Theorems of Points and Planes in Three-Dimensional Projective Space
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Projective Geometry: a Short Introduction
Projective Geometry: A Short Introduction Lecture Notes Edmond Boyer Master MOSIG Introduction to Projective Geometry Contents 1 Introduction 2 1.1 Objective . .2 1.2 Historical Background . .3 1.3 Bibliography . .4 2 Projective Spaces 5 2.1 Definitions . .5 2.2 Properties . .8 2.3 The hyperplane at infinity . 12 3 The projective line 13 3.1 Introduction . 13 3.2 Projective transformation of P1 ................... 14 3.3 The cross-ratio . 14 4 The projective plane 17 4.1 Points and lines . 17 4.2 Line at infinity . 18 4.3 Homographies . 19 4.4 Conics . 20 4.5 Affine transformations . 22 4.6 Euclidean transformations . 22 4.7 Particular transformations . 24 4.8 Transformation hierarchy . 25 Grenoble Universities 1 Master MOSIG Introduction to Projective Geometry Chapter 1 Introduction 1.1 Objective The objective of this course is to give basic notions and intuitions on projective geometry. The interest of projective geometry arises in several visual comput- ing domains, in particular computer vision modelling and computer graphics. It provides a mathematical formalism to describe the geometry of cameras and the associated transformations, hence enabling the design of computational ap- proaches that manipulates 2D projections of 3D objects. In that respect, a fundamental aspect is the fact that objects at infinity can be represented and manipulated with projective geometry and this in contrast to the Euclidean geometry. This allows perspective deformations to be represented as projective transformations. Figure 1.1: Example of perspective deformation or 2D projective transforma- tion. Another argument is that Euclidean geometry is sometimes difficult to use in algorithms, with particular cases arising from non-generic situations (e.g. -
Slices, Slabs, and Sections of the Unit Hypercube
Slices, Slabs, and Sections of the Unit Hypercube Jean-Luc Marichal Michael J. Mossinghoff Institute of Mathematics Department of Mathematics University of Luxembourg Davidson College 162A, avenue de la Fa¨ıencerie Davidson, NC 28035-6996 L-1511 Luxembourg USA Luxembourg [email protected] [email protected] Submitted: July 27, 2006; Revised: August 6, 2007; Accepted: January 21, 2008; Published: January 29, 2008 Abstract Using combinatorial methods, we derive several formulas for the volume of convex bodies obtained by intersecting a unit hypercube with a halfspace, or with a hyperplane of codimension 1, or with a flat defined by two parallel hyperplanes. We also describe some of the history of these problems, dating to P´olya’s Ph.D. thesis, and we discuss several applications of these formulas. Subject Class: Primary: 52A38, 52B11; Secondary: 05A19, 60D05. Keywords: Cube slicing, hyperplane section, signed decomposition, volume, Eulerian numbers. 1 Introduction In this note we study the volumes of portions of n-dimensional cubes determined by hyper- planes. More precisely, we study slices created by intersecting a hypercube with a halfspace, slabs formed as the portion of a hypercube lying between two parallel hyperplanes, and sections obtained by intersecting a hypercube with a hyperplane. These objects occur natu- rally in several fields, including probability, number theory, geometry, physics, and analysis. In this paper we describe an elementary combinatorial method for calculating volumes of arbitrary slices, slabs, and sections of a unit cube. We also describe some applications that tie these geometric results to problems in analysis and combinatorics. Some of the results we obtain here have in fact appeared earlier in other contexts. -
The Projective Geometry of the Spacetime Yielded by Relativistic Positioning Systems and Relativistic Location Systems Jacques Rubin
The projective geometry of the spacetime yielded by relativistic positioning systems and relativistic location systems Jacques Rubin To cite this version: Jacques Rubin. The projective geometry of the spacetime yielded by relativistic positioning systems and relativistic location systems. 2014. hal-00945515 HAL Id: hal-00945515 https://hal.inria.fr/hal-00945515 Submitted on 12 Feb 2014 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. The projective geometry of the spacetime yielded by relativistic positioning systems and relativistic location systems Jacques L. Rubin (email: [email protected]) Université de Nice–Sophia Antipolis, UFR Sciences Institut du Non-Linéaire de Nice, UMR7335 1361 route des Lucioles, F-06560 Valbonne, France (Dated: February 12, 2014) As well accepted now, current positioning systems such as GPS, Galileo, Beidou, etc. are not primary, relativistic systems. Nevertheless, genuine, relativistic and primary positioning systems have been proposed recently by Bahder, Coll et al. and Rovelli to remedy such prior defects. These new designs all have in common an equivariant conformal geometry featuring, as the most basic ingredient, the spacetime geometry. In a first step, we show how this conformal aspect can be the four-dimensional projective part of a larger five-dimensional geometry. -
