Basic Notions of Geometry and Euclidean Geometry - Tetsuya Ozawa
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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. -
1 Euclidean Vector Space and Euclidean Affi Ne Space
Profesora: Eugenia Rosado. E.T.S. Arquitectura. Euclidean Geometry1 1 Euclidean vector space and euclidean a¢ ne space 1.1 Scalar product. Euclidean vector space. Let V be a real vector space. De…nition. A scalar product is a map (denoted by a dot ) V V R ! (~u;~v) ~u ~v 7! satisfying the following axioms: 1. commutativity ~u ~v = ~v ~u 2. distributive ~u (~v + ~w) = ~u ~v + ~u ~w 3. ( ~u) ~v = (~u ~v) 4. ~u ~u 0, for every ~u V 2 5. ~u ~u = 0 if and only if ~u = 0 De…nition. Let V be a real vector space and let be a scalar product. The pair (V; ) is said to be an euclidean vector space. Example. The map de…ned as follows V V R ! (~u;~v) ~u ~v = x1x2 + y1y2 + z1z2 7! where ~u = (x1; y1; z1), ~v = (x2; y2; z2) is a scalar product as it satis…es the …ve properties of a scalar product. This scalar product is called standard (or canonical) scalar product. The pair (V; ) where is the standard scalar product is called the standard euclidean space. 1.1.1 Norm associated to a scalar product. Let (V; ) be a real euclidean vector space. De…nition. A norm associated to the scalar product is a map de…ned as follows V kk R ! ~u ~u = p~u ~u: 7! k k Profesora: Eugenia Rosado, E.T.S. Arquitectura. Euclidean Geometry.2 1.1.2 Unitary and orthogonal vectors. Orthonormal basis. Let (V; ) be a real euclidean vector space. De…nition. -
Higher Dimensional Conundra
Higher Dimensional Conundra Steven G. Krantz1 Abstract: In recent years, especially in the subject of harmonic analysis, there has been interest in geometric phenomena of RN as N → +∞. In the present paper we examine several spe- cific geometric phenomena in Euclidean space and calculate the asymptotics as the dimension gets large. 0 Introduction Typically when we do geometry we concentrate on a specific venue in a particular space. Often the context is Euclidean space, and often the work is done in R2 or R3. But in modern work there are many aspects of analysis that are linked to concrete aspects of geometry. And there is often interest in rendering the ideas in Hilbert space or some other infinite dimensional setting. Thus we want to see how the finite-dimensional result in RN changes as N → +∞. In the present paper we study some particular aspects of the geometry of RN and their asymptotic behavior as N →∞. We choose these particular examples because the results are surprising or especially interesting. We may hope that they will lead to further studies. It is a pleasure to thank Richard W. Cottle for a careful reading of an early draft of this paper and for useful comments. 1 Volume in RN Let us begin by calculating the volume of the unit ball in RN and the surface area of its bounding unit sphere. We let ΩN denote the former and ωN−1 denote the latter. In addition, we let Γ(x) be the celebrated Gamma function of L. Euler. It is a helpful intuition (which is literally true when x is an 1We are happy to thank the American Institute of Mathematics for its hospitality and support during this work. -
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 . -
Glossary of Linear Algebra Terms
INNER PRODUCT SPACES AND THE GRAM-SCHMIDT PROCESS A. HAVENS 1. The Dot Product and Orthogonality 1.1. Review of the Dot Product. We first recall the notion of the dot product, which gives us a familiar example of an inner product structure on the real vector spaces Rn. This product is connected to the Euclidean geometry of Rn, via lengths and angles measured in Rn. Later, we will introduce inner product spaces in general, and use their structure to define general notions of length and angle on other vector spaces. Definition 1.1. The dot product of real n-vectors in the Euclidean vector space Rn is the scalar product · : Rn × Rn ! R given by the rule n n ! n X X X (u; v) = uiei; viei 7! uivi : i=1 i=1 i n Here BS := (e1;:::; en) is the standard basis of R . With respect to our conventions on basis and matrix multiplication, we may also express the dot product as the matrix-vector product 2 3 v1 6 7 t î ó 6 . 7 u v = u1 : : : un 6 . 7 : 4 5 vn It is a good exercise to verify the following proposition. Proposition 1.1. Let u; v; w 2 Rn be any real n-vectors, and s; t 2 R be any scalars. The Euclidean dot product (u; v) 7! u · v satisfies the following properties. (i:) The dot product is symmetric: u · v = v · u. (ii:) The dot product is bilinear: • (su) · v = s(u · v) = u · (sv), • (u + v) · w = u · w + v · w. -
