Geometric Algebra and Its Application to Diverse Areas of Mathematics
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Multivector Differentiation and Linear Algebra 0.5Cm 17Th Santaló
Multivector differentiation and Linear Algebra 17th Santalo´ Summer School 2016, Santander Joan Lasenby Signal Processing Group, Engineering Department, Cambridge, UK and Trinity College Cambridge [email protected], www-sigproc.eng.cam.ac.uk/ s jl 23 August 2016 1 / 78 Examples of differentiation wrt multivectors. Linear Algebra: matrices and tensors as linear functions mapping between elements of the algebra. Functional Differentiation: very briefly... Summary Overview The Multivector Derivative. 2 / 78 Linear Algebra: matrices and tensors as linear functions mapping between elements of the algebra. Functional Differentiation: very briefly... Summary Overview The Multivector Derivative. Examples of differentiation wrt multivectors. 3 / 78 Functional Differentiation: very briefly... Summary Overview The Multivector Derivative. Examples of differentiation wrt multivectors. Linear Algebra: matrices and tensors as linear functions mapping between elements of the algebra. 4 / 78 Summary Overview The Multivector Derivative. Examples of differentiation wrt multivectors. Linear Algebra: matrices and tensors as linear functions mapping between elements of the algebra. Functional Differentiation: very briefly... 5 / 78 Overview The Multivector Derivative. Examples of differentiation wrt multivectors. Linear Algebra: matrices and tensors as linear functions mapping between elements of the algebra. Functional Differentiation: very briefly... Summary 6 / 78 We now want to generalise this idea to enable us to find the derivative of F(X), in the A ‘direction’ – where X is a general mixed grade multivector (so F(X) is a general multivector valued function of X). Let us use ∗ to denote taking the scalar part, ie P ∗ Q ≡ hPQi. Then, provided A has same grades as X, it makes sense to define: F(X + tA) − F(X) A ∗ ¶XF(X) = lim t!0 t The Multivector Derivative Recall our definition of the directional derivative in the a direction F(x + ea) − F(x) a·r F(x) = lim e!0 e 7 / 78 Let us use ∗ to denote taking the scalar part, ie P ∗ Q ≡ hPQi. -
Determinants in Geometric Algebra
Determinants in Geometric Algebra Eckhard Hitzer 16 June 2003, recovered+expanded May 2020 1 Definition Let f be a linear map1, of a real linear vector space Rn into itself, an endomor- phism n 0 n f : a 2 R ! a 2 R : (1) This map is extended by outermorphism (symbol f) to act linearly on multi- vectors f(a1 ^ a2 ::: ^ ak) = f(a1) ^ f(a2) ::: ^ f(ak); k ≤ n: (2) By definition f is grade-preserving and linear, mapping multivectors to mul- tivectors. Examples are the reflections, rotations and translations described earlier. The outermorphism of a product of two linear maps fg is the product of the outermorphisms f g f[g(a1)] ^ f[g(a2)] ::: ^ f[g(ak)] = f[g(a1) ^ g(a2) ::: ^ g(ak)] = f[g(a1 ^ a2 ::: ^ ak)]; (3) with k ≤ n. The square brackets can safely be omitted. The n{grade pseudoscalars of a geometric algebra are unique up to a scalar factor. This can be used to define the determinant2 of a linear map as det(f) = f(I)I−1 = f(I) ∗ I−1; and therefore f(I) = det(f)I: (4) For an orthonormal basis fe1; e2;:::; eng the unit pseudoscalar is I = e1e2 ::: en −1 q q n(n−1)=2 with inverse I = (−1) enen−1 ::: e1 = (−1) (−1) I, where q gives the number of basis vectors, that square to −1 (the linear space is then Rp;q). According to Grassmann n-grade vectors represent oriented volume elements of dimension n. The determinant therefore shows how these volumes change under linear maps. -
Final Exam Outline 1.1, 1.2 Scalars and Vectors in R N. the Magnitude
Final Exam Outline 1.1, 1.2 Scalars and vectors in Rn. The magnitude of a vector. Row and column vectors. Vector addition. Scalar multiplication. The zero vector. Vector subtraction. Distance in Rn. Unit vectors. Linear combinations. The dot product. The angle between two vectors. Orthogonality. The CBS and triangle inequalities. Theorem 1.5. 2.1, 2.2 Systems of linear equations. Consistent and inconsistent systems. The coefficient and augmented matrices. Elementary row operations. The echelon form of a matrix. Gaussian elimination. Equivalent matrices. The reduced row echelon form. Gauss-Jordan elimination. Leading and free variables. The rank of a matrix. The rank theorem. Homogeneous systems. Theorem 2.2. 2.3 The span of a set of vectors. Linear dependence. Linear dependence and independence. Determining n whether a set {~v1, . ,~vk} of vectors in R is linearly dependent or independent. 3.1, 3.2 Matrix operations. Scalar multiplication, matrix addition, matrix multiplication. The zero and identity matrices. Partitioned matrices. The outer product form. The standard unit vectors. The transpose of a matrix. A system of linear equations in matrix form. Algebraic properties of the transpose. 3.3 The inverse of a matrix. Invertible (nonsingular) matrices. The uniqueness of the solution to Ax = b when A is invertible. The inverse of a nonsingular 2 × 2 matrix. Elementary matrices and EROs. Equivalent conditions for invertibility. Using Gauss-Jordan elimination to invert a nonsingular matrix. Theorems 3.7, 3.9. n n n 3.5 Subspaces of R . {0} and R as subspaces of R . The subspace span(v1,..., vk). The the row, column and null spaces of a matrix. -
