Computational Geometry
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Object Oriented Programming
No. 52 March-A pril'1990 $3.95 T H E M TEe H CAL J 0 URN A L COPIA Object Oriented Programming First it was BASIC, then it was structures, now it's objects. C++ afi<;ionados feel, of course, that objects are so powerful, so encompassing that anything could be so defined. I hope they're not placing bets, because if they are, money's no object. C++ 2.0 page 8 An objective view of the newest C++. Training A Neural Network Now that you have a neural network what do you do with it? Part two of a fascinating series. Debugging C page 21 Pointers Using MEM Keep C fro111 (C)rashing your system. An AT Keyboard Interface Use an AT keyboard with your latest project. And More ... Understanding Logic Families EPROM Programming Speeding Up Your AT Keyboard ((CHAOS MADE TO ORDER~ Explore the Magnificent and Infinite World of Fractals with FRAC LS™ AN ELECTRONIC KALEIDOSCOPE OF NATURES GEOMETRYTM With FracTools, you can modify and play with any of the included images, or easily create new ones by marking a region in an existing image or entering the coordinates directly. Filter out areas of the display, change colors in any area, and animate the fractal to create gorgeous and mesmerizing images. Special effects include Strobe, Kaleidoscope, Stained Glass, Horizontal, Vertical and Diagonal Panning, and Mouse Movies. The most spectacular application is the creation of self-running Slide Shows. Include any PCX file from any of the popular "paint" programs. FracTools also includes a Slide Show Programming Language, to bring a higher degree of control to your shows. -
A Computational Basis for Conic Arcs and Boolean Operations on Conic Polygons
A Computational Basis for Conic Arcs and Boolean Operations on Conic Polygons Eric Berberich, Arno Eigenwillig, Michael Hemmer Susan Hert, Kurt Mehlhorn, and Elmar Schomer¨ [eric|arno|hemmer|hert|mehlhorn|schoemer]@mpi-sb.mpg.de Max-Planck-Institut fur¨ Informatik, Stuhlsatzenhausweg 85 66123 Saarbruck¨ en, Germany Abstract. We give an exact geometry kernel for conic arcs, algorithms for ex- act computation with low-degree algebraic numbers, and an algorithm for com- puting the arrangement of conic arcs that immediately leads to a realization of regularized boolean operations on conic polygons. A conic polygon, or polygon for short, is anything that can be obtained from linear or conic halfspaces (= the set of points where a linear or quadratic function is non-negative) by regularized boolean operations. The algorithm and its implementation are complete (they can handle all cases), exact (they give the mathematically correct result), and efficient (they can handle inputs with several hundred primitives). 1 Introduction We give an exact geometry kernel for conic arcs, algorithms for exact computation with low-degree algebraic numbers, and a sweep-line algorithm for computing arrangements of curved arcs that immediately leads to a realization of regularized boolean operations on conic polygons. A conic polygon, or polygon for short, is anything that can be ob- tained from linear or conic halfspaces (= the set of points where a linear or quadratic function is non-negative) by regularized boolean operations (Figure 1). A regularized boolean operation is a standard boolean operation (union, intersection, complement) followed by regularization. Regularization replaces a set by the closure of its interior and eliminates dangling low-dimensional features. -
Convex Hulls
CONVEX HULLS PETR FELKEL FEL CTU PRAGUE [email protected] https://cw.felk.cvut.cz/doku.php/courses/a4m39vg/start Based on [Berg] and [Mount] Version from 16.11.2017 Talk overview Motivation and Definitions Graham’s scan – incremental algorithm Divide & Conquer Quick hull Jarvis’s March – selection by gift wrapping Chan’s algorithm – optimal algorithm www.cguu.com Felkel: Computational geometry (2) Convex hull (CH) – why to deal with it? Shape approximation of a point set or complex shapes (other common approximations include: minimal area enclosing rectangle, circle, and ellipse,…) – e.g., for collision detection Initial stage of many algorithms to filter out irrelevant points, e.g.: – diameter of a point set – minimum enclosing convex shapes (such as rectangle, circle, and ellipse) depend only on points on CH Felkel: Computational geometry (3) Convexity Line test !!! A set S is convex convex not convex – if for any points p,q S the lines segment pq S, or – if any convex combination of p and q is in S Convex combination of points p, q is any point that can be expressed as q p =1 (1 – ) p + q, where 0 1 =0 Convex hull CH(S) of set S – is (similar definitions) – the smallest set that contains S (convex) – or: intersection of all convex sets that contain S – Or in 2D for points: the smallest convex polygon containing all given points Felkel: Computational geometry (5) Definitions from topology in metric spaces Metric space – each two of points have defined a distance r r-neighborhood of a point p and radius r> 0 p = set of -