Vector Differential Calculus
KAMIWAAI – INTERACTIVE 3D SKETCHING WITH JAVA BASED ON Cl(4,1) CONFORMAL MODEL OF EUCLIDEAN SPACE Submitted to (Feb. 28,2003): Advances in Applied Clifford Algebras, http://redquimica.pquim.unam.mx/clifford_algebras/ Eckhard M. S. Hitzer Dept. of Mech. Engineering, Fukui Univ. Bunkyo 3-9-, 910-8507 Fukui, Japan. Email: [email protected], homepage: http://sinai.mech.fukui-u.ac.jp/ Abstract. This paper introduces the new interactive Java sketching software KamiWaAi, recently developed at the University of Fukui. Its graphical user interface enables the user without any knowledge of both mathematics or computer science, to do full three dimensional “drawings” on the screen. The resulting constructions can be reshaped interactively by dragging its points over the screen. The programming approach is new. KamiWaAi implements geometric objects like points, lines, circles, spheres, etc. directly as software objects (Java classes) of the same name. These software objects are geometric entities mathematically defined and manipulated in a conformal geometric algebra, combining the five dimensions of origin, three space and infinity. Simple geometric products in this algebra represent geometric unions, intersections, arbitrary rotations and translations, projections, distance, etc. To ease the coordinate free and matrix free implementation of this fundamental geometric product, a new algebraic three level approach is presented. Finally details about the Java classes of the new GeometricAlgebra software package and their associated methods are given. KamiWaAi is available for free internet download. Key Words: Geometric Algebra, Conformal Geometric Algebra, Geometric Calculus Software, GeometricAlgebra Java Package, Interactive 3D Software, Geometric Objects 1. Introduction The name “KamiWaAi” of this new software is the Romanized form of the expression in verse sixteen of chapter four, as found in the Japanese translation of the first Letter of the Apostle John, which is part of the New Testament, i.e. -
Properties of Euclidean Space
Section 3.1 Properties of Euclidean Space As has been our convention throughout this course, we use the notation R2 to refer to the plane (two dimensional space); R3 for three dimensional space; and Rn to indicate n dimensional space. Alternatively, these spaces are often referred to as Euclidean spaces; for example, \three dimensional Euclidean space" refers to R3. In this section, we will point out many of the special features common to the Euclidean spaces; in Chapter 4, we will see that many of these features are shared by other \spaces" as well. Many of the graphics in this section are drawn in two dimensions (largely because this is the easiest space to visualize on paper), but you should be aware that the ideas presented apply to n dimensional Euclidean space, not just to two dimensional space. Vectors in Euclidean Space When we refer to a vector in Euclidean space, we mean directed line segments that are embedded in the space, such as the vector pictured below in R2: We often refer to a vector, such as the vector AB shown below, by its initial and terminal points; in this example, A is the initial point of AB, and B is the terminal point of the vector. While we often think of n dimensional space as being made up of points, we may equivalently consider it to be made up of vectors by identifying points and vectors. For instance, we identify the point (2; 1) in two dimensional Euclidean space with the vector with initial point (0; 0) and terminal point (2; 1): 1 Section 3.1 In this way, we think of n dimensional Euclidean space as being made up of n dimensional vectors. -
About Symmetries in Physics
