Introduction to Clifford's Geometric Algebra
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Introduction to Linear Bialgebra
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by University of New Mexico University of New Mexico UNM Digital Repository Mathematics and Statistics Faculty and Staff Publications Academic Department Resources 2005 INTRODUCTION TO LINEAR BIALGEBRA Florentin Smarandache University of New Mexico, [email protected] W.B. Vasantha Kandasamy K. Ilanthenral Follow this and additional works at: https://digitalrepository.unm.edu/math_fsp Part of the Algebra Commons, Analysis Commons, Discrete Mathematics and Combinatorics Commons, and the Other Mathematics Commons Recommended Citation Smarandache, Florentin; W.B. Vasantha Kandasamy; and K. Ilanthenral. "INTRODUCTION TO LINEAR BIALGEBRA." (2005). https://digitalrepository.unm.edu/math_fsp/232 This Book is brought to you for free and open access by the Academic Department Resources at UNM Digital Repository. It has been accepted for inclusion in Mathematics and Statistics Faculty and Staff Publications by an authorized administrator of UNM Digital Repository. For more information, please contact [email protected], [email protected], [email protected]. INTRODUCTION TO LINEAR BIALGEBRA W. B. Vasantha Kandasamy Department of Mathematics Indian Institute of Technology, Madras Chennai – 600036, India e-mail: [email protected] web: http://mat.iitm.ac.in/~wbv Florentin Smarandache Department of Mathematics University of New Mexico Gallup, NM 87301, USA e-mail: [email protected] K. Ilanthenral Editor, Maths Tiger, Quarterly Journal Flat No.11, Mayura Park, 16, Kazhikundram Main Road, Tharamani, Chennai – 600 113, India e-mail: [email protected] HEXIS Phoenix, Arizona 2005 1 This book can be ordered in a paper bound reprint from: Books on Demand ProQuest Information & Learning (University of Microfilm International) 300 N. -
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. -
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. -
A Bit About Hilbert Spaces
A Bit About Hilbert Spaces David Rosenberg New York University October 29, 2016 David Rosenberg (New York University ) DS-GA 1003 October 29, 2016 1 / 9 Inner Product Space (or “Pre-Hilbert” Spaces) An inner product space (over reals) is a vector space V and an inner product, which is a mapping h·,·i : V × V ! R that has the following properties 8x,y,z 2 V and a,b 2 R: Symmetry: hx,yi = hy,xi Linearity: hax + by,zi = ahx,zi + b hy,zi Postive-definiteness: hx,xi > 0 and hx,xi = 0 () x = 0. David Rosenberg (New York University ) DS-GA 1003 October 29, 2016 2 / 9 Norm from Inner Product For an inner product space, we define a norm as p kxk = hx,xi. Example Rd with standard Euclidean inner product is an inner product space: hx,yi := xT y 8x,y 2 Rd . Norm is p kxk = xT y. David Rosenberg (New York University ) DS-GA 1003 October 29, 2016 3 / 9 What norms can we get from an inner product? Theorem (Parallelogram Law) A norm kvk can be generated by an inner product on V iff 8x,y 2 V 2kxk2 + 2kyk2 = kx + yk2 + kx - yk2, and if it can, the inner product is given by the polarization identity jjxjj2 + jjyjj2 - jjx - yjj2 hx,yi = . 2 Example d `1 norm on R is NOT generated by an inner product. [Exercise] d Is `2 norm on R generated by an inner product? David Rosenberg (New York University ) DS-GA 1003 October 29, 2016 4 / 9 Pythagorean Theroem Definition Two vectors are orthogonal if hx,yi = 0. -
True False Questions from 4,5,6
(c)2015 UM Math Dept licensed under a Creative Commons By-NC-SA 4.0 International License. Math 217: True False Practice Professor Karen Smith 1. For every 2 × 2 matrix A, there exists a 2 × 2 matrix B such that det(A + B) 6= det A + det B. Solution note: False! Take A to be the zero matrix for a counterexample. 2. If the change of basis matrix SA!B = ~e4 ~e3 ~e2 ~e1 , then the elements of A are the same as the element of B, but in a different order. Solution note: True. The matrix tells us that the first element of A is the fourth element of B, the second element of basis A is the third element of B, the third element of basis A is the second element of B, and the the fourth element of basis A is the first element of B. 6 6 3. Every linear transformation R ! R is an isomorphism. Solution note: False. The zero map is a a counter example. 4. If matrix B is obtained from A by swapping two rows and det A < det B, then A is invertible. Solution note: True: we only need to know det A 6= 0. But if det A = 0, then det B = − det A = 0, so det A < det B does not hold. 5. If two n × n matrices have the same determinant, they are similar. Solution note: False: The only matrix similar to the zero matrix is itself. But many 1 0 matrices besides the zero matrix have determinant zero, such as : 0 0 6. -
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. -