On Combinatorial Approximation Algorithms in Geometry Bruno Jartoux
On combinatorial approximation algorithms in geometry Bruno Jartoux To cite this version: Bruno Jartoux. On combinatorial approximation algorithms in geometry. Distributed, Parallel, and Cluster Computing [cs.DC]. Université Paris-Est, 2018. English. NNT : 2018PESC1078. tel- 02066140 HAL Id: tel-02066140 https://pastel.archives-ouvertes.fr/tel-02066140 Submitted on 13 Mar 2019 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. Université Paris-Est École doctorale MSTIC On Combinatorial Sur les algorithmes d’approximation Approximation combinatoires Algorithms en géométrie in Geometry Bruno Jartoux Thèse de doctorat en informatique soutenue le 12 septembre 2018. Composition du jury : Lilian Buzer ESIEE Paris Jean Cardinal Université libre de Bruxelles président du jury Guilherme Dias da Fonseca Université Clermont Auvergne rapporteur Jesús A. de Loera University of California, Davis rapporteur Frédéric Meunier École nationale des ponts et chaussées Nabil H. Mustafa ESIEE Paris directeur Vera Sacristán Universitat Politècnica de Catalunya Kasturi R. Varadarajan The University of Iowa rapporteur Last revised 16th December 2018. Thèse préparée au laboratoire d’informatique Gaspard-Monge (LIGM), équipe A3SI, dans les locaux d’ESIEE Paris. LIGM UMR 8049 ESIEE Paris Cité Descartes, bâtiment Copernic Département IT 5, boulevard Descartes Cité Descartes Champs-sur-Marne 2, boulevard Blaise-Pascal 77454 Marne-la-Vallée Cedex 2 93162 Noisy-le-Grand Cedex Bruno Jartoux 2018. -
2020 SIGACT REPORT SIGACT EC – Eric Allender, Shuchi Chawla, Nicole Immorlica, Samir Khuller (Chair), Bobby Kleinberg September 14Th, 2020
2020 SIGACT REPORT SIGACT EC – Eric Allender, Shuchi Chawla, Nicole Immorlica, Samir Khuller (chair), Bobby Kleinberg September 14th, 2020 SIGACT Mission Statement: The primary mission of ACM SIGACT (Association for Computing Machinery Special Interest Group on Algorithms and Computation Theory) is to foster and promote the discovery and dissemination of high quality research in the domain of theoretical computer science. The field of theoretical computer science is the rigorous study of all computational phenomena - natural, artificial or man-made. This includes the diverse areas of algorithms, data structures, complexity theory, distributed computation, parallel computation, VLSI, machine learning, computational biology, computational geometry, information theory, cryptography, quantum computation, computational number theory and algebra, program semantics and verification, automata theory, and the study of randomness. Work in this field is often distinguished by its emphasis on mathematical technique and rigor. 1. Awards ▪ 2020 Gödel Prize: This was awarded to Robin A. Moser and Gábor Tardos for their paper “A constructive proof of the general Lovász Local Lemma”, Journal of the ACM, Vol 57 (2), 2010. The Lovász Local Lemma (LLL) is a fundamental tool of the probabilistic method. It enables one to show the existence of certain objects even though they occur with exponentially small probability. The original proof was not algorithmic, and subsequent algorithmic versions had significant losses in parameters. This paper provides a simple, powerful algorithmic paradigm that converts almost all known applications of the LLL into randomized algorithms matching the bounds of the existence proof. The paper further gives a derandomized algorithm, a parallel algorithm, and an extension to the “lopsided” LLL. -
Mathematics of Bioinformatics ---Theory, Practice, and Applications (Part I)
Mathematics of Bioinformatics ---Theory, Practice, and Applications (Part I) Matthew He, Ph.D. Professor/Director Division of Math, Science, and Technology Nova Southeastern University, Florida, USA December 18-21, 2010, Hong Kong, China BIBM 2010 OUTLINE INTRODUCTION: FUNDAMENTAL QUESTIONS PART I : GENETIC CODES , BIOLOGICAL SEQUENCES , DNA AND PROTEIN STRUCTURES PART II: BIOLOGICAL FUNCTIONS, NETWORKS, SYSTEMS BIOLOGY AND COGNITIVE INFORMATICS TABLE OF TOPICS: PART I I. Bioinformatics and Mathematics 1.1 Introduction 12G1.2 Genet ic Co de an dMd Mat hemat ics 1.3 Mathematical Background 1.4 Converting Data to Knowledge 1.5 Big Picture: Informatics 16Chll1.6 Challenges and dP Perspect ives II. Genetic Codes, Matrices, and Symmetrical Techniques 2.1 Introduction 2.2 Matrix Theory and Symmetry Preliminaries 2.3 Genetic Codes and Matrices 2.4 Challenges and Perspectives III. Biological Sequences, Sequence Alignment, and Statistics 3.1 Introduction 3.2 Mathematical Sequences 3.3 Sequence Alignment 3.4 Sequence Analysis/Further Discussions 3.5 Challenges and Perspectives TABLE OF TOPICS: PART I IV. Structures of DNA and Knot Theory 4.1 Introduction 4.2 Knot Theory Preliminaries 4.3 DNA Knots and Links 4.4 Challenggpes and Perspectives V. Protein Structures, Geometry, and Topology 51I5.1 In tro duc tion 5.2 Computational Geometry and Topology 5.3 Protein Structures and Prediction 5.4 Statistical Approach and Discussions 5. 5 Cha llenges an d Perspec tives TABLE OF TOPICS: PART II VI. Biological Networks and Graph Theory 6. 1 Introduction 6.2 Graph Theory and Network Topology 6.3 Models of Biological Networks 6.4 Challenges and Perspectives VII. -
