5 PSEUDOLINE ARRANGEMENTS Stefan Felsner and Jacob E
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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. -
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. -
30 ARRANGEMENTS Dan Halperin and Micha Sharir
30 ARRANGEMENTS Dan Halperin and Micha Sharir INTRODUCTION Given a finite collection of geometric objects such as hyperplanes or spheres in Rd, the arrangement ( S) is the decomposition of Rd into connected open cells of dimensions 0, 1,...,d AinducedS by . Besides being interesting in their own right, ar- rangements of hyperplanes have servedS as a unifying structure for many problems in discrete and computational geometry. With the recent advances in the study of arrangements of curved (algebraic) surfaces, arrangements have emerged as the underlying structure of geometric problems in a variety of “physical world” applica- tion domains such as robot motion planning and computer vision. This chapter is devoted to arrangements of hyperplanes and of curved surfaces in low-dimensional Euclidean space, with an emphasis on combinatorics and algorithms. In the first section we introduce basic terminology and combinatorics of ar- rangements. In Section 30.2 we describe substructures in arrangements and their combinatorial complexity. Section 30.3 deals with data structures for representing arrangements and with special refinements of arrangements. The following two sections focus on algorithms: algorithms for constructing full arrangements are described in Section 30.4, and algorithms for constructing substructures in Sec- tion 30.5. In Section 30.6 we discuss the relation between arrangements and other structures. Several applications of arrangements are reviewed in Section 30.7. Sec- tion 30.8 deals with robustness issues when implementing algorithms and data structures for arrangements and Section 30.9 surveys software implementations. We conclude in Section 30.10 with a brief review of Davenport-Schinzel sequences, a combinatorial structure that plays an important role in the analysis of arrange- ments. -
Arrangements of Approaching Pseudo-Lines
Arrangements of Approaching Pseudo-Lines Stefan Felsner∗1, Alexander Pilzy2, and Patrick Schnider3 1Institut f¨urMathematik, Technische Universit¨atBerlin, [email protected] 2Institute of SoftwareTechnology, Graz University of Technology, Austria, [email protected] 3Department of Computer Science, ETH Z¨urich, Switzerland, [email protected] January 24, 2020 Abstract We consider arrangements of n pseudo-lines in the Euclidean plane where each pseudo-line `i is represented by a bi-infinite connected x-monotone curve fi(x), x 2 R, s.t. for any two pseudo-lines `i and `j with i < j, the function x 7! fj (x) − fi(x) is monotonically decreasing and surjective (i.e., the pseudo-lines approach each other until they cross, and then move away from each other). We show that such arrangements of approaching pseudo-lines, under some aspects, behave similar to arrangements of lines, while for other aspects, they share the freedom of general pseudo-line arrangements. For the former, we prove: • There are arrangements of pseudo-lines that are not realizable with approaching pseudo-lines. • Every arrangement of approaching pseudo-lines has a dual generalized configura- tion of points with an underlying arrangement of approaching pseudo-lines. For the latter, we show: 2 • There are 2Θ(n ) isomorphism classes of arrangements of approaching pseudo-lines (while there are only 2Θ(n log n) isomorphism classes of line arrangements). • It can be decided in polynomial time whether an allowable sequence is realizable by an arrangement of approaching pseudo-lines. Furthermore, arrangements of approaching pseudo-lines can be transformed into each arXiv:2001.08419v1 [cs.CG] 23 Jan 2020 other by flipping triangular cells, i.e., they have a connected flip graph, and every bichromatic arrangement of this type contains a bichromatic triangular cell. -
Sylvester-Gallai-Like Theorems for Polygons in the Plane
