Vectors, Matrices and Coordinate Transformations
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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 . -
MATH 2030: MATRICES Introduction to Linear Transformations We Have
MATH 2030: MATRICES Introduction to Linear Transformations We have seen that we may describe matrices as symbol with simple algebraic properties like matrix multiplication, addition and scalar addition. In the particular case of matrix-vector multiplication, i.e., Ax = b where A is an m × n matrix and x; b are n×1 matrices (column vectors) we may represent this as a transformation on the space of column vectors, that is a function F (x) = b , where x is the independent variable and b the dependent variable. In this section we will give a more rigorous description of this idea and provide examples of such matrix transformations, which will lead to the idea of a linear transformation. To begin we look at a matrix-vector multiplication to give an idea of what sort of functions we are working with 21 0 3 1 A = 2 −1 ; v = : 4 5 −1 3 4 then matrix-vector multiplication yields 2 1 3 Av = 4 3 5 −1 We have taken a 2 × 1 matrix and produced a 3 × 1 matrix. More generally for any x we may describe this transformation as a matrix equation y 21 0 3 2 x 3 x 2 −1 = 2x − y : 4 5 y 4 5 3 4 3x + 4y From this product we have found a formula describing how A transforms an arbi- 2 3 trary vector in R into a new vector in R . Expressing this as a transformation TA we have 2 x 3 x T = 2x − y : A y 4 5 3x + 4y From this example we can define some helpful terminology. -
DERIVATIONS and PROJECTIONS on JORDAN TRIPLES an Introduction to Nonassociative Algebra, Continuous Cohomology, and Quantum Functional Analysis
DERIVATIONS AND PROJECTIONS ON JORDAN TRIPLES An introduction to nonassociative algebra, continuous cohomology, and quantum functional analysis Bernard Russo July 29, 2014 This paper is an elaborated version of the material presented by the author in a three hour minicourse at V International Course of Mathematical Analysis in Andalusia, at Almeria, Spain September 12-16, 2011. The author wishes to thank the scientific committee for the opportunity to present the course and to the organizing committee for their hospitality. The author also personally thanks Antonio Peralta for his collegiality and encouragement. The minicourse on which this paper is based had its genesis in a series of talks the author had given to undergraduates at Fullerton College in California. I thank my former student Dana Clahane for his initiative in running the remarkable undergraduate research program at Fullerton College of which the seminar series is a part. With their knowledge only of the product rule for differentiation as a starting point, these enthusiastic students were introduced to some aspects of the esoteric subject of non associative algebra, including triple systems as well as algebras. Slides of these talks and of the minicourse lectures, as well as other related material, can be found at the author's website (www.math.uci.edu/∼brusso). Conversely, these undergraduate talks were motivated by the author's past and recent joint works on derivations of Jordan triples ([116],[117],[200]), which are among the many results discussed here. Part I (Derivations) is devoted to an exposition of the properties of derivations on various algebras and triple systems in finite and infinite dimensions, the primary questions addressed being whether the derivation is automatically continuous and to what extent it is an inner derivation. -
Triple Product Formula and the Subconvexity Bound of Triple Product L-Function in Level Aspect
TRIPLE PRODUCT FORMULA AND THE SUBCONVEXITY BOUND OF TRIPLE PRODUCT L-FUNCTION IN LEVEL ASPECT YUEKE HU Abstract. In this paper we derived a nice general formula for the local integrals of triple product formula whenever one of the representations has sufficiently higher level than the other two. As an application we generalized Venkatesh and Woodbury’s work on the subconvexity bound of triple product L-function in level aspect, allowing joint ramifications, higher ramifications, general unitary central characters and general special values of local epsilon factors. 1. introduction 1.1. Triple product formula. Let F be a number field. Let ⇡i, i = 1, 2, 3 be three irreducible unitary cuspidal automorphic representations, such that the product of their central characters is trivial: (1.1) w⇡i = 1. Yi Let ⇧=⇡ ⇡ ⇡ . Then one can define the triple product L-function L(⇧, s) associated to them. 1 ⌦ 2 ⌦ 3 It was first studied in [6] by Garrett in classical languages, where explicit integral representation was given. In particular the triple product L-function has analytic continuation and functional equation. Later on Shapiro and Rallis in [19] reformulated his work in adelic languages. In this paper we shall study the following integral representing the special value of triple product L function (see Section 2.2 for more details): − ⇣2(2)L(⇧, 1/2) (1.2) f (g) f (g) f (g)dg 2 = F I0( f , f , f ), | 1 2 3 | ⇧, , v 1,v 2,v 3,v 8L( Ad 1) v ZAD (FZ) D (A) ⇤ \ ⇤ Y D D Here fi ⇡i for a specific quaternion algebra D, and ⇡i is the image of ⇡i under Jacquet-Langlands 2 0 correspondence. -