On the Theory of Point Weight Designs Royal Holloway
On the theory of Point Weight Designs Alexander W. Dent Technical Report RHUL–MA–2003–2 26 March 2001 Royal Holloway University of London Department of Mathematics Royal Holloway, University of London Egham, Surrey TW20 0EX, England http://www.rhul.ac.uk/mathematics/techreports Abstract A point-weight incidence structure is a structure of blocks and points where each point is associated with a positive integer weight. A point-weight design is a point-weight incidence structure where the sum of the weights of the points on a block is constant and there exist some condition that specifies the number of blocks that certain sets of points lie on. These structures share many similarities to classical designs. Chapter one provides an introduction to design theory and to some of the existing theory of point-weight designs. Chapter two develops a new type of point-weight design, termed a row-sum point-weight design, that has some of the matrix properties of classical designs. We examine the combinatorial aspects of these designs and show that a Fisher inequality holds and that this is dependent on certain combinatorial properties of the points of minimal weight. We define these points, and the designs containing them, to be either ‘awkward’ or ‘difficult’ depending on these properties. Chapter three extends the combinatorial analysis of row-sum point-weight designs. We examine structures that are simultaneously row-sum and point-sum point-weight designs, paying particular attention to the question of regularity. We also present several general construction techniques and specific examples of row-sum point-weight designs that are generated using these techniques. -
Linear Algebra Handout
Artificial Intelligence: 6.034 Massachusetts Institute of Technology April 20, 2012 Spring 2012 Recitation 10 Linear Algebra Review • A vector is an ordered list of values. It is often denoted using angle brackets: ha; bi, and its variable name is often written in bold (z) or with an arrow (~z). We can refer to an individual element of a vector using its index: for example, the first element of z would be z1 (or z0, depending on how we're indexing). Each element of a vector generally corresponds to a particular dimension or feature, which could be discrete or continuous; often you can think of a vector as a point in Euclidean space. p 2 2 2 • The magnitude (also called norm) of a vector x = hx1; x2; :::; xni is x1 + x2 + ::: + xn, and is denoted jxj or kxk. • The sum of a set of vectors is their elementwise sum: for example, ha; bi + hc; di = ha + c; b + di (so vectors can only be added if they are the same length). The dot product (also called scalar product) of two vectors is the sum of their elementwise products: for example, ha; bi · hc; di = ac + bd. The dot product x · y is also equal to kxkkyk cos θ, where θ is the angle between x and y. • A matrix is a generalization of a vector: instead of having just one row or one column, it can have m rows and n columns. A square matrix is one that has the same number of rows as columns. A matrix's variable name is generally a capital letter, often written in bold. -
Second Edition Volume I
Second Edition Thomas Beth Universitat¨ Karlsruhe Dieter Jungnickel Universitat¨ Augsburg Hanfried Lenz Freie Universitat¨ Berlin Volume I PUBLISHED BY THE PRESS SYNDICATE OF THE UNIVERSITY OF CAMBRIDGE The Pitt Building, Trumpington Street, Cambridge, United Kingdom CAMBRIDGE UNIVERSITY PRESS The Edinburgh Building, Cambridge CB2 2RU, UK www.cup.cam.ac.uk 40 West 20th Street, New York, NY 10011-4211, USA www.cup.org 10 Stamford Road, Oakleigh, Melbourne 3166, Australia Ruiz de Alarc´on 13, 28014 Madrid, Spain First edition c Bibliographisches Institut, Zurich, 1985 c Cambridge University Press, 1993 Second edition c Cambridge University Press, 1999 This book 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 1999 Printed in the United Kingdom at the University Press, Cambridge Typeset in Times Roman 10/13pt. in LATEX2ε[TB] A catalogue record for this book is available from the British Library Library of Congress Cataloguing in Publication data Beth, Thomas, 1949– Design theory / Thomas Beth, Dieter Jungnickel, Hanfried Lenz. – 2nd ed. p. cm. Includes bibliographical references and index. ISBN 0 521 44432 2 (hardbound) 1. Combinatorial designs and configurations. I. Jungnickel, D. (Dieter), 1952– . II. Lenz, Hanfried. III. Title. QA166.25.B47 1999 5110.6 – dc21 98-29508 CIP ISBN 0 521 44432 2 hardback Contents I. Examples and basic definitions .................... 1 §1. Incidence structures and incidence matrices ............ 1 §2. Block designs and examples from affine and projective geometry ........................6 §3. t-designs, Steiner systems and configurations ......... -