RC25263 (W1201-047) January 24, 2012 Mathematics IBM Research Report Sylvester-Gallai-like Theorems for Polygons in the Plane Jonathan Lenchner IBM Research Division Thomas J. Watson Research Center P.O. Box 704 Yorktown Heights, NY 10598 Research Division Almaden - Austin - Beijing - Cambridge - Haifa - India - T. J. Watson - Tokyo - Zurich LIMITED DISTRIBUTION NOTICE: This report has been submitted for publication outside of IBM and will probably be copyrighted if accepted for publication. It has been issued as a Research Report for early dissemination of its contents. In view of the transfer of copyright to the outside publisher, its distribution outside of IBM prior to publication should be limited to peer communications and specific requests. After outside publication, requests should be filled only by reprints or legally obtained copies of the article (e.g. , payment of royalties). Copies may be requested from IBM T. J. Watson Research Center , P. O. Box 218, Yorktown Heights, NY 10598 USA (email: [email protected]). Some reports are available on the internet at http://domino.watson.ibm.com/library/CyberDig.nsf/home . Sylvester-Gallai-like Theorems for Polygons in the Plane Jonathan Lenchner¤ Abstract Given an arrangement of lines in the plane, an ordinary point is a point of intersection of precisely two of the lines. Motivated by a desire to understand the fine structure of ordinary points in line arrangements, we consider the following problem: given a polygon P in the plane and a family of lines passing into the interior of P, how many ordinary intersection points must there be on or inside of P? We answer this question for a variety of different types of polygons. -
Stony Brook University
SSStttooonnnyyy BBBrrrooooookkk UUUnnniiivvveeerrrsssiiitttyyy The official electronic file of this thesis or dissertation is maintained by the University Libraries on behalf of The Graduate School at Stony Brook University. ©©© AAAllllll RRRiiiggghhhtttsss RRReeessseeerrrvvveeeddd bbbyyy AAAuuuttthhhooorrr... Combinatorics and Complexity in Geometric Visibility Problems A Dissertation Presented by Justin G. Iwerks to The Graduate School in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Applied Mathematics and Statistics (Operations Research) Stony Brook University August 2012 Stony Brook University The Graduate School Justin G. Iwerks We, the dissertation committee for the above candidate for the Doctor of Philosophy degree, hereby recommend acceptance of this dissertation. Joseph S. B. Mitchell - Dissertation Advisor Professor, Department of Applied Mathematics and Statistics Esther M. Arkin - Chairperson of Defense Professor, Department of Applied Mathematics and Statistics Steven Skiena Distinguished Teaching Professor, Department of Computer Science Jie Gao - Outside Member Associate Professor, Department of Computer Science Charles Taber Interim Dean of the Graduate School ii Abstract of the Dissertation Combinatorics and Complexity in Geometric Visibility Problems by Justin G. Iwerks Doctor of Philosophy in Applied Mathematics and Statistics (Operations Research) Stony Brook University 2012 Geometric visibility is fundamental to computational geometry and its ap- plications in areas such as robotics, sensor networks, CAD, and motion plan- ning. We explore combinatorial and computational complexity problems aris- ing in a collection of settings that depend on various notions of visibility. We first consider a generalized version of the classical art gallery problem in which the input specifies the number of reflex vertices r and convex vertices c of the simple polygon (n = r + c). -
Arrangements of Approaching Pseudo-Lines
1 Arrangements of Approaching Pseudo-Lines ∗1 †2 3 2 Stefan Felsner , Alexander Pilz , and Patrick Schnider 1 3 Institut f¨urMathematik, Technische Universit¨atBerlin, 4 [email protected] 2 5 Institute of SoftwareTechnology, Graz University of Technology, Austria, 6 [email protected] 3 7 Department of Computer Science, ETH Z¨urich, Switzerland, 8 [email protected] 9 August 23, 2020 10 Abstract 11 We consider arrangements of n pseudo-lines in the Euclidean plane where each 12 pseudo-line `i is represented by a bi-infinite connected x-monotone curve fi(x), x 2 R, 13 such that for any two pseudo-lines `i and `j with i < j, the function x 7! fj (x)−fi(x) is 14 monotonically decreasing and surjective (i.e., the pseudo-lines approach each other until 15 they cross, and then move away from each other). We show that such arrangements of 16 approaching pseudo-lines, under some aspects, behave similar to arrangements of lines, 17 while for other aspects, they share the freedom of general pseudo-line arrangements. 