Quaternions and Elliptical Space (Quaternions Et Espace Elliptique1)
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Quaternions and Cli Ord Geometric Algebras
Quaternions and Cliord Geometric Algebras Robert Benjamin Easter First Draft Edition (v1) (c) copyright 2015, Robert Benjamin Easter, all rights reserved. Preface As a rst rough draft that has been put together very quickly, this book is likely to contain errata and disorganization. The references list and inline citations are very incompete, so the reader should search around for more references. I do not claim to be the inventor of any of the mathematics found here. However, some parts of this book may be considered new in some sense and were in small parts my own original research. Much of the contents was originally written by me as contributions to a web encyclopedia project just for fun, but for various reasons was inappropriate in an encyclopedic volume. I did not originally intend to write this book. This is not a dissertation, nor did its development receive any funding or proper peer review. I oer this free book to the public, such as it is, in the hope it could be helpful to an interested reader. June 19, 2015 - Robert B. Easter. (v1) [email protected] 3 Table of contents Preface . 3 List of gures . 9 1 Quaternion Algebra . 11 1.1 The Quaternion Formula . 11 1.2 The Scalar and Vector Parts . 15 1.3 The Quaternion Product . 16 1.4 The Dot Product . 16 1.5 The Cross Product . 17 1.6 Conjugates . 18 1.7 Tensor or Magnitude . 20 1.8 Versors . 20 1.9 Biradials . 22 1.10 Quaternion Identities . 23 1.11 The Biradial b/a . -
Marc De Graef, CMU
Rotations, rotations, and more rotations … Marc De Graef, CMU AN OVERVIEW OF ROTATION REPRESENTATIONS AND THE RELATIONS BETWEEN THEM AFOSR MURI FA9550-12-1-0458 CMU, 7/8/15 1 Outline 2D rotations 3D rotations 7 rotation representations Conventions (places where you can go wrong…) Motivation 2 Outline 2D rotations 3D rotations 7 rotation representations Conventions (places where you can go wrong…) Motivation PREPRINT: “Tutorial: Consistent Representations of and Conversions between 3D Rotations,” D. Rowenhorst, A.D. Rollett, G.S. Rohrer, M. Groeber, M. Jackson, P.J. Konijnenberg, M. De Graef, MSMSE, under review (2015). 2 2D Rotations 3 2D Rotations y x 3 2D Rotations y0 y ✓ x0 x 3 2D Rotations y0 y ✓ x0 x ex0 cos ✓ sin ✓ ex p = ei0 = Rijej e0 sin ✓ cos ✓ ey ! ✓ y ◆ ✓ − ◆✓ ◆ 3 2D Rotations y0 y ✓ p x R = e0 e 0 ij i · j x ex0 cos ✓ sin ✓ ex p = ei0 = Rijej e0 sin ✓ cos ✓ ey ! ✓ y ◆ ✓ − ◆✓ ◆ 3 2D Rotations y0 y ✓ p x R = e0 e 0 ij i · j x ex0 cos ✓ sin ✓ ex p = ei0 = Rijej e0 sin ✓ cos ✓ ey ! ✓ y ◆ ✓ − ◆✓ ◆ Rotating the reference frame while keeping the object constant is known as a passive rotation. 3 2D Rotations y0 y Assumptions: Cartesian reference frame, right-handed; positive ✓ rotation is counter-clockwise p x R = e0 e 0 ij i · j x ex0 cos ✓ sin ✓ ex p = ei0 = Rijej e0 sin ✓ cos ✓ ey ! ✓ y ◆ ✓ − ◆✓ ◆ Rotating the reference frame while keeping the object constant is known as a passive rotation. 3 2D Rotations 4 2D Rotations y0 y ✓ x0 x 4 2D Rotations y0 y ✓ x0 x 4 2D Rotations y0 y ✓ r = ri0ei0 = rjej x0 x 4 2D Rotations y0 y ✓ r = ri0ei0 = rjej p = ri0Rijej x0 p T =(R )jiri0ej x 4 2D Rotations y0 y ✓ r = ri0ei0 = rjej p = ri0Rijej x0 p T =(R )jiri0ej x p T r =(R ) r0 ! j ji i 4 2D Rotations y0 y ✓ r = ri0ei0 = rjej p = ri0Rijej x0 p T =(R )jiri0ej x p T r =(R ) r0 ! j ji i p r0 = R r ! i ij j 4 2D Rotations y0 y ✓ r = ri0ei0 = rjej p = ri0Rijej x0 p T =(R )jiri0ej x p T r =(R ) r0 ! j ji i p r0 = R r ! i ij j The passive matrix converts the old coordinates into the new coordinates by left-multiplication. -
The Devil of Rotations Is Afoot! (James Watt in 1781)