LYCEN 9754 December 1997 ABOUT SYMMETRIES IN PHYSICS Dedicated to H. Reeh and R. Stora1 Fran¸cois Gieres Institut de Physique Nucl´eaire de Lyon, IN2P3/CNRS, Universit´eClaude Bernard 43, boulevard du 11 novembre 1918, F - 69622 - Villeurbanne CEDEX Abstract. The goal of this introduction to symmetries is to present some general ideas, to outline the fundamental concepts and results of the subject and to situate a bit the following arXiv:hep-th/9712154v1 16 Dec 1997 lectures of this school. [These notes represent the write-up of a lecture presented at the fifth S´eminaire Rhodanien de Physique “Sur les Sym´etries en Physique” held at Dolomieu (France), 17-21 March 1997. Up to the appendix and the graphics, it is to be published in Symmetries in Physics, F. Gieres, M. Kibler, C. Lucchesi and O. Piguet, eds. (Editions Fronti`eres, 1998).] 1I wish to dedicate these notes to my diploma and Ph.D. supervisors H. Reeh and R. Stora who devoted a major part of their scientific work to the understanding, description and explo- ration of symmetries in physics. Contents 1 Introduction ................................................... .......1 2 Symmetries of geometric objects ...................................2 3 Symmetries of the laws of nature ..................................5 1 Geometric (space-time) symmetries .............................6 2 Internal symmetries .............................................10 3 From global to local symmetries ...............................11 4 Combining geometric and internal symmetries ...............14 -
A Partition Property of Simplices in Euclidean Space
JOURNAL OF THE AMERICAN MATHEMATICAL SOCIETY Volume 3, Number I, January 1990 A PARTITION PROPERTY OF SIMPLICES IN EUCLIDEAN SPACE P. FRANKL AND V. RODL 1. INTRODUCTION AND STATEMENT OF THE RESULTS Let ]Rn denote n-dimensional Euclidean space endowed with the standard metric. For x, y E ]Rn , their distance is denoted by Ix - yl. Definition 1.1 [E]. A subset B of ]Rd is called Ramsey if for every r ~ 2 there exists some n = n(r, B) with the following partition property. For every partition ]Rn = V; u ... U ~ , there exists some j, 1::::; j ::::; r, and Bj C fj such that B is congruent to Bj • In a series of papers, Erdos et al. [E] have investigated this property. They have shown that all Ramsey sets are spherical, that is, every Ramsey set is con- tained in an appropriate sphere. On the other hand, they have shown that the vertex set (and, therefore, all its subsets) of bricks (d-dimensional paral- lelepipeds) is Ramsey. The simplest sets that are spherical but cannot be embedded into the vertex set of a brick are the sets of obtuse triangles. In [FR 1], it is shown that they are indeed Ramsey, using Ramsey's Theorem (cf. [G2]) and the Product Theorem of [E]. The aim of the present paper is twofold. First, we want to show that the vertex set of every nondegenerate simplex in any dimension is Ramsey. Second, we want to show that for both simplices and bricks, and even for their products, one can in fact choose n(r, B) = c(B)logr, where c(B) is an appropriate positive constant. -
Basics of Linear Algebra
Basics of Linear Algebra Jos and Sophia Vectors ● Linear Algebra Definition: A list of numbers with a magnitude and a direction. ○ Magnitude: a = [4,3] |a| =sqrt(4^2+3^2)= 5 ○ Direction: angle vector points ● Computer Science Definition: A list of numbers. ○ Example: Heights = [60, 68, 72, 67] Dot Product of Vectors Formula: a · b = |a| × |b| × cos(θ) ● Definition: Multiplication of two vectors which results in a scalar value ● In the diagram: ○ |a| is the magnitude (length) of vector a ○ |b| is the magnitude of vector b ○ Θ is the angle between a and b Matrix ● Definition: ● Matrix elements: ● a)Matrix is an arrangement of numbers into rows and columns. ● b) A matrix is an m × n array of scalars from a given field F. The individual values in the matrix are called entries. ● Matrix dimensions: the number of rows and columns of the matrix, in that order. Multiplication of Matrices ● The multiplication of two matrices ● Result matrix dimensions ○ Notation: (Row, Column) ○ Columns of the 1st matrix must equal the rows of the 2nd matrix ○ Result matrix is equal to the number of (1, 2, 3) • (7, 9, 11) = 1×7 +2×9 + 3×11 rows in 1st matrix and the number of = 58 columns in the 2nd matrix ○ Ex. 3 x 4 ॱ 5 x 3 ■ Dot product does not work ○ Ex. 5 x 3 ॱ 3 x 4 ■ Dot product does work ■ Result: 5 x 4 Dot Product Application ● Application: Ray tracing program ○ Quickly create an image with lower quality ○ “Refinement rendering pass” occurs ■ Removes the jagged edges ○ Dot product used to calculate ■ Intersection between a ray and a sphere ■ Measure the length to the intersection points ● Application: Forward Propagation ○ Input matrix * weighted matrix = prediction matrix http://immersivemath.com/ila/ch03_dotprodu ct/ch03.html#fig_dp_ray_tracer Projections One important use of dot products is in projections. -
