Spherical, Hyperbolic and Other Projective Geometries: Convexity, Duality, Transitions François Fillastre, Andrea Seppi
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Real Composition Algebras by Steven Clanton
Real Composition Algebras by Steven Clanton A Thesis Submitted to the Faculty of The Wilkes Honors College in Partial Fulfillment of the Requirements for the Degree of Bachelor of Arts in Liberal Arts and Sciences with a Concentration in Mathematics Wilkes Honors College of Florida Atlantic University Jupiter, FL May 2009 Real Composition Algebras by Steven Clanton This thesis was prepared under the direction of the candidates thesis advisor, Dr. Ryan Karr, and has been approved by the members of his supervisory committee. It was sub- mitted to the faculty of The Honors College and was accepted in partial fulfillment of the requirements for the degree of Bachelor of Arts in Liberal Arts and Sciences. SUPERVISORY COMMITTEE: Dr. Ryan Karr Dr. Eugene Belogay Dean, Wilkes Honors College Date ii Abstract Author: Steve Clanton Title: Real Composition Algebras Institution: Wilkes Honors College of Florida Atlantic University Thesis Advisor: Dr. Ryan Karr Degree: Bachelor of Arts in Liberal Arts and Sciences Concentration: Mathematics Year: 2009 According to Koecher and Remmert [Ebb91, p. 267], Gauss introduced the \com- position of quadratic forms" to study the representability of natural numbers in binary (rank 2) quadratic forms. In particular, Gauss proved that any positive defi- nite binary quadratic form can be linearly transformed into the two-squares formula 2 2 2 2 2 2 w1 + w2 = (u1 + u2)(v1 + v2). This shows not only the existence of an algebra for every form but also an isomorphism to the complex numbers for each. Hurwitz gen- eralized the \theory of composition" to arbitrary dimensions and showed that exactly four such systems exist: the real numbers, the complex numbers, the quaternions, and octonions [CS03, p. -
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. -
Squaring the Circle in Elliptic Geometry
Rose-Hulman Undergraduate Mathematics Journal Volume 18 Issue 2 Article 1 Squaring the Circle in Elliptic Geometry Noah Davis Aquinas College Kyle Jansens Aquinas College, [email protected] Follow this and additional works at: https://scholar.rose-hulman.edu/rhumj Recommended Citation Davis, Noah and Jansens, Kyle (2017) "Squaring the Circle in Elliptic Geometry," Rose-Hulman Undergraduate Mathematics Journal: Vol. 18 : Iss. 2 , Article 1. Available at: https://scholar.rose-hulman.edu/rhumj/vol18/iss2/1 Rose- Hulman Undergraduate Mathematics Journal squaring the circle in elliptic geometry Noah Davis a Kyle Jansensb Volume 18, No. 2, Fall 2017 Sponsored by Rose-Hulman Institute of Technology Department of Mathematics Terre Haute, IN 47803 a [email protected] Aquinas College b scholar.rose-hulman.edu/rhumj Aquinas College Rose-Hulman Undergraduate Mathematics Journal Volume 18, No. 2, Fall 2017 squaring the circle in elliptic geometry Noah Davis Kyle Jansens Abstract. Constructing a regular quadrilateral (square) and circle of equal area was proved impossible in Euclidean geometry in 1882. Hyperbolic geometry, however, allows this construction. In this article, we complete the story, providing and proving a construction for squaring the circle in elliptic geometry. We also find the same additional requirements as the hyperbolic case: only certain angle sizes work for the squares and only certain radius sizes work for the circles; and the square and circle constructions do not rely on each other. Acknowledgements: We thank the Mohler-Thompson Program for supporting our work in summer 2014. Page 2 RHIT Undergrad. Math. J., Vol. 18, No. 2 1 Introduction In the Rose-Hulman Undergraduate Math Journal, 15 1 2014, Noah Davis demonstrated the construction of a hyperbolic circle and hyperbolic square in the Poincar´edisk [1]. -
Elliptic Geometry Hawraa Abbas Almurieb Spherical Geometry Axioms of Incidence
Elliptic Geometry Hawraa Abbas Almurieb Spherical Geometry Axioms of Incidence • Ax1. For every pair of antipodal point P and P’ and for every pair of antipodal point Q and Q’ such that P≠Q and P’≠Q’, there exists a unique circle incident with both pairs of points. • Ax2. For every great circle c, there exist at least two distinct pairs of antipodal points incident with c. • Ax3. There exist three distinct pairs of antipodal points with the property that no great circle is incident with all three of them. Betweenness Axioms Betweenness fails on circles What is the relation among points? • (A,B/C,D)= points A and B separates C and D 2. Axioms of Separation • Ax4. If (A,B/C,D), then points A, B, C, and D are collinear and distinct. In other words, non-collinear points cannot separate one another. • Ax5. If (A,B/C,D), then (B,A/C,D) and (C,D/A,B) • Ax6. If (A,B/C,D), then not (A,C/B,D) • Ax7. If points A, B, C, and D are collinear and distinct then (A,B/C,D) , (A,C/B,D) or (A,D/B,C). • Ax8. If points A, B, and C are collinear and distinct then there exists a point D such that(A,B/C,D) • Ax9. For any five distinct collinear points, A, B, C, D, and E, if (A,B/E,D), then either (A,B/C,D) or (A,B/C,E) Definition • Let l and m be any two lines and let O be a point not on either of them. -
Module 2 : Fundamentals of Vector Spaces Section 2 : Vector Spaces
