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Forbidden Configurations in Discrete Geometry

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Forbidden Configurations in Discrete Geometry FORBIDDEN CONFIGURATIONS IN DISCRETE GEOMETRY This book surveys the mathematical and computational properties of finite sets of points in the plane, covering recent breakthroughs on important problems in discrete geometry and listing many open prob- lems. It unifies these mathematical and computational views using for- bidden configurations, which are patterns that cannot appear in sets with a given property, and explores the implications of this unified view. Written with minimal prerequisites and featuring plenty of fig- ures, this engaging book will be of interest to undergraduate students and researchers in mathematics and computer science. Most topics are introduced with a related puzzle or brain-teaser. The topics range from abstract issues of collinearity, convexity, and general position to more applied areas including robust statistical estimation and network visualization, with connections to related areas of mathe- matics including number theory, graph theory, and the theory of per- mutation patterns. Pseudocode is included for many algorithms that compute properties of point sets. David Eppstein is Chancellor’s Professor of Computer Science at the University of California, Irvine. He has more than 350 publications on subjects including discrete and computational geometry, graph the- ory, graph algorithms, data structures, robust statistics, social network analysis and visualization, mesh generation, biosequence comparison, exponential algorithms, and recreational mathematics. He has been the moderator for data structures and algorithms on arXiv.org since 2006 and is a major contributor to Wikipedia’s articles on mathematics and theoretical computer science. He was elected as an ACM fellow in 2012. Forbidden Configurations in Discrete Geometry DAVID EPPSTEIN University of California, Irvine University Printing House, Cambridge CB2 8BS, United Kingdom One Liberty Plaza, 20th Floor, New York, NY 10006, USA 477 Williamstown Road, Port Melbourne, VIC 3207, Australia 314–321, 3rd Floor, Plot 3, Splendor Forum, Jasola District Centre, New Delhi - 110025, India 79 Anson Road, #06-04/06, Singapore 079906 Cambridge University Press is part of the University of Cambridge. It furthers the University’s mission by disseminating knowledge in the pursuit of education, learning, and research at the highest international levels of excellence. www.cambridge.org Information on this title: www.cambridge.org/9781108423915 DOI: 10.1017/9781108529167 © David Eppstein 2018 This publication is in copyright. Subject to statutory exception and to the provisions of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University Press. First published 2018 Printed in the United States of America by Sheridan Books, Inc. A catalogue record for this publication is available from the British Library ISBN 978-1-108-42391-5 Hardback Cambridge University Press has no responsibility for the persistence or accuracy of URLs for external or third-party internet websites referred to in this publication and does not guarantee that any content on such websites is, or will remain, accurate or appropriate. Contents Acknowledgments page ix 1AHappyEnding 1 2Overview 4 3 Configurations 8 3.1 Small Configurations 8 3.2 Orientations and Order Types 10 3.3 Defining Configurations 12 3.4 Example Configurations 13 3.5 Partitions by Lines 14 4 Subconfigurations 19 4.1 Definitions 19 4.2 Partial Orders and Quasi-Orders 20 4.3 Antichains 21 4.4 Well-Quasi-Ordering 22 5 Properties, Parameters, and Obstacles 24 5.1 Definitions 24 5.2 Obstacles 25 5.3 Parameters from Obstacles 27 5.4 Relation between Parameters 28 5.5 Comparing Parameters 29 6 Computing with Configurations 33 6.1 Input Representation 33 6.2 Computing the Order Type 34 6.3 Finding Subconfigurations 36 6.4 O-Notation 37 6.5 Property Testing 38 v vi Contents 7 Complexity Theory 43 7.1 Decision Problems and Complexity Classes 43 7.2 Approximation Algorithms 45 7.3 Parameterized Complexity 46 7.4 Complexity of Obstacle-Based Parameters 47 7.5 Parameterized Complexity of hitting 49 8 Collinearity 53 8.1 Planting Orchards 53 8.2 Definitions 55 8.3 Inequalities 56 8.4 Finding All Lines 57 8.5 Complexity of Line Covers 61 8.6 Property Testing 62 8.7 Obstacle Size and Well-Quasi-Ordering 63 8.8 Approximation 67 9 General Position 72 9.1 No-Three-in-Line 72 9.2 Finding General-Position Subconfigurations 75 9.3 Property Testing 78 9.4 Inequalities 79 9.5 Approximation 81 9.6 Entropy Compression 83 10 General-Position Partitions 87 10.1 Glimpses of Higher Dimensions 87 10.2 Inequalities 92 10.3 Complexity 93 10.4 Approximation 98 10.5 Configurations Covered by Few Lines 101 11 Convexity 106 11.1 