectral
UBC CPSC 536N: Sparse Approximations Winter 2013 Lecture 20 | March 20, 2013 Prof. Nick Harvey Scribe: Alexandre Fr´echette We study the Maximum Cut Problem with approximation in mind, and naturally we provide a spectral graph theoretic approach. 1 The Maximum Cut Problem Definition 1.1. Given a (undirected) graph G = (V; E), the maximum cut δ(U) for U ⊆ V is the cut with maximal value jδ(U)j. The Maximum Cut Problem consists of finding a maximal cut. We let MaxCut(G) = maxfjδ(U)j : U ⊆ V g be the value of the maximum cut in G, and 0 MaxCut(G) MaxCut = jEj be the normalized version (note that both normalized and unnormalized maximum cut values are induced by the same set of nodes | so we can interchange them in our search for an actual maximum cut). The Maximum Cut Problem is NP-hard. For a long time, the randomized algorithm 1 1 consisting of uniformly choosing a cut was state-of-the-art with its 2 -approximation factor The simplicity of the algorithm and our inability to find a better solution were unsettling. Linear pro- gramming unfortunately didn't seem to help. However, a (novel) technique called semi-definite programming provided a 0:878-approximation algorithm [Goemans-Williamson '94], which is optimal assuming the Unique Games Conjecture. 2 A Spectral Approximation Algorithm Today, we study a result of [Trevison '09] giving a spectral approach to the Maximum Cut Problem with a 0:531-approximation factor. We will present a slightly simplified result with 0:5292-approximation factor. -
Tight Integrality Gaps for Lovasz-Schrijver LP Relaxations of Vertex Cover and Max Cut
Tight Integrality Gaps for Lovasz-Schrijver LP Relaxations of Vertex Cover and Max Cut Grant Schoenebeck∗ Luca Trevisany Madhur Tulsianiz Abstract We study linear programming relaxations of Vertex Cover and Max Cut arising from repeated applications of the \lift-and-project" method of Lovasz and Schrijver starting from the standard linear programming relaxation. For Vertex Cover, Arora, Bollobas, Lovasz and Tourlakis prove that the integrality gap remains at least 2 − " after Ω"(log n) rounds, where n is the number of vertices, and Tourlakis proves that integrality gap remains at least 1:5 − " after Ω((log n)2) rounds. Fernandez de la 1 Vega and Kenyon prove that the integrality gap of Max Cut is at most 2 + " after any constant number of rounds. (Their result also applies to the more powerful Sherali-Adams method.) We prove that the integrality gap of Vertex Cover remains at least 2 − " after Ω"(n) rounds, and that the integrality gap of Max Cut remains at most 1=2 + " after Ω"(n) rounds. 1 Introduction Lovasz and Schrijver [LS91] describe a method, referred to as LS, to tighten a linear programming relaxation of a 0/1 integer program. The method adds auxiliary variables and valid inequalities, and it can be applied several times sequentially, yielding a sequence (a \hierarchy") of tighter and tighter relaxations. The method is interesting because it produces relaxations that are both tightly constrained and efficiently solvable. For a linear programming relaxation K, denote by N(K) the relaxation obtained by the application of the LS method, and by N k(K) the relaxation obtained by applying the LS method k times. -
The Complexity of Two Graph Orientation Problems
The Complexity of Two Graph Orientation Problems Nicole Eggemann∗ and Steven D. Noble† Department of Mathematical Sciences, Brunel University Kingston Lane Uxbridge UB8 3PH United Kingdom [email protected] and [email protected] November 8, 2018 Abstract We consider two orientation problems in a graph, namely the mini- mization of the sum of all the shortest path lengths and the minimization of the diameter. We show that it is NP-complete to decide whether a graph has an orientation such that the sum of all the shortest paths lengths is at most an integer specified in the input. The proof is a short reduction from a result of Chv´atal and Thomassen showing that it is NP-complete to decide whether a graph can be oriented so that its diameter is at most 2. In contrast, for each positive integer k, we describe a linear-time algo- rithm that decides for a planar graph G whether there is an orientation for which the diameter is at most k. We also extend this result from planar graphs to any minor-closed family F not containing all apex graphs. 1 Introduction We consider two problems concerned with orienting the edges of an undirected graph in order to minimize two global measures of distance in the resulting directed graph. Our work is motivated by an application involving the design of arXiv:1004.2478v1 [math.CO] 14 Apr 2010 urban light rail networks of the sort described in [22]. In such an application, a number of stations are to be linked with unidirectional track in order to minimize some function of the travel times between stations and subject to constraints on cost, engineering and planning. -
Optimal Inapproximability Results for MAX-CUT and Other 2-Variable Csps?