Support Graph Preconditioners for Sparse Linear Systems
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Texas A&M University SUPPORT GRAPH PRECONDITIONERS FOR SPARSE LINEAR SYSTEMS A Thesis by RADHIKA GUPTA Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE December 2004 Major Subject: Computer Science SUPPORT GRAPH PRECONDITIONERS FOR SPARSE LINEAR SYSTEMS A Thesis by RADHIKA GUPTA Submitted to Texas A&M University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Approved as to style and content by: Vivek Sarin Paul Nelson (Chair of Committee) (Member) N. K. Anand Valerie E. Taylor (Member) (Head of Department) December 2004 Major Subject: Computer Science iii ABSTRACT Support Graph Preconditioners for Sparse Linear Systems. (December 2004) Radhika Gupta, B.E., Indian Institute of Technology, Bombay; M.S., Georgia Institute of Technology, Atlanta Chair of Advisory Committee: Dr. Vivek Sarin Elliptic partial differential equations that are used to model physical phenomena give rise to large sparse linear systems. Such systems can be symmetric positive definite and can be solved by the preconditioned conjugate gradients method. In this thesis, we develop support graph preconditioners for symmetric positive definite matrices that arise from the finite element discretization of elliptic partial differential equations. An object oriented code is developed for the construction, integration and application of these preconditioners. Experimental results show that the advantages of support graph preconditioners are retained in the proposed extension to the finite element matrices. iv To my parents v ACKNOWLEDGMENTS I would like to express sincere thanks to my advisor, Dr. -
Vector Analysis
DOING PHYSICS WITH MATLAB VECTOR ANANYSIS Ian Cooper School of Physics, University of Sydney [email protected] DOWNLOAD DIRECTORY FOR MATLAB SCRIPTS cemVectorsA.m Inputs: Cartesian components of the vector V Outputs: cylindrical and spherical components and [3D] plot of vector cemVectorsB.m Inputs: Cartesian components of the vectors A B C Outputs: dot products, cross products and triple products cemVectorsC.m Rotation of XY axes around Z axis to give new of reference X’Y’Z’. Inputs: rotation angle and vector (Cartesian components) in XYZ frame Outputs: Cartesian components of vector in X’Y’Z’ frame mscript can be modified to calculate the rotation matrix for a [3D] rotation and give the Cartesian components of the vector in the X’Y’Z’ frame of reference. Doing Physics with Matlab 1 SPECIFYING a [3D] VECTOR A scalar is completely characterised by its magnitude, and has no associated direction (mass, time, direction, work). A scalar is given by a simple number. A vector has both a magnitude and direction (force, electric field, magnetic field). A vector can be specified in terms of its Cartesian or cylindrical (polar in [2D]) or spherical coordinates. Cartesian coordinate system (XYZ right-handed rectangular: if we curl our fingers on the right hand so they rotate from the X axis to the Y axis then the Z axis is in the direction of the thumb). A vector V in specified in terms of its X, Y and Z Cartesian components ˆˆˆ VVVV x,, y z VViVjVk x y z where iˆˆ,, j kˆ are unit vectors parallel to the X, Y and Z axes respectively. -
Matrices and Tensors
APPENDIX MATRICES AND TENSORS A.1. INTRODUCTION AND RATIONALE The purpose of this appendix is to present the notation and most of the mathematical tech- niques that are used in the body of the text. The audience is assumed to have been through sev- eral years of college-level mathematics, which included the differential and integral calculus, differential equations, functions of several variables, partial derivatives, and an introduction to linear algebra. Matrices are reviewed briefly, and determinants, vectors, and tensors of order two are described. The application of this linear algebra to material that appears in under- graduate engineering courses on mechanics is illustrated by discussions of concepts like the area and mass moments of inertia, Mohr’s circles, and the