The Determinant Inner Product and the Heisenberg Product of Sym(2)
INTERNATIONAL ELECTRONIC JOURNAL OF GEOMETRY VOLUME 14 NO. 1 PAGE 1–12 (2021) DOI: HTTPS://DOI.ORG/10.32323/IEJGEO.602178 The determinant inner product and the Heisenberg product of Sym(2) Mircea Crasmareanu (Dedicated to the memory of Prof. Dr. Aurel BEJANCU (1946 - 2020)Cihan Ozgur) ABSTRACT The aim of this work is to introduce and study the nondegenerate inner product < ·; · >det induced by the determinant map on the space Sym(2) of symmetric 2 × 2 real matrices. This symmetric bilinear form of index 2 defines a rational symmetric function on the pairs of rays in the plane and an associated function on the 2-torus can be expressed with the usual Hopf bundle projection 3 2 1 S ! S ( 2 ). Also, the product < ·; · >det is treated with complex numbers by using the Hopf invariant map of Sym(2) and this complex approach yields a Heisenberg product on Sym(2). Moreover, the quadratic equation of critical points for a rational Morse function of height type generates a cosymplectic structure on Sym(2) with the unitary matrix as associated Reeb vector and with the Reeb 1-form being half of the trace map. Keywords: symmetric matrix; determinant; Hopf bundle; Hopf invariant AMS Subject Classification (2020): Primary: 15A15 ; Secondary: 15A24; 30C10; 22E47. 1. Introduction This short paper, whose nature is mainly expository, is dedicated to the memory of Professor Dr. Aurel Bejancu, who was equally a gifted mathematician and a dedicated professor. As we hereby present an in memoriam paper, we feel it would be better if we choose an informal presentation rather than a classical structured discourse, where theorems and propositions are followed by their proofs. -
Inner Product Spaces
CHAPTER 6 Woman teaching geometry, from a fourteenth-century edition of Euclid’s geometry book. Inner Product Spaces In making the definition of a vector space, we generalized the linear structure (addition and scalar multiplication) of R2 and R3. We ignored other important features, such as the notions of length and angle. These ideas are embedded in the concept we now investigate, inner products. Our standing assumptions are as follows: 6.1 Notation F, V F denotes R or C. V denotes a vector space over F. LEARNING OBJECTIVES FOR THIS CHAPTER Cauchy–Schwarz Inequality Gram–Schmidt Procedure linear functionals on inner product spaces calculating minimum distance to a subspace Linear Algebra Done Right, third edition, by Sheldon Axler 164 CHAPTER 6 Inner Product Spaces 6.A Inner Products and Norms Inner Products To motivate the concept of inner prod- 2 3 x1 , x 2 uct, think of vectors in R and R as x arrows with initial point at the origin. x R2 R3 H L The length of a vector in or is called the norm of x, denoted x . 2 k k Thus for x .x1; x2/ R , we have The length of this vector x is p D2 2 2 x x1 x2 . p 2 2 x1 x2 . k k D C 3 C Similarly, if x .x1; x2; x3/ R , p 2D 2 2 2 then x x1 x2 x3 . k k D C C Even though we cannot draw pictures in higher dimensions, the gener- n n alization to R is obvious: we define the norm of x .x1; : : : ; xn/ R D 2 by p 2 2 x x1 xn : k k D C C The norm is not linear on Rn. -
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. -
Inner Product Spaces Isaiah Lankham, Bruno Nachtergaele, Anne Schilling (March 2, 2007)
MAT067 University of California, Davis Winter 2007 Inner Product Spaces Isaiah Lankham, Bruno Nachtergaele, Anne Schilling (March 2, 2007) The abstract definition of vector spaces only takes into account algebraic properties for the addition and scalar multiplication of vectors. For vectors in Rn, for example, we also have geometric intuition which involves the length of vectors or angles between vectors. In this section we discuss inner product spaces, which are vector spaces with an inner product defined on them, which allow us to introduce the notion of length (or norm) of vectors and concepts such as orthogonality. 1 Inner product In this section V is a finite-dimensional, nonzero vector space over F. Definition 1. An inner product on V is a map ·, · : V × V → F (u, v) →u, v with the following properties: 1. Linearity in first slot: u + v, w = u, w + v, w for all u, v, w ∈ V and au, v = au, v; 2. Positivity: v, v≥0 for all v ∈ V ; 3. Positive definiteness: v, v =0ifandonlyifv =0; 4. Conjugate symmetry: u, v = v, u for all u, v ∈ V . Remark 1. Recall that every real number x ∈ R equals its complex conjugate. Hence for real vector spaces the condition about conjugate symmetry becomes symmetry. Definition 2. An inner product space is a vector space over F together with an inner product ·, ·. Copyright c 2007 by the authors. These lecture notes may be reproduced in their entirety for non- commercial purposes. 2NORMS 2 Example 1. V = Fn n u =(u1,...,un),v =(v1,...,vn) ∈ F Then n u, v = uivi. -
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].