12 Binary Space Partitions the Painter's Algorithm
Computational Geometry Third Edition Mark de Berg · Otfried Cheong Marc van Kreveld · Mark Overmars Computational Geometry Algorithms and Applications Third Edition 123 Prof. Dr. Mark de Berg Dr. Marc van Kreveld Department of Mathematics Department of Information and Computer Science and Computing Sciences TU Eindhoven Utrecht University P.O. Box 513 P.O. Box 80.089 5600 MB Eindhoven 3508 TB Utrecht The Netherlands The Netherlands [email protected] [email protected] Dr. Otfried Cheong, ne´ Schwarzkopf Prof. Dr. Mark Overmars Department of Computer Science Department of Information KAIST and Computing Sciences Gwahangno 335, Yuseong-gu Utrecht University Daejeon 305-701 P.O. Box 80.089 Korea 3508 TB Utrecht [email protected] The Netherlands [email protected] ISBN 978-3-540-77973-5 e-ISBN 978-3-540-77974-2 DOI 10.1007/978-3-540-77974-2 ACM Computing Classification (1998): F.2.2, I.3.5 Library of Congress Control Number: 2008921564 © 2008, 2000, 1997 Springer-Verlag Berlin Heidelberg This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable for prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. -
Computational Geometry and Computer Graphics by Dobkin
Computational Geometry and Computer Graphics David P. Dobkin+ Department of Computer Science Princeton University Princeton, NJ 08544 ABSTRACT Computer graphics is a de®ning application for computational geometry. The interaction between these ®elds is explored through two scenarios. Spatial subdivisions studied from the viewpoint of computa- tional geometry are shown to have found application in computer graph- ics. Hidden surface removal problems of computer graphics have led to sweepline and area subdivision algorithms in computational geometry. The paper ends with two promising research areas with practical applica- tions: precise computation and polyhedral decomposition. 1. Introduction Computational geometry and computer graphics both consider geometric phenomena as they relate to computing. Computational geometry provides a theoretical foundation involving the study of algorithms and data structures for doing geometric computations. Computer graphics concerns the practical development of the software, hardware and algorithms necessary to create graphics (i.e. to display geometry) on the computer screen. At the interface lie many common problems which each discipline must solve to reach its goal. The techniques of the ®eld are often similar, sometimes dif- ferent. And, each ®elds has built a set of paradigms from which to operate. Thus, it is natural to ask the question: How do computer graphics and computational geometry interact? It is this question that motivates this paper. I explore here life at the interface between computational geometry and computer graphics. Rather than striving for completeness (see e.g. Yao [Ya] for a broader survey), I consider in detail two scenarios in which the two ®elds have overlapped. The goal is to trace the development of the ideas used in the two areas and to document the movement back and forth. -
Acknowledgements
Acknowledgements So many people have made a significant contribution to this thesis in various ways. Al- though all of them provided me with valuable information, a few played a more direct role in the preparation of this thesis. First of all, I extend my gratitude to my promotor, Mark Overmars, and my co-promotor, Otfried Cheong for their perfect supervision. During the writing of my Ph.D. thesis, Mark Overmars provided many corrections and suggestions for improvement of the manuscript, for which I’m grateful. I am deeply indebted to Otfried Cheong: When I was a M.Sc. student at POSTECH in Korea, he motivated me to do a Ph.D. in Computational Geometry and provided me an offer of being his first Ph.D. student. With unfailing courtesy and monumental patience, he have supervised me in a perfect way, in Hong Kong and the Netherlands. I would also like to thank Siu-Wing Cheng and Mordecai Golin. Siu-Wing Cheng had advised me in various ways, and we had worked together on several problems. Mordecai Golin had showed me interesting geometry problems that I challenged to solve. I should also thank Rene´ van Oostrum. When we were in Hong Kong, I learned from him how to develope films and print photographs. He also helped me a lot when I settled down to the life in the Netherlands, and translated “Summary” into Dutch for this thesis. I must acknowledge my debt to the co-authors of the papers of which this thesis is com- posed: apart from Otfried Cheong, Siu-Wing Cheng and Rene´ van Oostrum, these are Mark de Berg, Prosenjit Bose, Dan Halperin, Jirˇ´ı Matousek,ˇ and Jack Snoeyink. -