The Devil of Rotations is Afoot! (James Watt in 1781) Leo Dorst Informatics Institute, University of Amsterdam XVII summer school, Santander, 2016 0 1 The ratio of vectors is an operator in 2D Given a and b, find a vector x that is to c what b is to a? So, solve x from: x : c = b : a: The answer is, by geometric product: x = (b=a) c kbk = cos(φ) − I sin(φ) c kak = ρ e−Iφ c; an operator on c! Here I is the unit-2-blade of the plane `from a to b' (so I2 = −1), ρ is the ratio of their norms, and φ is the angle between them. (Actually, it is better to think of Iφ as the angle.) Result not fully dependent on a and b, so better parametrize by ρ and Iφ. GAViewer: a = e1, label(a), b = e1+e2, label(b), c = -e1+2 e2, dynamicfx = (b/a) c,g 1 2 Another idea: rotation as multiple reflection Reflection in an origin plane with unit normal a x 7! x − 2(x · a) a=kak2 (classic LA): Now consider the dot product as the symmetric part of a more fundamental geometric product: 1 x · a = 2(x a + a x): Then rewrite (with linearity, associativity): x 7! x − (x a + a x) a=kak2 (GA product) = −a x a−1 with the geometric inverse of a vector: −1 2 FIG(7,1) a = a=kak . 2 3 Orthogonal Transformations as Products of Unit Vectors A reflection in two successive origin planes a and b: x 7! −b (−a x a−1) b−1 = (b a) x (b a)−1 So a rotation is represented by the geometric product of two vectors b a, also an element of the algebra. -
1 Vectors & Tensors
1 Vectors & Tensors The mathematical modeling of the physical world requires knowledge of quite a few different mathematics subjects, such as Calculus, Differential Equations and Linear Algebra. These topics are usually encountered in fundamental mathematics courses. However, in a more thorough and in-depth treatment of mechanics, it is essential to describe the physical world using the concept of the tensor, and so we begin this book with a comprehensive chapter on the tensor. The chapter is divided into three parts. The first part covers vectors (§1.1-1.7). The second part is concerned with second, and higher-order, tensors (§1.8-1.15). The second part covers much of the same ground as done in the first part, mainly generalizing the vector concepts and expressions to tensors. The final part (§1.16-1.19) (not required in the vast majority of applications) is concerned with generalizing the earlier work to curvilinear coordinate systems. The first part comprises basic vector algebra, such as the dot product and the cross product; the mathematics of how the components of a vector transform between different coordinate systems; the symbolic, index and matrix notations for vectors; the differentiation of vectors, including the gradient, the divergence and the curl; the integration of vectors, including line, double, surface and volume integrals, and the integral theorems. The second part comprises the definition of the tensor (and a re-definition of the vector); dyads and dyadics; the manipulation of tensors; properties of tensors, such as the trace, transpose, norm, determinant and principal values; special tensors, such as the spherical, identity and orthogonal tensors; the transformation of tensor components between different coordinate systems; the calculus of tensors, including the gradient of vectors and higher order tensors and the divergence of higher order tensors and special fourth order tensors. -
A Quick Study on Quaternions
A Quick Study on Quaternions George K. Francis Department of Mathematics University of Illinois [email protected] http://www.math.uiuc.edu/ gfrancis/ Motivation from Algebra (there are other motivations). In algebra, to solve mx = y for x we merely divide x = y/m. And if asked, what is this m, we again divide: m = y/x.1 For Hamilton, x and y were forces applied to two different points in space, think of steering a bicycle. In computer graphics, it might be two different places for the camera in space. For the two divisions to make sense in this context, we need to give a meaning to two inverses. (I have used m for “move”.) The first one, x = y/m = (/m)y, is a no-brainer: /m is the inverse operation, if there is one, for the operation of applying m to x. The second, m = y/x, is deeper: what is the reciprocal of a (bound) vector? Solution by generalizing Complex Numbers. For 2d-physics (or graphics) the answer resides in the complex numbers which were invented for different purposes long before Hamilton. Complex addition (and scalar multiplication) embodied the “vector” qualities (e.g. translation, as in the parallelogram rule) and complex multiplication took care of rotations. Hamilton found that if he went to 4D, his generalized complex numbers, the quaternions, had (almost) the same properties. The “almost” included the unexpected wrinkle that multiplication was no longer commutative, ab! = ba 1We use no special mathematical typography here to encourage readers to adopt a sim- ple, ascii keyboard style of mathematical notation. -