Notices of the American Mathematical Society 35 Monticello Place, Pawtucket, RI 02861 USA American Mathematical Society Distribution Center
ISSN 0002-9920 Notices of the American Mathematical Society 35 Monticello Place, Pawtucket, RI 02861 USA Society Distribution Center American Mathematical of the American Mathematical Society February 2012 Volume 59, Number 2 Conformal Mappings in Geometric Algebra Page 264 Infl uential Mathematicians: Birth, Education, and Affi liation Page 274 Supporting the Next Generation of “Stewards” in Mathematics Education Page 288 Lawrence Meeting Page 352 Volume 59, Number 2, Pages 257–360, February 2012 About the Cover: MathJax (see page 346) Trim: 8.25" x 10.75" 104 pages on 40 lb Velocity • Spine: 1/8" • Print Cover on 9pt Carolina AMS-Simons Travel Grants TS AN GR EL The AMS is accepting applications for the second RAV T year of the AMS-Simons Travel Grants program. Each grant provides an early career mathematician with $2,000 per year for two years to reimburse travel expenses related to research. Sixty new awards will be made in 2012. Individuals who are not more than four years past the comple- tion of the PhD are eligible. The department of the awardee will also receive a small amount of funding to help enhance its research atmosphere. The deadline for 2012 applications is March 30, 2012. Applicants must be located in the United States or be U.S. citizens. For complete details of eligibility and application instructions, visit: www.ams.org/programs/travel-grants/AMS-SimonsTG Solve the differential equation. t ln t dr + r = 7tet dt 7et + C r = ln t WHO HAS THE #1 HOMEWORK SYSTEM FOR CALCULUS? THE ANSWER IS IN THE QUESTIONS. -
Introduction to Clifford's Geometric Algebra
解説:特集 コンピューテーショナル・インテリジェンスの新展開 —クリフォード代数表現など高次元表現を中心として— Introduction to Clifford’s Geometric Algebra Eckhard HITZER* *Department of Applied Physics, University of Fukui, Fukui, Japan *E-mail: [email protected] Key Words:hypercomplex algebra, hypercomplex analysis, geometry, science, engineering. JL 0004/12/5104–0338 C 2012 SICE erty 15), 21) that any isometry4 from the vector space V Abstract into an inner-product algebra5 A over the field6 K can Geometric algebra was initiated by W.K. Clifford over be uniquely extended to an isometry7 from the Clifford 130 years ago. It unifies all branches of physics, and algebra Cl(V )intoA. The Clifford algebra Cl(V )is has found rich applications in robotics, signal process- the unique associative and multilinear algebra with this ing, ray tracing, virtual reality, computer vision, vec- property. Thus if we wish to generalize methods from tor field processing, tracking, geographic information algebra, analysis, calculus, differential geometry (etc.) systems and neural computing. This tutorial explains of real numbers, complex numbers, and quaternion al- the basics of geometric algebra, with concrete exam- gebra to vector spaces and multivector spaces (which ples of the plane, of 3D space, of spacetime, and the include additional elements representing 2D up to nD popular conformal model. Geometric algebras are ideal subspaces, i.e. plane elements up to hypervolume ele- to represent geometric transformations in the general ments), the study of Clifford algebras becomes unavoid- framework of Clifford groups (also called versor or Lip- able. Indeed repeatedly and independently a long list of schitz groups). Geometric (algebra based) calculus al- Clifford algebras, their subalgebras and in Clifford al- lows e.g., to optimize learning algorithms of Clifford gebras embedded algebras (like octonions 17))ofmany neurons, etc. -
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. -
Geometric Algebra Techniques for General Relativity