18 For the former, we prove: 19 • There are arrangements of pseudo-lines that are not realizable with approaching 20 pseudo-lines. 21 • Every arrangement of approaching pseudo-lines has a dual generalized configura- 22 tion of points with an underlying arrangement of approaching pseudo-lines. 23 For the latter, we show: Θ(n2) 24 • There are 2 isomorphism classes of arrangements of approaching pseudo-lines Θ(n log n) 25 (while there are only 2 isomorphism classes of line arrangements). -
Preprocessing Imprecise Points and Splitting Triangulations
Preprocessing Imprecise Points and Splitting Triangulations Marc van Kreveld Maarten L¨offler Joseph S.B. Mitchell Technical Report UU-CS-2009-007 March 2009 Department of Information and Computing Sciences Utrecht University, Utrecht, The Netherlands www.cs.uu.nl ISSN: 0924-3275 Department of Information and Computing Sciences Utrecht University P.O. Box 80.089 3508 TB Utrecht The Netherlands Preprocessing Imprecise Points and Splitting Triangulations∗ Marc van Kreveld Maarten L¨offler Joseph S.B. Mitchell [email protected] [email protected] [email protected] Abstract Traditional algorithms in computational geometry assume that the input points are given precisely. In practice, data is usually imprecise, but information about the imprecision is often available. In this context, we investigate what the value of this information is. We show here how to preprocess a set of disjoint regions in the plane of total complexity n in O(n log n) time so that if one point per set is specified with precise coordinates, a triangulation of the points can be computed in linear time. In our solution, we solve another problem which we believe to be of independent interest. Given a triangulation with red and blue vertices, we show how to compute a triangulation of only the blue vertices in linear time. 1 Introduction Computational geometry deals with computing structure in 2-dimensional space (or higher). The most popular input is a set of points, on which something useful is then computed, for example, a triangulation. Algorithms for these tasks have been developed many years ago, and are provably fast and correct. -
Computational Geometry Lecture Notes1 HS 2012
Computational Geometry Lecture Notes1 HS 2012 Bernd Gärtner <[email protected]> Michael Hoffmann <[email protected]> Tuesday 15th January, 2013 1Some parts are based on material provided by Gabriel Nivasch and Emo Welzl. We also thank Tobias Christ, Anna Gundert, and May Szedlák for pointing out errors in preceding versions. Contents 1 Fundamentals 7 1.1 ModelsofComputation ............................ 7 1.2 BasicGeometricObjects. 8 2 Polygons 11 2.1 ClassesofPolygons............................... 11 2.2 PolygonTriangulation . 15 2.3 TheArtGalleryProblem ........................... 19 3 Convex Hull 23 3.1 Convexity.................................... 24 3.2 PlanarConvexHull .............................. 27 3.3 Trivialalgorithms ............................... 29 3.4 Jarvis’Wrap .................................. 29 3.5 GrahamScan(SuccessiveLocalRepair) . .... 31 3.6 LowerBound .................................. 33 3.7 Chan’sAlgorithm................................ 33 4 Line Sweep 37 4.1 IntervalIntersections . ... 38 4.2 SegmentIntersections . 38 4.3 Improvements.................................. 42 4.4 Algebraic degree of geometric primitives . ....... 42 4.5 Red-BlueIntersections . .. 45 5 Plane Graphs and the DCEL 51 5.1 TheEulerFormula............................... 52 5.2 TheDoubly-ConnectedEdgeList. .. 53 5.2.1 ManipulatingaDCEL . 54 5.2.2 GraphswithUnboundedEdges . 57 5.2.3 Remarks................................. 58 3 Contents CG 2012 6 Delaunay Triangulations 61 6.1 TheEmptyCircleProperty . 64 6.2 TheLawsonFlipalgorithm -
Chapter 12 Pseudotriangulations