Working with 3D Rotations
Working With 3D Rotations Stan Melax Graphics Software Engineer, Intel Human Brain is wired for Spatial Computation Which shape is the same: a) b) c) “I don’t need to ask A childhood IQ test question for directions” Translations Rotations Agenda ● Rotations and Matrices (hopefully review) ● Combining Rotations ● Matrix and Axis Angle ● Challenges of deep Space (of Rotations) ● Quaternions ● Applications Terminology Clarification Preferred usages of various terms: Linear Angular Object Pose Position (point) Orientation A change in Pose Translation (vector) Rotation Rate of change Linear Velocity Spin also: Direction specifies 2 DOF, Orientation specifies all 3 angular DOF. Rotations Trickier than Translations Translations Rotations a then b == b then a x then y != y then x (non-commutative) ● Programming with rotations also more challenging! 2D Rotation θ Rotate [1 0] by θ about origin 1,1 [ cos(θ) sin(θ) ] θ θ sin cos θ 1,0 2D Rotation θ Rotate [0 1] by θ about origin -1,1 0,1 sin θ [-sin(θ) cos(θ)] θ θ cos 2D Rotation of an arbitrary point Rotate about origin by θ = cos θ + sin θ 2D Rotation of an arbitrary point 푥 Rotate 푦 about origin by θ 푥′, 푦′ 푥′ = 푥 cos θ − 푦 sin θ 푦′ = 푥 sin θ + 푦 cos θ 푦 푥 2D Rotation Matrix 푥 Rotate 푦 about origin by θ 푥′, 푦′ 푥′ = 푥 cos θ − 푦 sin θ 푦′ = 푥 sin θ + 푦 cos θ 푦 푥′ cos θ − sin θ 푥 푥 = 푦′ sin θ cos θ 푦 cos θ − sin θ Matrix is rotation by θ sin θ cos θ 2D Orientation 풚 Yellow grid placed over first grid but at angle of θ cos θ − sin θ sin θ cos θ 풙 Columns of the matrix are the directions of the axes. -
Clifford Algebra with Mathematica
Clifford Algebra with Mathematica J.L. ARAGON´ G. ARAGON-CAMARASA Universidad Nacional Aut´onoma de M´exico University of Glasgow Centro de F´ısica Aplicada School of Computing Science y Tecnolog´ıa Avanzada Sir Alwyn William Building, Apartado Postal 1-1010, 76000 Quer´etaro Glasgow, G12 8QQ Scotland MEXICO UNITED KINGDOM [email protected] [email protected] G. ARAGON-GONZ´ ALEZ´ M.A. RODRIGUEZ-ANDRADE´ Universidad Aut´onoma Metropolitana Instituto Polit´ecnico Nacional Unidad Azcapotzalco Departamento de Matem´aticas, ESFM San Pablo 180, Colonia Reynosa-Tamaulipas, UP Adolfo L´opez Mateos, 02200 D.F. M´exico Edificio 9. 07300 D.F. M´exico MEXICO MEXICO [email protected] [email protected] Abstract: The Clifford algebra of a n-dimensional Euclidean vector space provides a general language comprising vectors, complex numbers, quaternions, Grassman algebra, Pauli and Dirac matrices. In this work, we present an introduction to the main ideas of Clifford algebra, with the main goal to develop a package for Clifford algebra calculations for the computer algebra program Mathematica.∗ The Clifford algebra package is thus a powerful tool since it allows the manipulation of all Clifford mathematical objects. The package also provides a visualization tool for elements of Clifford Algebra in the 3-dimensional space. clifford.m is available from https://github.com/jlaragonvera/Geometric-Algebra Key–Words: Clifford Algebras, Geometric Algebra, Mathematica Software. 1 Introduction Mathematica, resulting in a package for doing Clif- ford algebra computations. There exists some other The importance of Clifford algebra was recognized packages and specialized programs for doing Clif- for the first time in quantum field theory. -
Construction of Multivector Inverse for Clif
Construction of multivector inverse for Clif- ford algebras over 2m+1-dimensional vector spaces from multivector inverse for Clifford algebras over 2m-dimensional vector spaces.∗ Eckhard Hitzer and Stephen J. Sangwine Abstract. Assuming known algebraic expressions for multivector inver- p0;q0 ses in any Clifford algebra over an even dimensional vector space R , n0 = p0 + q0 = 2m, we derive a closed algebraic expression for the multi- p;q vector inverse over vector spaces one dimension higher, namely over R , n = p+q = p0+q0+1 = 2m+1. Explicit examples are provided for dimen- sions n0 = 2; 4; 6, and the resulting inverses for n = n0 + 1 = 3; 5; 7. The general result for n = 7 appears to be the first ever reported closed al- gebraic expression for a multivector inverse in Clifford algebras Cl(p; q), n = p + q = 7, only involving a single addition of multivector products in forming the determinant. Mathematics Subject Classification (2010). Primary 15A66; Secondary 11E88, 15A15, 15A09. Keywords. Clifford algebra, multivector determinants, multivector in- verse. 