Electronic Colloquium on Computational Complexity, Report No. 101 (2005) Optimal Inapproximability Results for MAX-CUT and Other 2-Variable CSPs? Subhash Khot∗ Guy Kindlery Elchanan Mosselz College of Computing Theory Group Department of Statistics Georgia Tech Microsoft Research U.C. Berkeley [email protected] [email protected] [email protected] Ryan O'Donnell∗ Theory Group Microsoft Research [email protected] September 19, 2005 Abstract In this paper we show a reduction from the Unique Games problem to the problem of approximating MAX-CUT to within a factor of αGW + , for all > 0; here αGW :878567 denotes the approxima- tion ratio achieved by the Goemans-Williamson algorithm [25]. This≈implies that if the Unique Games Conjecture of Khot [36] holds then the Goemans-Williamson approximation algorithm is optimal. Our result indicates that the geometric nature of the Goemans-Williamson algorithm might be intrinsic to the MAX-CUT problem. Our reduction relies on a theorem we call Majority Is Stablest. This was introduced as a conjecture in the original version of this paper, and was subsequently confirmed in [42]. A stronger version of this conjecture called Plurality Is Stablest is still open, although [42] contains a proof of an asymptotic version of it. Our techniques extend to several other two-variable constraint satisfaction problems. In particular, subject to the Unique Games Conjecture, we show tight or nearly tight hardness results for MAX-2SAT, MAX-q-CUT, and MAX-2LIN(q). For MAX-2SAT we show approximation hardness up to a factor of roughly :943. This nearly matches the :940 approximation algorithm of Lewin, Livnat, and Zwick [40]. -
Arxiv:2104.09976V2 [Cs.CG] 2 Jun 2021
Finding Geometric Representations of Apex Graphs is NP-Hard Dibyayan Chakraborty* and Kshitij Gajjar† June 3, 2021 Abstract Planar graphs can be represented as intersection graphs of different types of geometric objects in the plane, e.g., circles (Koebe, 1936), line segments (Chalopin & Gonc¸alves, 2009), L-shapes (Gonc¸alves et al., 2018). For general graphs, however, even deciding whether such representations exist is often NP-hard. We consider apex graphs, i.e., graphs that can be made planar by removing one vertex from them. We show, somewhat surprisingly, that deciding whether geometric representations exist for apex graphs is NP-hard. More precisely, we show that for every positive integer k, recognizing every graph class G which satisfies PURE-2-DIR ⊆ G ⊆ 1-STRING is NP-hard, even when the input graphs are apex graphs of girth at least k. Here, PURE-2-DIR is the class of intersection graphs of axis-parallel line segments (where intersections are allowed only between horizontal and vertical segments) and 1-STRING is the class of intersection graphs of simple curves (where two curves share at most one point) in the plane. This partially answers an open question raised by Kratochv´ıl & Pergel (2007). Most known NP-hardness reductions for these problems are from variants of 3-SAT. We reduce from the PLANAR HAMILTONIAN PATH COMPLETION problem, which uses the more intuitive notion of planarity. As a result, our proof is much simpler and encapsulates several classes of geometric graphs. Keywords: Planar graphs, apex graphs, NP-hard, Hamiltonian path completion, recognition problems, geometric intersection graphs, 1-STRING, PURE-2-DIR. -
Nowhere-Zero Flows in Regular Matroids and Hadwiger's Conjecture