vector cross and triple scalar prod- ucts. The notation, as far as possible, will be a matrix notation that is easily entered into exist- ing symbolic computational programs like Maple, Mathematica, Matlab, and Mathcad. The desire to represent the components of three-dimensional fourth-order tensors that appear in anisotropic elasticity as the components of six-dimensional second-order tensors and thus rep- resent these components in matrices of tensor components in six dimensions leads to the non- traditional part of this appendix. This is also one of the nontraditional aspects in the text of the book, but a minor one. This is described in §A.11, along with the rationale for this approach. A.2. DEFINITION OF SQUARE, COLUMN, AND ROW MATRICES An r-by-c matrix, M, is a rectangular array of numbers consisting of r rows and c columns: ¯MM.. -
Chapter 5 ANGULAR MOMENTUM and ROTATIONS
Chapter 5 ANGULAR MOMENTUM AND ROTATIONS In classical mechanics the total angular momentum L~ of an isolated system about any …xed point is conserved. The existence of a conserved vector L~ associated with such a system is itself a consequence of the fact that the associated Hamiltonian (or Lagrangian) is invariant under rotations, i.e., if the coordinates and momenta of the entire system are rotated “rigidly” about some point, the energy of the system is unchanged and, more importantly, is the same function of the dynamical variables as it was before the rotation. Such a circumstance would not apply, e.g., to a system lying in an externally imposed gravitational …eld pointing in some speci…c direction. Thus, the invariance of an isolated system under rotations ultimately arises from the fact that, in the absence of external …elds of this sort, space is isotropic; it behaves the same way in all directions. Not surprisingly, therefore, in quantum mechanics the individual Cartesian com- ponents Li of the total angular momentum operator L~ of an isolated system are also constants of the motion. The di¤erent components of L~ are not, however, compatible quantum observables. Indeed, as we will see the operators representing the components of angular momentum along di¤erent directions do not generally commute with one an- other. Thus, the vector operator L~ is not, strictly speaking, an observable, since it does not have a complete basis of eigenstates (which would have to be simultaneous eigenstates of all of its non-commuting components). This lack of commutivity often seems, at …rst encounter, as somewhat of a nuisance but, in fact, it intimately re‡ects the underlying structure of the three dimensional space in which we are immersed, and has its source in the fact that rotations in three dimensions about di¤erent axes do not commute with one another. -
Solving the Geodesic Equation
Solving the Geodesic Equation Jeremy Atkins December 12, 2018 Abstract We find the general form of the geodesic equation and discuss the closed form relation to find Christoffel symbols. We then show how to use metric independence to find Killing vector fields, which allow us to solve the geodesic equation when there are helpful symmetries. We also discuss a more general way to find Killing vector fields, and some of their properties as a Lie algebra. 1 The Variational Method We will exploit the following variational principle to characterize motion in general relativity: The world line of a free test particle between two timelike separated points extremizes the proper time between them. where a test particle is one that is not a significant source of spacetime cur- vature, and a free particles is one that is only under the influence of curved spacetime. Similarly to classical Lagrangian mechanics, we can use this to de- duce the equations of motion for a metric. The proper time along a timeline worldline between point A and point B for the metric gµν is given by Z B Z B µ ν 1=2 τAB = dτ = (−gµν (x)dx dx ) (1) A A using the Einstein summation notation, and µ, ν = 0; 1; 2; 3. We can parame- terize the four coordinates with the parameter σ where σ = 0 at A and σ = 1 at B. This gives us the following equation for the proper time: Z 1 dxµ dxν 1=2 τAB = dσ −gµν (x) (2) 0 dσ dσ We can treat the integrand as a Lagrangian, dxµ dxν 1=2 L = −gµν (x) (3) dσ dσ and it's clear that the world lines extremizing proper time are those that satisfy the Euler-Lagrange equation: @L d @L − = 0 (4) @xµ dσ @(dxµ/dσ) 1 These four equations together give the equation for the worldline extremizing the proper time. -
Geodetic Position Computations