1. Introduction The inverse of Clifford algebra multivectors is useful for the instant solution of multivector equations, like AXB = C, which gives with the inverses of A and B the solution X = A−1CB−1, A; B; C; X 2 Cl(p; q). Furthermore the inverse of geometric transformation versors is essential for computing two- sided spinor- and versor transformations as X0 = V −1XV , where V can in principle be factorized into a product of vectors from Rp;q, X 2 Cl(p; q). * This paper is dedicated to the Turkish journalist Dennis Y¨ucel,who is since 14. -
Algebraic Foundations of Split Hypercomplex Nonlinear Adaptive
Mathematical Methods in the Research Article Applied Sciences Received XXXX (www.interscience.wiley.com) DOI: 10.1002/sim.0000 MOS subject classification: 60G35; 15A66 Algebraic foundations of split hypercomplex nonlinear adaptive filtering E. Hitzer∗ A split hypercomplex learning algorithm for the training of nonlinear finite impulse response adaptive filters for the processing of hypercomplex signals of any dimension is proposed. The derivation strictly takes into account the laws of hypercomplex algebra and hypercomplex calculus, some of which have been neglected in existing learning approaches (e.g. for quaternions). Already in the case of quaternions we can predict improvements in performance of hypercomplex processes. The convergence of the proposed algorithms is rigorously analyzed. Copyright c 2011 John Wiley & Sons, Ltd. Keywords: Quaternionic adaptive filtering, Hypercomplex adaptive filtering, Nonlinear adaptive filtering, Hypercomplex Multilayer Perceptron, Clifford geometric algebra 1. Introduction Split quaternion nonlinear adaptive filtering has recently been treated by [23], who showed its superior performance for Saito’s Chaotic Signal and for wind forecasting. The quaternionic methods constitute a generalization of complex valued adaptive filters, treated in detail in [19]. A method of quaternionic least mean square algorithm for adaptive quaternionic has previously been developed in [22]. Additionally, [24] successfully proposes the usage of local analytic fully quaternionic functions in the Quaternion Nonlinear Gradient Descent (QNGD). Yet the unconditioned use of analytic fully quaternionic activation functions in neural networks faces problems with poles due to the Liouville theorem [3]. The quaternion algebra of Hamilton is a special case of the higher dimensional Clifford algebras [10]. The problem with poles in nonlinear analytic functions does not generally occur for hypercomplex activation functions in Clifford algebras, where the Dirac and the Cauchy-Riemann operators are not elliptic [21], as shown, e.g., for hyperbolic numbers in [17]. -
Introduction to Clifford's Geometric Algebra
SICE Journal of Control, Measurement, and System Integration, Vol. 4, No. 1, pp. 001–011, January 2011 Introduction to Clifford’s Geometric Algebra ∗ Eckhard HITZER Abstract : Geometric algebra was initiated by W.K. Clifford over 130 years ago. It unifies all branchesof physics, and has found rich applications in robotics, signal processing, ray tracing, virtual reality, computer vision, vector field processing, tracking, geographic information systems and neural computing. This tutorial explains the basics of geometric algebra, with concrete examples of the plane, of 3D space, of spacetime, and the popular conformal model. Geometric algebras are ideal to represent geometric transformations in the general framework of Clifford groups (also called versor or Lipschitz groups). Geometric (algebra based) calculus allows, e.g., to optimize learning algorithms of Clifford neurons, etc. Key Words: Hypercomplex algebra, hypercomplex analysis, geometry, science, engineering. 1. Introduction unique associative and multilinear algebra with this property. W.K. Clifford (1845-1879), a young English Goldsmid pro- Thus if we wish to generalize methods from algebra, analysis, ff fessor of applied mathematics at the University College of Lon- calculus, di erential geometry (etc.) of real numbers, complex don, published in 1878 in the American Journal of Mathemat- numbers, and quaternion algebra to vector spaces and multivec- ics Pure and Applied a nine page long paper on Applications of tor spaces (which include additional elements representing 2D Grassmann’s Extensive Algebra. In this paper, the young ge- up to nD subspaces, i.e. plane elements up to hypervolume el- ff nius Clifford, standing on the shoulders of two giants of alge- ements), the study of Cli ord algebras becomes unavoidable. -