NOWHERE-ZERO FLOWS IN REGULAR MATROIDS AND HADWIGER'S CONJECTURE LUIS A. GODDYN AND WINFRIED HOCHSTATTLER¨ To the memory of Reinhard B¨orger Abstract. We present a tool that shows, that the existence of a k-nowhere-zero-flow is compatible with 1-,2- and 3-sums in regular matroids. As application we present a conjecture for regular matroids that is equivalent to Hadwiger's conjecture for graphs and Tuttes's 4- and 5-flow conjectures. Keywords: nowhere zero flow, regular matroid, chromatic number, flow number, total unimodularity 1. Introduction A (real) matrix is totally unimodular (TUM) if each subdeterminant belongs to f0; ±1g. Totally unimodular matrices enjoy several nice properties which give them a fundamental role in combinatorial optimization and matroid theory. In this note we prove that the TUM possesses an attractive property. Let S ⊆ R, and let A be a real matrix. A column vector f is a S-flow of A if Af = 0 and every entry of f is a member of ±S. For any additive abelian group Γ use the notation Γ∗ = Γ n f0g. For a TUM A and a column vector f with entries in Γ, the product Af is a well defined column vector with entries in Γ, by interpreting (−1)γ to be the additive inverse of γ. It is convenient to use the language of matroids. A regular oriented matroid M is an oriented matroid that is representable M = M[A] by a TUM matrix A. Here the elements E(M) of M label the columns of A. Each (signed) cocircuit D = (D+;D−) of M corresponds to a f0; ±1g-valued vector in the row space of A and having minimal support. -
Characterizations of Graphs Without Certain Small Minors by J. Zachary
Characterizations of Graphs Without Certain Small Minors By J. Zachary Gaslowitz Dissertation Submitted to the Faculty of the Graduate School of Vanderbilt University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY in Mathematics May 11, 2018 Nashville, Tennessee Approved: Mark Ellingham, Ph.D. Paul Edelman, Ph.D. Bruce Hughes, Ph.D. Mike Mihalik, Ph.D. Jerry Spinrad, Ph.D. Dong Ye, Ph.D. TABLE OF CONTENTS Page 1 Introduction . 2 2 Previous Work . 5 2.1 Planar Graphs . 5 2.2 Robertson and Seymour's Graph Minor Project . 7 2.2.1 Well-Quasi-Orderings . 7 2.2.2 Tree Decomposition and Treewidth . 8 2.2.3 Grids and Other Graphs with Large Treewidth . 10 2.2.4 The Structure Theorem and Graph Minor Theorem . 11 2.3 Graphs Without K2;t as a Minor . 15 2.3.1 Outerplanar and K2;3-Minor-Free Graphs . 15 2.3.2 Edge-Density for K2;t-Minor-Free Graphs . 16 2.3.3 On the Structure of K2;t-Minor-Free Graphs . 17 3 Algorithmic Aspects of Graph Minor Theory . 21 3.1 Theoretical Results . 21 3.2 Practical Graph Minor Containment . 22 4 Characterization and Enumeration of 4-Connected K2;5-Minor-Free Graphs 25 4.1 Preliminary Definitions . 25 4.2 Characterization . 30 4.3 Enumeration . 38 5 Characterization of Planar 4-Connected DW6-minor-free Graphs . 51 6 Future Directions . 91 BIBLIOGRAPHY . 93 1 Chapter 1 Introduction All graphs in this paper are finite and simple. Given a graph G, the vertex set of G is denoted V (G) and the edge set is denoted E(G). -
Minor-Closed Graph Classes with Bounded Layered Pathwidth
Minor-Closed Graph Classes with Bounded Layered Pathwidth Vida Dujmovi´c z David Eppstein y Gwena¨elJoret x Pat Morin ∗ David R. Wood { 19th October 2018; revised 4th June 2020 Abstract We prove that a minor-closed class of graphs has bounded layered pathwidth if and only if some apex-forest is not in the class. This generalises a theorem of Robertson and Seymour, which says that a minor-closed class of graphs has bounded pathwidth if and only if some forest is not in the class. 