Geometric Algebra Techniques for General Relativity Matthew R. Francis∗ and Arthur Kosowsky† Dept. of Physics and Astronomy, Rutgers University 136 Frelinghuysen Road, Piscataway, NJ 08854 (Dated: February 4, 2008) Geometric (Clifford) algebra provides an efficient mathematical language for describing physical problems. We formulate general relativity in this language. The resulting formalism combines the efficiency of differential forms with the straightforwardness of coordinate methods. We focus our attention on orthonormal frames and the associated connection bivector, using them to find the Schwarzschild and Kerr solutions, along with a detailed exposition of the Petrov types for the Weyl tensor. PACS numbers: 02.40.-k; 04.20.Cv Keywords: General relativity; Clifford algebras; solution techniques I. INTRODUCTION Geometric (or Clifford) algebra provides a simple and natural language for describing geometric concepts, a point which has been argued persuasively by Hestenes [1] and Lounesto [2] among many others. Geometric algebra (GA) unifies many other mathematical formalisms describing specific aspects of geometry, including complex variables, matrix algebra, projective geometry, and differential geometry. Gravitation, which is usually viewed as a geometric theory, is a natural candidate for translation into the language of geometric algebra. This has been done for some aspects of gravitational theory; notably, Hestenes and Sobczyk have shown how geometric algebra greatly simplifies certain calculations involving the curvature tensor and provides techniques for classifying the Weyl tensor [3, 4]. Lasenby, Doran, and Gull [5] have also discussed gravitation using geometric algebra via a reformulation in terms of a gauge principle. In this paper, we formulate standard general relativity in terms of geometric algebra. A comprehensive overview like the one presented here has not previously appeared in the literature, although unpublished works of Hestenes and of Doran take significant steps in this direction. -
Download Grunberg's Meet and Join
The Meet and Join, Constraints Between Them, and Their Transformation under Outermorphism A supplementary discussion by Greg Grunberg of Sections 5.1—5.7 of the textbook Geometric Algebra for Computer Science, by Dorst, Fontijne, & Mann (2007) Given two subspaces A and B of the overall vector space, the largest subspace common to both of them is called the meet of those subspaces, and as a set is the intersection A ∩ B of those subspaces. The join of the two given subspaces is the smallest superspace common to both of them, and as a set is the sum A + B = {x1 + x2 : x1 ∈ A and x2 ∈ B} of those subspaces. A + B is not usually a “direct sum” A B, as x1 x2 join the decomposition + of a nonzero element of the is uniquely determined only when A ∩ B= {0}. The system of subspaces, with its subset partial ordering and meet and join operations, is an example of the type of algebraic system called a “lattice”. Recall that a subspace A can be represented by a blade A, with A = {x : x ∧ A = 0}. If A is unoriented, then any scalar multiple αA (α = 0) also represents A; if A is oriented, then the scalar must be positive. Our emphasis is on the algebra of the representing blades, so hereafter we usually do not refer to A and B themselves but rather to their representatives A and B. When necessary we will abuse language and refer to the “subspace” A or B rather than to A or B. We will examine the meet and join of two given blades A and B, both before and after application of an (invertible) transformation f to get new blades A¯ = f [A] and B¯ = f [B]. -
Introduction to MATLAB®
Introduction to MATLAB® Lecture 2: Vectors and Matrices Introduction to MATLAB® Lecture 2: Vectors and Matrices 1 / 25 Vectors and Matrices Vectors and Matrices Vectors and matrices are used to store values of the same type A vector can be either column vector or a row vector. Matrices can be visualized as a table of values with dimensions r × c (r is the number of rows and c is the number of columns). Introduction to MATLAB® Lecture 2: Vectors and Matrices 2 / 25 Vectors and Matrices Creating row vectors Place the values that you want in the vector in square brackets separated by either spaces or commas. e.g 1 >> row vec=[1 2 3 4 5] 2 row vec = 3 1 2 3 4 5 4 5 >> row vec=[1,2,3,4,5] 6 row vec= 7 1 2 3 4 5 Introduction to MATLAB® Lecture 2: Vectors and Matrices 3 / 25 Vectors and Matrices Creating row vectors - colon operator If the values of the vectors are regularly spaced, the colon operator can be used to iterate through these values. (first:last) produces a vector with all integer entries from first to last e.g. 1 2 >> row vec = 1:5 3 row vec = 4 1 2 3 4 5 Introduction to MATLAB® Lecture 2: Vectors and Matrices 4 / 25 Vectors and Matrices Creating row vectors - colon operator A step value can also be specified with another colon in the form (first:step:last) 1 2 >>odd vec = 1:2:9 3 odd vec = 4 1 3 5 7 9 Introduction to MATLAB® Lecture 2: Vectors and Matrices 5 / 25 Vectors and Matrices Exercise In using (first:step:last), what happens if adding the step value would go beyond the range specified by last? e.g: 1 >>v = 1:2:6 Introduction to MATLAB® Lecture 2: Vectors and Matrices 6 / 25 Vectors and Matrices Exercise Use (first:step:last) to generate the vector v1 = [9 7 5 3 1 ]? Introduction to MATLAB® Lecture 2: Vectors and Matrices 7 / 25 Vectors and Matrices Creating row vectors - linspace function linspace (Linearly spaced vector) 1 >>linspace(x,y,n) linspace creates a row vector with n values in the inclusive range from x to y. -