Chapter 12 Pseudotriangulations We have seen that arrangements and visibility graphs are useful and powerful models for motion planning. However, these structures can be rather large and thus expensive to build and to work with. Let us therefore consider a simpli ed problem: for a robot which is positioned in some environment of obstacles, which and where is the next obstacle that it would hit, if it continues to move linearly in some direction? This type of problem is known as a ray shooting query because we imagine to shoot a ray starting from the current position in a certain direction and want to know what is the rst object hit by this ray. If the robot is modeled as a point and the environment as a simple polygon, we arrive at the following problem. Problem 8 (Ray-shooting in a simple polygon.) Given a simple polygon P = (p1, . , pn), a point q 2 R2, and a ray r emanating from q, which is the rst edge of P hit by r? r q P Figure 12.1: An instance of the ray-shooting problem within a simple polygon. In the end, we would like to have a data structure to preprocess a given polygon such that a ray shooting query can be answered eciently for any query ray starting somewhere inside P. As a warmup, let us look at the case that P is a convex polygon. Here the problem is easy: Supposing we are given the vertices of P in an array-like structure, the boundary 103 Chapter 12. -
The Symplectic Geometry of Polygons in Euclidean Space
The Symplectic Geometry of Polygons in Euclidean Space Michael Kap ovich and John J Millson March Abstract We study the symplectic geometry of mo duli spaces M of p oly r gons with xed side lengths in Euclidean space We show that M r has a natural structure of a complex analytic space and is complex 2 n analytically isomorphic to the weighted quotient of S constructed by Deligne and Mostow We study the Hamiltonian ows on M ob r tained by b ending the p olygon along diagonals and show the group generated by such ows acts transitively on M We also relate these r ows to the twist ows of Goldman and JereyWeitsman Contents Intro duction Mo duli of p olygons and weighted quotients of conguration spaces of p oints on the sphere Bending ows and p olygons Actionangle co ordinates This research was partially supp orted by NSF grant DMS at University of Utah Kap ovich and NSF grant DMS the University of Maryland Millson The connection with gauge theory and the results of Gold man and JereyWeitsman Transitivity of b ending deformations Bending of quadrilaterals Deformations of ngons Intro duction Let P b e the space of all ngons with distinguished vertices in Euclidean n 3 space E An ngon P is determined by its vertices v v These vertices 1 n are joined in cyclic order by edges e e where e is the oriented line 1 n i segment from v to v Two p olygons P v v and Q w w i i+1 1 n 1 n are identied if and only if there exists an orientation preserving isometry g 3 of E which -
Pseudotriangulations and the Expansion Polytope
99 Pseudotriangulations G¨unter Rote Freie Universit¨atBerlin, Institut f¨urInformatik 2nd Winter School on Computational Geometry Tehran, March 2–6, 2010 Day 5 Literature: Pseudo-triangulations — a survey. G.Rote, F.Santos, I.Streinu, 2008 98 Outline 1. Motivation: ray shooting 2. Pseudotriangulations: definitions and properties 3. Rigidity, Laman graphs 4. Rigidity: kinematics of linkages 5. Liftings of pseudotriangulations to 3 dimensions 97 1. Motivation: Ray Shooting in a Simple Polygon 97 1. Motivation: Ray Shooting in a Simple Polygon Walking in a triangulation: Walk to starting point. Then walk along the ray. 97 1. Motivation: Ray Shooting in a Simple Polygon Walking in a triangulation: Walk to starting point. Then walk along the ray. O(n) steps in the worst case. 96 Triangulations of a convex polygon 1 12 2 11 3 10 4 9 5 8 6 7 96 Triangulations of a convex polygon 1 1 12 2 12 2 11 3 11 3 10 4 10 4 9 5 9 5 8 6 8 6 7 7 balanced triangulation A path crosses O(log n) triangles. 95 Triangulations of a simple polygon 1 5 2 12 1 12 2 3 11 10 11 4 3 6 8 10 4 9 7 9 5 balanced triangulation: balanced geodesic8 triangulation:6 An edge crosses O(log n) An edge crosses O7 (log n) triangles. pseudotriangles. [Chazelle, Edelsbrunner, Grigni, Guibas, Hershberger, Sharir, Snoeyink 1994] 95 Triangulations of a simple polygon 1 5 2 12 1 12 2 3 11 10 11 4 3 corner 6 8 pseudotriangle 10 4 tail 9 7 9 5 balanced triangulation: balanced geodesic8 triangulation:6 An edge crosses O(log n) An edge crosses O7 (log n) triangles.