A Guided Tour to the Plane-Based Geometric Algebra PGA
A Guided Tour to the Plane-Based Geometric Algebra PGA Leo Dorst University of Amsterdam Version 1.15{ July 6, 2020 Planes are the primitive elements for the constructions of objects and oper- ators in Euclidean geometry. Triangulated meshes are built from them, and reflections in multiple planes are a mathematically pure way to construct Euclidean motions. A geometric algebra based on planes is therefore a natural choice to unify objects and operators for Euclidean geometry. The usual claims of `com- pleteness' of the GA approach leads us to hope that it might contain, in a single framework, all representations ever designed for Euclidean geometry - including normal vectors, directions as points at infinity, Pl¨ucker coordinates for lines, quaternions as 3D rotations around the origin, and dual quaternions for rigid body motions; and even spinors. This text provides a guided tour to this algebra of planes PGA. It indeed shows how all such computationally efficient methods are incorporated and related. We will see how the PGA elements naturally group into blocks of four coordinates in an implementation, and how this more complete under- standing of the embedding suggests some handy choices to avoid extraneous computations. In the unified PGA framework, one never switches between efficient representations for subtasks, and this obviously saves any time spent on data conversions. Relative to other treatments of PGA, this text is rather light on the mathematics. Where you see careful derivations, they involve the aspects of orientation and magnitude. These features have been neglected by authors focussing on the mathematical beauty of the projective nature of the algebra. -
Euclidean Space - Wikipedia, the Free Encyclopedia Page 1 of 5
Euclidean space - Wikipedia, the free encyclopedia Page 1 of 5 Euclidean space From Wikipedia, the free encyclopedia In mathematics, Euclidean space is the Euclidean plane and three-dimensional space of Euclidean geometry, as well as the generalizations of these notions to higher dimensions. The term “Euclidean” distinguishes these spaces from the curved spaces of non-Euclidean geometry and Einstein's general theory of relativity, and is named for the Greek mathematician Euclid of Alexandria. Classical Greek geometry defined the Euclidean plane and Euclidean three-dimensional space using certain postulates, while the other properties of these spaces were deduced as theorems. In modern mathematics, it is more common to define Euclidean space using Cartesian coordinates and the ideas of analytic geometry. This approach brings the tools of algebra and calculus to bear on questions of geometry, and Every point in three-dimensional has the advantage that it generalizes easily to Euclidean Euclidean space is determined by three spaces of more than three dimensions. coordinates. From the modern viewpoint, there is essentially only one Euclidean space of each dimension. In dimension one this is the real line; in dimension two it is the Cartesian plane; and in higher dimensions it is the real coordinate space with three or more real number coordinates. Thus a point in Euclidean space is a tuple of real numbers, and distances are defined using the Euclidean distance formula. Mathematicians often denote the n-dimensional Euclidean space by , or sometimes if they wish to emphasize its Euclidean nature. Euclidean spaces have finite dimension. Contents 1 Intuitive overview 2 Real coordinate space 3 Euclidean structure 4 Topology of Euclidean space 5 Generalizations 6 See also 7 References Intuitive overview One way to think of the Euclidean plane is as a set of points satisfying certain relationships, expressible in terms of distance and angle.