Geometric Algebra: a Computational Framework for Geometrical
Feature Tutorial Geometric Algebra: A Computational Framework For Geometrical Stephen Mann Applications University of Waterloo Leo Dorst Part 2 University of Amsterdam his is the second of a two-part tutorial on defines a rotation/dilation operator. Let us do a slight- Tgeometric algebra. In part one,1 we intro- ly simpler problem. In Figure 1a, if a and b have the duced blades, a computational algebraic representa- same norm, what is the vector x in the (a ∧ b) plane that tion of oriented subspaces, which are the basic elements is to c as the vector b is to a? Geometric algebra phras- of computation in geometric algebra. We also looked es this as xc–1 = ba–1 and solves it (see Equation 13 at the geometric product and two products derived from part one1) as from it, the inner and outer products. A crucial feature of the geometric product is that it is invertible. − 1 xbac= ( 1) =⋅+∧(ba b a) c From that first article, you should 2 have gathered that every vector a This second part of our space with an inner product has a b − φ geometric algebra, whether or not =−(cosφφIc sin ) = e I c (1) tutorial uses geometric you choose to use it. This article a shows how to call on this structure algebra to represent to define common geometrical con- Here Iφ is the angle in the I plane from a to b, so –Iφ is structs, ensuring a consistent com- the angle from b to a. Figure 1a suggests that we obtain rotations, intersections, and putational framework. -
Math 102 -- Linear Algebra I -- Study Guide
Math 102 Linear Algebra I Stefan Martynkiw These notes are adapted from lecture notes taught by Dr.Alan Thompson and from “Elementary Linear Algebra: 10th Edition” :Howard Anton. Picture above sourced from (http://i.imgur.com/RgmnA.gif) 1/52 Table of Contents Chapter 3 – Euclidean Vector Spaces.........................................................................................................7 3.1 – Vectors in 2-space, 3-space, and n-space......................................................................................7 Theorem 3.1.1 – Algebraic Vector Operations without components...........................................7 Theorem 3.1.2 .............................................................................................................................7 3.2 – Norm, Dot Product, and Distance................................................................................................7 Definition 1 – Norm of a Vector..................................................................................................7 Definition 2 – Distance in Rn......................................................................................................7 Dot Product.......................................................................................................................................8 Definition 3 – Dot Product...........................................................................................................8 Definition 4 – Dot Product, Component by component..............................................................8 -
Lesson 1 Introduction to MATLAB
Math 323 Linear Algebra and Matrix Theory I Fall 1999 Dr. Constant J. Goutziers Department of Mathematical Sciences [email protected] Lesson 1 Introduction to MATLAB MATLAB is an interactive system whose basic data element is an array that does not require dimensioning. This allows easy solution of many technical computing problems, especially those with matrix and vector formulations. For many colleges and universities MATLAB is the Linear Algebra software of choice. The name MATLAB stands for matrix laboratory. 1.1 Row and Column vectors, Formatting In linear algebra we make a clear distinction between row and column vectors. • Example 1.1.1 Entering a row vector. The components of a row vector are entered inside square brackets, separated by blanks. v=[1 2 3] v = 1 2 3 • Example 1.1.2 Entering a column vector. The components of a column vector are also entered inside square brackets, but they are separated by semicolons. w=[4; 5/2; -6] w = 4.0000 2.5000 -6.0000 • Example 1.1.3 Switching between a row and a column vector, the transpose. In MATLAB it is possible to switch between row and column vectors using an apostrophe. The process is called "taking the transpose". To illustrate the process we print v and w and generate the transpose of each of these vectors. Observe that MATLAB commands can be entered on the same line, as long as they are separated by commas. v, vt=v', w, wt=w' v = 1 2 3 vt = 1 2 3 w = 4.0000 2.5000 -6.0000 wt = 4.0000 2.5000 -6.0000 • Example 1.1.4 Formatting.