GEODETIC POSITION COMPUTATIONS E. J. KRAKIWSKY D. B. THOMSON February 1974 TECHNICALLECTURE NOTES REPORT NO.NO. 21739 PREFACE In order to make our extensive series of lecture notes more readily available, we have scanned the old master copies and produced electronic versions in Portable Document Format. The quality of the images varies depending on the quality of the originals. The images have not been converted to searchable text. GEODETIC POSITION COMPUTATIONS E.J. Krakiwsky D.B. Thomson Department of Geodesy and Geomatics Engineering University of New Brunswick P.O. Box 4400 Fredericton. N .B. Canada E3B5A3 February 197 4 Latest Reprinting December 1995 PREFACE The purpose of these notes is to give the theory and use of some methods of computing the geodetic positions of points on a reference ellipsoid and on the terrain. Justification for the first three sections o{ these lecture notes, which are concerned with the classical problem of "cCDputation of geodetic positions on the surface of an ellipsoid" is not easy to come by. It can onl.y be stated that the attempt has been to produce a self contained package , cont8.i.ning the complete development of same representative methods that exist in the literature. The last section is an introduction to three dimensional computation methods , and is offered as an alternative to the classical approach. Several problems, and their respective solutions, are presented. The approach t~en herein is to perform complete derivations, thus stqing awrq f'rcm the practice of giving a list of for11111lae to use in the solution of' a problem. -
The Dot Product
The Dot Product In this section, we will now concentrate on the vector operation called the dot product. The dot product of two vectors will produce a scalar instead of a vector as in the other operations that we examined in the previous section. The dot product is equal to the sum of the product of the horizontal components and the product of the vertical components. If v = a1 i + b1 j and w = a2 i + b2 j are vectors then their dot product is given by: v · w = a1 a2 + b1 b2 Properties of the Dot Product If u, v, and w are vectors and c is a scalar then: u · v = v · u u · (v + w) = u · v + u · w 0 · v = 0 v · v = || v || 2 (cu) · v = c(u · v) = u · (cv) Example 1: If v = 5i + 2j and w = 3i – 7j then find v · w. Solution: v · w = a1 a2 + b1 b2 v · w = (5)(3) + (2)(-7) v · w = 15 – 14 v · w = 1 Example 2: If u = –i + 3j, v = 7i – 4j and w = 2i + j then find (3u) · (v + w). Solution: Find 3u 3u = 3(–i + 3j) 3u = –3i + 9j Find v + w v + w = (7i – 4j) + (2i + j) v + w = (7 + 2) i + (–4 + 1) j v + w = 9i – 3j Example 2 (Continued): Find the dot product between (3u) and (v + w) (3u) · (v + w) = (–3i + 9j) · (9i – 3j) (3u) · (v + w) = (–3)(9) + (9)(-3) (3u) · (v + w) = –27 – 27 (3u) · (v + w) = –54 An alternate formula for the dot product is available by using the angle between the two vectors. -
Cross Product Review
12.4 Cross Product Review: The dot product of uuuu123, , and v vvv 123, , is u v uvuvuv 112233 uv u u u u v u v cos or cos uv u and v are orthogonal if and only if u v 0 u uv uv compvu projvuv v v vv projvu cross product u v uv23 uv 32 i uv 13 uv 31 j uv 12 uv 21 k u v is orthogonal to both u and v. u v u v sin Geometric description of the cross product of the vectors u and v The cross product of two vectors is a vector! • u x v is perpendicular to u and v • The length of u x v is u v u v sin • The direction is given by the right hand side rule Right hand rule Place your 4 fingers in the direction of the first vector, curl them in the direction of the second vector, Your thumb will point in the direction of the cross product Algebraic description of the cross product of the vectors u and v The cross product of uu1, u 2 , u 3 and v v 1, v 2 , v 3 is uv uv23 uvuv 3231,, uvuv 1312 uv 21 check (u v ) u 0 and ( u v ) v 0 (u v ) u uv23 uvuv 3231 , uvuv 1312 , uv 21 uuu 123 , , uvu231 uvu 321 uvu 312 uvu 132 uvu 123 uvu 213 0 similary: (u v ) v 0 length u v u v sin is a little messier : 2 2 2 2 2 2 2 2uv 2 2 2 uvuv sin22 uv 1 cos uv 1 uvuv 22 uv now need to show that u v2 u 2 v 2 u v2 (try it..) An easier way to remember the formula for the cross products is in terms of determinants: ab 12 2x2 determinant: ad bc 4 6 2 cd 34 3x3 determinants: An example Copy 1st 2 columns 1 6 2 sum of sum of 1 6 2 1 6 forward backward 3 1 3 3 1 3 3 1 diagonal diagonal 4 5 2 4 5 2 4 5 products products determinant = 2 72 30 8 15 36 40 59 19 recall: uv uv23 uvuv 3231, uvuv 1312 , uv 21 i j k i j k i j u1 u 2 u 3 u 1 u 2 now we claim that uvu1 u 2 u 3 v1 v 2 v 3 v 1 v 2 v1 v 2 v 3 iuv23 j uv 31 k uv 12 k uv 21 i uv 32 j uv 13 u v uv23 uv 32 i uv 13 uv 31 j uv 12 uv 21 k uv uv23 uvuv 3231,, uvuv 1312 uv 21 Example: Let u1, 2,1 and v 3,1, 2 Find u v.