3SUM with Preprocessing
Data Structures Meet Cryptography: 3SUM with Preprocessing Alexander Golovnev Siyao Guo Thibaut Horel Harvard NYU Shanghai MIT [email protected] [email protected] [email protected] Sunoo Park Vinod Vaikuntanathan MIT & Harvard MIT [email protected] [email protected] Abstract This paper shows several connections between data structure problems and cryptography against preprocessing attacks. Our results span data structure upper bounds, cryptographic applications, and data structure lower bounds, as summarized next. First, we apply Fiat{Naor inversion, a technique with cryptographic origins, to obtain a data-structure upper bound. In particular, our technique yields a suite of algorithms with space S and (online) time T for a preprocessing version of the N-input 3SUM problem where S3 · T = Oe(N 6). This disproves a strong conjecture (Goldstein et al., WADS 2017) that there is no data structure that solves this problem for S = N 2−δ and T = N 1−δ for any constant δ > 0. Secondly, we show equivalence between lower bounds for a broad class of (static) data struc- ture problems and one-way functions in the random oracle model that resist a very strong form of preprocessing attack. Concretely, given a random function F :[N] ! [N] (accessed as an oracle) we show how to compile it into a function GF :[N 2] ! [N 2] which resists S-bit prepro- cessing attacks that run in query time T where ST = O(N 2−") (assuming a corresponding data structure lower bound on 3SUM). In contrast, a classical result of Hellman tells us that F itself can be more easily inverted, say with N 2=3-bit preprocessing in N 2=3 time. -
Jean-Paul Laumond
San Francisco CA 1:51:39 m4v Jean-Paul Laumond An interview conducted by Selma Šabanović with Matthew R. Francisco September 27 2011 Q: So if we can start with your name, and where you were born and when? Jean-Paul Laumond: Okay I'm Jean-Paul Laumond. I born in '53 in countryside North from Toulouse in France, in a small village in France. Q: And where did you go to school? Jean-Paul Laumond: I have been to school in Brive which is the main city of the area in that countryside, and after that I entered the university in Toulouse, first by following special training in French which is what we call the “classe préparatoire” for the engineering school. But in fact after two years I moved to university to in the mathematics department and I got a diploma to be a teacher in mathematics. Q: What did you study while you were in university and how did you decide on that particular direction? Jean-Paul Laumond: <clears throat> At the very beginning, I was not very convinced by the choice I made to enter into engineering, because engineering is to go towards the industry world while I was more interested by the academic position. And at that time I didn't know that engineering could be a possibility to enter the university or to make then. Then for me it was just pure abstraction or like literature, and then this is why and of course I was very enthusiastic with mathematics, and this is why I chosen to enter in the university in the department of mathematics. -
Creative and Coordinated Computation
Creative and Coordinated Computation Inaugural address delivered by Marc van Kreveld Professor in Computational Geometry and its Application Department of Information and Computing Sciences Faculty of Science Utrecht University on Friday, January 17, 2014 Utrecht, The Netherlands c Marc van Kreveld, 2014 Rector magnificus, colleagues, family, friends, ... I am honored to stand before you today, and have the opportunity to tell you all about my work, my research, and some other things that I will get to later. It is Friday afternoon, after 4 o'clock, and I realize that most people would call this the beginning of the weekend. However, I am going to be talking about scientific work for a while, so bear with me. I promise that I will not show any formulas, but I will show many illustrations. I truly hope that everyone in the audience will hear something worthwhile in this lecture. My research is related to geometry, a branch of mathematics that deals with objects, like points, lines, triangles and circles, and addresses rela- tions between these objects, like distances, intersections, and containment, or properties like lengths, areas, and angles. My research is also about al- gorithms, the use of automated methods to execute tasks, and the formal study of these methods. The third aspect of my research is applications, because points, triangles and circles can have a concrete meaning in appli- cations only, and the application dictates which tasks we want to perform on the geometry. The title of this speech, \Creative and Coordinated Computation", may seem contradictory: a creative process is often not coordinated, and more- over, coordination may obstruct creativity.