Happy Endings Revisited 106 11.2 Additional Examples 110 11.3 Obstacles 113 11.4 Finding Convex Subconfigurations 115 11.5 Finding Convex Partitions 117 11.6 Inequalities 119 11.7 Well-Quasi-Ordering 122 12 More on Convexity 125 12.1 Weak Convexity 125 12.2 Complexity and Well-Quasi-Ordering 127 12.3 Crossing Families 128 12.4 Onion Layers 129 12.5 Inequalities 132 Contents vii 12.6 Polygons Containing Many Points 134 12.7 Depth and Deep Points 135 13 Integer Realizations 141 13.1 The Perles Configuration 142 13.2 Polygons 144 13.3 Points on Two Lines 145 13.4 Non-Cocircularity 147 13.5 Harborth, Erdos,˝ and Ulam 148 14 The Stretched Geometry of Permutations 151 14.1 The Stretching Transformation 151 14.2 Permutations and Patterns 155 14.3 Stair-Convexity 157 14.4 Characterization 160 14.5 Recognition 163 14.6 Finding Subconfigurations 164 15 Configurations from Graphs 166 15.1 Definitions 166 15.2 Convex Embeddings of Arbitrary Graphs 169 15.3 Hardness Proofs Based on Convex Embeddings 171 15.4 Three-Line Embeddings of Bipartite Graphs 172 16 Universality 177 16.1 Planarity 177 16.2 Definition 179 16.3 Obstacles 181 16.4 Relations to Other Parameters 185 17 Stabbing 188 17.1 Path and Tree Stabbing Numbers 189 17.2 Stabbing versus Shattering and Length 190 17.3 Inequalities and Complexity 195 17.4 Unstabbed Segments 197 17.5 Well-Quasi-Ordering 200 17.6 Applications of Unstabbed Segments 202 18 The Big Picture 207 18.1 Properties 207 18.2 Parameters 208 18.3 Only the Beginning 210 Bibliography 211 Index 223 Acknowledgments This book stemmed from an invitation to present the Erdos˝ Memorial Lecture at the 29th Canadian Conference on Computational Geometry (held in Ottawa in July 2017) and would not have existed without that invitation. I would like to thank the many people who have given me helpful advice on it, especially Jean Cardinal, Sariel Har-Peled, Stefan Langerman, Joe O’Rourke, János Pach, Vijay Vazirani, and several anonymous reviewers. I am also grateful for the careful copyediting of Maureen Eppstein. The research in this work was supported in part by the US National Science Foundation under grants CCF-1618301 and CCF-1616248. ix 1 A Happy Ending In the early 1930s, Hungarian mathematician Esther Klein made a discovery that, despite its apparent simplicity, would kick off two major lines of research in mathematics. Klein observed that every set of five points in the plane has either three points in a line or four points in a convex quadrilateral. This became one of the first results in the two fields of discrete geometry (the study of combinatorial properties of geometric objects such as points in the Euclidean plane, and the subject of this book) and Ramsey theory (the study of the phenomenon that unstructured mathematical systems often contain highly structured subsystems). Klein’s observation can be proven by a simple case analysis that consid- ers how many of the points belong to their convex hull. The convex hull is a convex polygon, having some of the given points as its vertices and contain- ing the others. It can be defined mathematically in many ways, for instance as the smallest-area convex polygon that contains all of the given points or as the largest-area simple polygon whose vertices all belong to the given points. The convex hull of points that are not all on a line always has at least three vertices (for otherwise it could not enclose a nonzero area) and, for five given points, at most five vertices. If it has five vertices, any four of them form a convex quadri- lateral, and if it has four vertices then it is a convex quadrilateral. The remaining possibility for the convex hull is a triangle, with the other two points either part of a line of three points or inside the triangle. When both points are inside, and the line through them misses the triangle vertices, it also misses one side of the triangle. In this case the two interior points and the two points on the missed side form a convex quadrilateral (Figure 1.1). The challenge of extending and generalizing this observation was taken up by two of Klein’s friends, Paul Erdos˝ and George Szekeres. They proved that, 1 2 A Happy Ending Figure 1.1. Five points with no three in line always contain a convex quadrilat- eral, either four vertices of the convex hull or (as shown here) two vertices of a tri- angular convex hull and two interior vertices. When the convex hull is a triangle, the line through the remaining two points has two triangle vertices on one of its sides, and those four points are in convex position. for every k,aconvexk-gon can be found in all large enough sets of points, as long as no three of the points lie on a line.1 Klein later married Szekeres, and their marriage is commemorated in the name of Erdos˝ and Szekeres’s result: the happy ending theorem.
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