Invariants of Incidence Matrices
Invariants of Incidence Matrices Chris Godsil (University of Waterloo), Peter Sin (University of Florida), Qing Xiang (University of Delaware). March 29 – April 3, 2009 1 Overview of the Field Incidence matrices arise whenever one attempts to find invariants of a relation between two (usually finite) sets. Researchers in design theory, coding theory, algebraic graph theory, representation theory, and finite geometry all encounter problems about modular ranks and Smith normal forms (SNF) of incidence matrices. For example, the work by Hamada [9] on the dimension of the code generated by r-flats in a projective geometry was motivated by problems in coding theory (Reed-Muller codes) and finite geometry; the work of Wilson [18] on the diagonal forms of subset-inclusion matrices was motivated by questions on existence of designs; and the papers [2] and [5] on p-ranks and Smith normal forms of subspace-inclusion matrices have their roots in representation theory and finite geometry. An impression of the current directions in research can be gained by considering our level of understanding of some fundamental examples. Incidence of subsets of a finite set Let Xr denote the set of subsets of size r in a finite set X. We can consider various incidence relations between Xr and Xs, such as inclusion, empty intersection or, more generally, intersection of fixed size t. These incidence systems are of central importance in the theory of designs, where they play a key role in Wilson’s fundamental work on existence theorems. They also appear in the theory of association schemes. 1 2 Incidence of subspaces of a finite vector space This class of incidence systems is the exact q-analogue of the class of subset incidences. -
7 Combinatorial Geometry
7 Combinatorial Geometry Planes Incidence Structure: An incidence structure is a triple (V; B; ∼) so that V; B are disjoint sets and ∼ is a relation on V × B. We call elements of V points, elements of B blocks or lines and we associate each line with the set of points incident with it. So, if p 2 V and b 2 B satisfy p ∼ b we say that p is contained in b and write p 2 b and if b; b0 2 B we let b \ b0 = fp 2 P : p ∼ b; p ∼ b0g. Levi Graph: If (V; B; ∼) is an incidence structure, the associated Levi Graph is the bipartite graph with bipartition (V; B) and incidence given by ∼. Parallel: We say that the lines b; b0 are parallel, and write bjjb0 if b \ b0 = ;. Affine Plane: An affine plane is an incidence structure P = (V; B; ∼) which satisfies the following properties: (i) Any two distinct points are contained in exactly one line. (ii) If p 2 V and ` 2 B satisfy p 62 `, there is a unique line containing p and parallel to `. (iii) There exist three points not all contained in a common line. n Line: Let ~u;~v 2 F with ~v 6= 0 and let L~u;~v = f~u + t~v : t 2 Fg. Any set of the form L~u;~v is called a line in Fn. AG(2; F): We define AG(2; F) to be the incidence structure (V; B; ∼) where V = F2, B is the set of lines in F2 and if v 2 V and ` 2 B we define v ∼ ` if v 2 `. -
Cutting Hyperplane Arrangements*
Discrete Comput Geom 6:385 406 (1991) GDieometryscrete & Computational 1991 Springer-VerlagNew York Inc Cutting Hyperplane Arrangements* Jifi Matou~ek Department of Applied Mathematics, Charles University, Malostransk6 nfim. 25, 118 00 Praha 1. Czechoslovakia Abstract. We consider a collection H of n hyperplanes in E a (where the dimension d is fixed). An e.-cuttin9 for H is a collection of (possibly unbounded) d-dimensional simplices with disjoint interiors, which cover all E a and such that the interior of any simplex is intersected by at most en hyperplanes of H. We give a deterministic algorithm for finding a (1/r)-cutting with O(r d) simplices (which is asymptotically optimal). For r < n I 6, where 6 > 0 is arbitrary but fixed, the running time of this algorithm is O(n(log n)~ In the plane we achieve a time bound O(nr) for r _< n I-6, which is optimal if we also want to compute the collection of lines intersecting each simplex of the cutting. This improves a result of Agarwal, and gives a conceptually simpler algorithm. For an n point set X ~_ E d and a parameter r, we can deterministically compute a (1/r)-net of size O(r log r) for the range space (X, {X c~ R; R is a simplex}), in time O(n(log n)~ d- 1 + roe11). The size of the (1/r)-net matches the best known existence result. By a simple transformation, this allows us to find e-nets for other range spaces usually encountered in computational geometry. -
15 BASIC PROPERTIES of CONVEX POLYTOPES Martin Henk, J¨Urgenrichter-Gebert, and G¨Unterm