Module 2 : Fundamentals of Vector Spaces Section 2 : Vector Spaces 2. Vector Spaces The concept of a vector space will now be formally introduced. This requires the concept of closure and field. Definition 1 (Closure) A set is said to be closed under an operation when any two elements of the set subject to the operation yields a third element belonging to the same set. Example 2 The set of integers is closed under addition, multiplication and subtraction. However, this set is not closed under division. Example 3 The set of real numbers ( ) and the set of complex numbers (C) are closed under addition, subtraction, multiplication and division. Definition 4 (Field) A field is a set of elements closed under addition, subtraction, multiplication and division. Example 5 The set of real numbers ( ) and the set of complex numbers ( ) are scalar fields. However, the set of integers is not a field. A vector space is a set of elements, which is closed under addition and scalar multiplication. Thus, associated with every vector space is a set of scalars (also called as scalar field or coefficient field) used to define scalar multiplication on the space. In functional analysis, the scalars will be always taken to be the set of real numbers ( ) or complex numbers ( ). Definition 6 (Vector Space): A vector space is a set of elements called vectors and scalar field together with two operations. The first operation is called addition which associates with any two vectors a vector , the sum of and . The second operation is called scalar multiplication, which associates with any vector and any scalar a vector (a scalar multiple of by Thus, when is a linear vector space, given any vectors and any scalars the element . -
+ 2R - P = I + 2R - P + 2, Where P, I, P Are the Invariants P and Those of Zeuthen-Segre for F
232 THE FOURTH DIMENSION. [Feb., N - 4p - (m - 2) + 2r - p = I + 2r - p + 2, where p, I, p are the invariants p and those of Zeuthen-Segre for F. If I is the same invariant for F, since I — p = I — p, we have I = 2V — 4p — m = 2s — 4p — m. Hence 2s is equal to the "equivalence" in nodes of the point (a, b, c) in the evaluation of the invariant I for F by means of the pencil Cy. This property can be shown directly for the following cases: 1°. F has only ordinary nodes. 2°. In the vicinity of any of these nodes there lie other nodes, or ordinary infinitesimal multiple curves. In these cases it is easy to show that in the vicinity of the nodes all the numbers such as h are equal to 2. It would be interesting to know if such is always the case, but the preceding investigation shows that for the applications this does not matter. UNIVERSITY OF KANSAS, October 17, 1914. THE FOURTH DIMENSION. Geometry of Four Dimensions. By HENRY PARKER MANNING, Ph.D. New York, The Macmillan Company, 1914. 8vo. 348 pp. EVERY professional mathematician must hold himself at all times in readiness to answer certain standard questions of perennial interest to the layman. "What is, or what are quaternions ?" "What are least squares?" but especially, "Well, have you discovered the fourth dimension yet?" This last query is the most difficult of the three, suggesting forcibly the sophists' riddle "Have you ceased beating your mother?" The fact is that there is no common locus standi for questioner and questioned. -
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. -
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. -
Understand the Principles and Properties of Axiomatic (Synthetic
Michael Bonomi Understand the principles and properties of axiomatic (synthetic) geometries (0016) Euclidean Geometry: To understand this part of the CST I decided to start off with the geometry we know the most and that is Euclidean: − Euclidean geometry is a geometry that is based on axioms and postulates − Axioms are accepted assumptions without proofs − In Euclidean geometry there are 5 axioms which the rest of geometry is based on − Everybody had no problems with them except for the 5 axiom the parallel postulate − This axiom was that there is only one unique line through a point that is parallel to another line − Most of the geometry can be proven without the parallel postulate − If you do not assume this postulate, then you can only prove that the angle measurements of right triangle are ≤ 180° Hyperbolic Geometry: − We will look at the Poincare model − This model consists of points on the interior of a circle with a radius of one − The lines consist of arcs and intersect our circle at 90° − Angles are defined by angles between the tangent lines drawn between the curves at the point of intersection − If two lines do not intersect within the circle, then they are parallel − Two points on a line in hyperbolic geometry is a line segment − The angle measure of a triangle in hyperbolic geometry < 180° Projective Geometry: − This is the geometry that deals with projecting images from one plane to another this can be like projecting a shadow − This picture shows the basics of Projective geometry − The geometry does not preserve length -
Hyperbolic Geometry
Flavors of Geometry MSRI Publications Volume 31,1997 Hyperbolic Geometry JAMES W. CANNON, WILLIAM J. FLOYD, RICHARD KENYON, AND WALTER R. PARRY Contents 1. Introduction 59 2. The Origins of Hyperbolic Geometry 60 3. Why Call it Hyperbolic Geometry? 63 4. Understanding the One-Dimensional Case 65 5. Generalizing to Higher Dimensions 67 6. Rudiments of Riemannian Geometry 68 7. Five Models of Hyperbolic Space 69 8. Stereographic Projection 72 9. Geodesics 77 10. Isometries and Distances in the Hyperboloid Model 80 11. The Space at Infinity 84 12. The Geometric Classification of Isometries 84 13. Curious Facts about Hyperbolic Space 86 14. The Sixth Model 95 15. Why Study Hyperbolic Geometry? 98 16. When Does a Manifold Have a Hyperbolic Structure? 103 17. How to Get Analytic Coordinates at Infinity? 106 References 108 Index 110 1. Introduction Hyperbolic geometry was created in the first half of the nineteenth century in the midst of attempts to understand Euclid’s axiomatic basis for geometry. It is one type of non-Euclidean geometry, that is, a geometry that discards one of Euclid’s axioms. Einstein and Minkowski found in non-Euclidean geometry a This work was supported in part by The Geometry Center, University of Minnesota, an STC funded by NSF, DOE, and Minnesota Technology, Inc., by the Mathematical Sciences Research Institute, and by NSF research grants. 59 60 J. W. CANNON, W. J. FLOYD, R. KENYON, AND W. R. PARRY geometric basis for the understanding of physical time and space. In the early part of the twentieth century every serious student of mathematics and physics studied non-Euclidean geometry. -
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.