Arxiv:2007.13674V2 [Hep-Ph] 1 Nov 2020 Contents
Prepared for submission to JHEP Universality-breaking effects in e+e− hadronic production processes M. Boglione, and A. Simonelli Dipartimento di Fisica Teorica, Universit`adi Torino, Via P. Giuria 1, I-10125 Torino, Italy INFN, Sezione di Torino, Via P. Giuria 1, I-10125 Torino, Italy E-mail: [email protected], [email protected] Abstract: Recent BELLE measurements provide the cross section for single hadron pro- duction in e+e− annihilations, differential in thrust and in the hadron transverse momen- tum with respect to the thrust axis. Universality breaking effects due to process-dependent soft factors make it very difficult to relate this cross sections to those corresponding to hadron-pair production in e+e− annihilations, where transverse momentum dependent (TMD) factorization can be applied. The correspondence between these two cross sections is examined in the framework of the Collins-Soper-Sterman factorization, in the collinear as well as in the TMD approach. We propose a scheme that allows to relate the TMD parton densities defined in 1-hadron and in 2-hadron processes, neatly separating, within the soft and collinear parts, the non-perturbative terms from the contributions that can be calculated perturbatively. The regularization of rapidity divergences introduces cut-offs, the arbitrariness of which will be properly reabsorbed by means of a mechanism closely reminiscent of a gauge transformation. In this way, we restore the possibility to perform global phenomenological studies of TMD physics, simultaneously analyzing data belonging to different hadron classes. arXiv:2007.13674v2 [hep-ph] 1 Nov 2020 Contents 1 Introduction2 1.1 Collinear and TMD Factorization3 2 Soft Factor6 2.1 2-h Soft Factor9 3 Collinear Parts and TMDs 11 3.1 Evolution Equations for TMDs 14 3.2 Rapidity dilations 16 3.2.1 Rapidity dilation and z-axis reflection 21 4 Universality and Process Classification 22 5 The 2-hadron class 25 5.1 2-h Class Cross Section 25 5.2 Factorization Definition vs. -
1 Vectors: Geometric Approach
c F. Waleffe, 2008/09/01 Vectors These are compact lecture notes for Math 321 at UW-Madison. Read them carefully, ideally before the lecture, and complete with your own class notes and pictures. Skipping the `theory' and jumping directly to the exercises is a tried-and-failed strategy that only leads to the typical question `I have no idea how to get started'. There are many explicit and implicit exercises within the text that complement the `theory'. Many of the `proofs' are actually good `solved exercises.' The objectives are to review the key concepts, emphasizing geometric understanding and visualization. 1 Vectors: Geometric Approach What's a vector? in elementary calculus and linear algebra you probably defined vectors as a list of numbers such as ~x = (4; 2; 5) with special algebraic manipulations rules, but in elementary physics vectors were probably defined as `quantities that have both a magnitude and a direction such as displacements, velocities and forces' as opposed to scalars, such as mass, temperature and pressure, which only have magnitude. We begin with the latter point of view because the algebraic version hides geometric issues that are important to physics, namely that physical laws are invariant under a change of coordinates - they do not depend on our particular choice of coordinates - and there is no special system of coordinates, everything is relative. Our other motivation is that to truly understand vectors, and math in general, you have to be able to visualize the concepts, so rather than developing the geometric interpretation as an after-thought, we start with it.