15 BASIC PROPERTIES OF CONVEX POLYTOPES Martin Henk, J¨urgenRichter-Gebert, and G¨unterM. Ziegler INTRODUCTION Convex polytopes are fundamental geometric objects that have been investigated since antiquity. The beauty of their theory is nowadays complemented by their im- portance for many other mathematical subjects, ranging from integration theory, algebraic topology, and algebraic geometry to linear and combinatorial optimiza- tion. In this chapter we try to give a short introduction, provide a sketch of \what polytopes look like" and \how they behave," with many explicit examples, and briefly state some main results (where further details are given in subsequent chap- ters of this Handbook). We concentrate on two main topics: • Combinatorial properties: faces (vertices, edges, . , facets) of polytopes and their relations, with special treatments of the classes of low-dimensional poly- topes and of polytopes \with few vertices;" • Geometric properties: volume and surface area, mixed volumes, and quer- massintegrals, including explicit formulas for the cases of the regular simplices, cubes, and cross-polytopes. We refer to Gr¨unbaum [Gr¨u67]for a comprehensive view of polytope theory, and to Ziegler [Zie95] respectively to Gruber [Gru07] and Schneider [Sch14] for detailed treatments of the combinatorial and of the convex geometric aspects of polytope theory. 15.1 COMBINATORIAL STRUCTURE GLOSSARY d V-polytope: The convex hull of a finite set X = fx1; : : : ; xng of points in R , n n X i X P = conv(X) := λix λ1; : : : ; λn ≥ 0; λi = 1 : i=1 i=1 H-polytope: The solution set of a finite system of linear inequalities, d T P = P (A; b) := x 2 R j ai x ≤ bi for 1 ≤ i ≤ m ; with the extra condition that the set of solutions is bounded, that is, such that m×d there is a constant N such that jjxjj ≤ N holds for all x 2 P . -
Desargues' Theorem and Perspectivities
Desargues' theorem and perspectivities A file of the Geometrikon gallery by Paris Pamfilos The joy of suddenly learning a former secret and the joy of suddenly discovering a hitherto unknown truth are the same to me - both have the flash of enlightenment, the almost incredibly enhanced vision, and the ecstasy and euphoria of released tension. Paul Halmos, I Want to be a Mathematician, p.3 Contents 1 Desargues’ theorem1 2 Perspective triangles3 3 Desargues’ theorem, special cases4 4 Sides passing through collinear points5 5 A case handled with projective coordinates6 6 Space perspectivity7 7 Perspectivity as a projective transformation9 8 The case of the trilinear polar 11 9 The case of conjugate triangles 13 1 Desargues’ theorem The theorem of Desargues represents the geometric foundation of “photography” and “per- spectivity”, used by painters and designers in order to represent in paper objects of the space. Two triangles are called “perspective relative to a point” or “point perspective”, when we can label them ABC and A0B0C0 in such a way, that lines fAA0; BB0;CC0g pass through a 1 Desargues’ theorem 2 common point P: Points fA; A0g are then called “homologous” and similarly points fB; B0g and fC;C0g. The point P is then called “perspectivity center” of the two triangles. The A'' B'' C' C A' P A B B' C'' Figure 1: “Desargues’ configuration”: Theorem of Desargues two triangles are called “perspective relative to a line” or “line perspective” when we can label them ABC and A0B0C0 , in such a way (See Figure 1), that the points of intersection of their sides C00 = ¹AB; A0B0º; A00 = ¹BC; B0C0º and B00 = ¹CA;C0 A0º are contained in the same line ": Sides AB and A0B0 are then called “homologous”, and similarly the side pairs ¹BC; B0C0º and ¹CA;C0 A0º: Line " is called “perspectivity axis” of the two triangles. -
Convex Sets in Projective Space Compositio Mathematica, Tome 13 (1956-1958), P
COMPOSITIO MATHEMATICA J. DE GROOT H. DE VRIES Convex sets in projective space Compositio Mathematica, tome 13 (1956-1958), p. 113-118 <http://www.numdam.org/item?id=CM_1956-1958__13__113_0> © Foundation Compositio Mathematica, 1956-1958, tous droits réser- vés. L’accès aux archives de la revue « Compositio Mathematica » (http: //http://www.compositio.nl/) implique l’accord avec les conditions gé- nérales d’utilisation (http://www.numdam.org/conditions). Toute utili- sation commerciale ou impression systématique est constitutive d’une infraction pénale. Toute copie ou impression de ce fichier doit conte- nir la présente mention de copyright. Article numérisé dans le cadre du programme Numérisation de documents anciens mathématiques http://www.numdam.org/ Convex sets in projective space by J. de Groot and H. de Vries INTRODUCTION. We consider the following properties of sets in n-dimensional real projective space Pn(n > 1 ): a set is semiconvex, if any two points of the set can be joined by a (line)segment which is contained in the set; a set is convex (STEINITZ [1]), if it is semiconvex and does not meet a certain P n-l. The main object of this note is to characterize the convexity of a set by the following interior and simple property: a set is convex if and only if it is semiconvex and does not contain a whole (projective) line; in other words: a subset of Pn is convex if and only if any two points of the set can be joined uniquely by a segment contained in the set. In many cases we can prove more; see e.g.