1 Introduction Pathwidth and treewidth are graph parameters that respectively measure how similar a given graph is to a path or a tree. These parameters are of fundamental importance in structural graph theory, especially in Roberston and Seymour's graph minors series. They also have numerous applications in algorithmic graph theory. Indeed, many NP-complete problems are solvable in polynomial time on graphs of bounded treewidth [23]. Recently, Dujmovi´c,Morin, and Wood [19] introduced the notion of layered treewidth. Loosely speaking, a graph has bounded layered treewidth if it has a tree decomposition and a layering such that each bag of the tree decomposition contains a bounded number of vertices in each layer (defined formally below). This definition is interesting since several natural graph classes, such as planar graphs, that have unbounded treewidth have bounded layered treewidth. Bannister, Devanny, Dujmovi´c,Eppstein, and Wood [1] introduced layered pathwidth, which is analogous to layered treewidth where the tree decomposition is arXiv:1810.08314v2 [math.CO] 4 Jun 2020 required to be a path decomposition. -
On Graph Crossing Number and Edge Planarization∗
On Graph Crossing Number and Edge Planarization∗ Julia Chuzhoyy Yury Makarychevz Anastasios Sidiropoulosx Abstract Given an n-vertex graph G, a drawing of G in the plane is a mapping of its vertices into points of the plane, and its edges into continuous curves, connecting the images of their endpoints. A crossing in such a drawing is a point where two such curves intersect. In the Minimum Crossing Number problem, the goal is to find a drawing of G with minimum number of crossings. The value of the optimal solution, denoted by OPT, is called the graph's crossing number. This is a very basic problem in topological graph theory, that has received a significant amount of attention, but is still poorly understood algorithmically. The best currently known efficient algorithm produces drawings with O(log2 n) · (n + OPT) crossings on bounded-degree graphs, while only a constant factor hardness of approximation is known. A closely related problem is Minimum Planarization, in which the goal is to remove a minimum-cardinality subset of edges from G, such that the remaining graph is planar. Our main technical result establishes the following connection between the two problems: if we are given a solution of cost k to the Minimum Planarization problem on graph G, then we can efficiently find a drawing of G with at most poly(d) · k · (k + OPT) crossings, where d is the maximum degree in G. This result implies an O(n · poly(d) · log3=2 n)-approximation for Minimum Crossing Number, as well as improved algorithms for special cases of the problem, such as, for example, k-apex and bounded-genus graphs. -
Sphere-Cut Decompositions and Dominating Sets in Planar Graphs
Sphere-cut Decompositions and Dominating Sets in Planar Graphs Michalis Samaris R.N. 201314 Scientific committee: Dimitrios M. Thilikos, Professor, Dep. of Mathematics, National and Kapodistrian University of Athens. Supervisor: Stavros G. Kolliopoulos, Dimitrios M. Thilikos, Associate Professor, Professor, Dep. of Informatics and Dep. of Mathematics, National and Telecommunications, National and Kapodistrian University of Athens. Kapodistrian University of Athens. white Lefteris M. Kirousis, Professor, Dep. of Mathematics, National and Kapodistrian University of Athens. Aposunjèseic sfairik¸n tom¸n kai σύνοla kuriarqÐac se epÐpeda γραφήματa Miχάλης Σάμαρης A.M. 201314 Τριμελής Epiτροπή: Δημήτρioc M. Jhlυκός, Epiblèpwn: Kajhγητής, Tm. Majhmatik¸n, E.K.P.A. Δημήτρioc M. Jhlυκός, Staύρoc G. Kolliόποuloc, Kajhγητής tou Τμήμatoc Anaπληρωτής Kajhγητής, Tm. Plhroforiκής Majhmatik¸n tou PanepisthmÐou kai Thl/ni¸n, E.K.P.A. Ajhn¸n Leutèrhc M. Kuroύσης, white Kajhγητής, Tm. Majhmatik¸n, E.K.P.A. PerÐlhyh 'Ena σημαντικό apotèlesma sth JewrÐa Γραφημάτwn apoteleÐ h apόdeixh thc eikasÐac tou Wagner από touc Neil Robertson kai Paul D. Seymour. sth σειρά ergasi¸n ‘Ελλάσσοna Γραφήματα’ apo to 1983 e¸c to 2011. H eikasÐa αυτή lèei όti sthn κλάση twn γραφημάtwn den υπάρχει άπειρη antialusÐda ¸c proc th sqèsh twn ελλασόnwn γραφημάτwn. H JewrÐa pou αναπτύχθηκε gia thn απόδειξη αυτής thc eikasÐac eÐqe kai èqei ακόμα σημαντικό antÐktupo tόσο sthn δομική όσο kai sthn algoriθμική JewrÐa Γραφημάτwn, άλλα kai se άλλα pedÐa όπως h Παραμετρική Poλυπλοκόthta. Sta πλάιsia thc απόδειξης oi suggrafeÐc eiσήγαγαν kai nèec paramètrouc πλά- touc. Se autèc ήτan h κλαδοαποσύνθεση kai to κλαδοπλάτoc ενός γραφήματoc. H παράμετρος αυτή χρησιμοποιήθηκε idiaÐtera sto σχεδιασμό algorÐjmwn kai sthn χρήση thc τεχνικής ‘διαίρει kai basÐleue’. -
Derandomizing Isolation Lemma for K3,3-Free and K5-Free Bipartite Graphs
Derandomizing Isolation Lemma for K3,3-free and K5-free Bipartite Graphs Rahul Arora1, Ashu Gupta2, Rohit Gurjar∗3, and Raghunath Tewari4 1 University of Toronto, Canada; and Indian Institute of Technology Kanpur, India [email protected], [email protected] 2 University of Illinois at Urbana-Champaign, USA [email protected] 3 HTW Aalen, Germany [email protected] 4 Indian Institute of Technology Kanpur, India [email protected] Abstract The perfect matching problem has a randomized NC algorithm, using the celebrated Isolation Lemma of Mulmuley, Vazirani and Vazirani. The Isolation Lemma states that giving a random weight assignment to the edges of a graph ensures that it has a unique minimum weight perfect matching, with a good probability. We derandomize this lemma for K3,3-free and K5-free bipart- ite graphs. That is, we give a deterministic log-space construction of such a weight assignment for these graphs. Such a construction was known previously for planar bipartite graphs. Our result implies that the perfect matching problem for K3,3-free and K5-free bipartite graphs is in SPL. It also gives an alternate proof for an already known result – reachability for K3,3-free and K5-free graphs is in UL. 1998 ACM Subject Classification F.1.3 Complexity Measures and Classes, G.2.2 Graph Theory Keywords and phrases bipartite matching, derandomization, isolation lemma, SPL, minor-free graph Digital Object Identifier 10.4230/LIPIcs.STACS.2016.10 1 Introduction The perfect matching problem is one of the most extensively studied problem in combinatorics, algorithms and complexity. -
Lecture 16: Constraint Satisfaction Problems 1 Max-Cut SDP
A Theorist's Toolkit (CMU 18-859T, Fall 2013) Lecture 16: Constraint Satisfaction Problems 10/30/2013 Lecturer: Ryan O'Donnell Scribe: Neal Barcelo 1 Max-Cut SDP Approximation Recall the Max-Cut problem from last lecture. We are given a graph G = (V; E) and our goal is to find a function F : V ! {−1; 1g that maximizes Pr(i;j)∼E[F (vi) 6= F (vj)]. As we saw before, this is NP-hard to do optimally, but we had the following SDP relaxation. 1 1 max avg(i;j)2Ef 2 − 2 yijg s.t. yvv = 1 8v Y = (yij) is symmetric and PSD Note that the second condition is a relaxation of the constraint \9xi s.t. yij = xixj" which is the constraint needed to exactly model the max-cut problem. Further, recall that a matrix being Positive Semi Definitie (PSD) is equivalent to the following characterizations. 1. Y 2 Rn×m is PSD. 2. There exists a U 2 Rm×n such that Y = U T U. 3. There exists joint random variables X1;::: Xn such that yuv = E[XuXv]. ~ ~ ~ ~ 4. There exists vectors U1;:::; Un such that yvw = hUv; Uwi. Using the last characterization, we can rewrite our SDP relaxation as follows 1 1 max avg(i;j)2Ef 2 − 2 h~vi; ~vjig 2 s.t. k~vik2 = 1 Vector ~vi for each vi 2 V First let's make sure this is actually a relaxation of our original problem. To see this, ∗ ∗ consider an optimal cut F : V ! f1; −1g. Then, if we let ~vi = (F (vi); 0;:::; 0), all of the constraints are satisfied and the objective value remains the same.