Math 321 Vector and Complex Calculus for the Physical Sciences

Total Page:16

File Type:pdf, Size:1020Kb

Math 321 Vector and Complex Calculus for the Physical Sciences Math 321 Vector and Complex Calculus for the Physical Sciences c Fabian Waleffe Department of Mathematics & Department of Engineering Physics University of Wisconsin, Madison 2016/1/18 ii \Any fool can know. The point is to understand." | Albert Einstein Contents 1 Vector Geometry and Algebra 1 1 Get your bearings . .1 1.1 Magnitude and direction . .1 1.2 Representations of 2D vectors . .2 1.3 Representations of 3D vectors . .3 2 Addition and scaling of vectors . .6 3 General Vector Spaces . .7 3.1 Axiomatic definition of a vector space . .7 3.2 The vector space Rn .....................7 4 Bases and Components . .8 5 Points and Coordinates . 11 5.1 Position vector . 11 5.2 Lines and Planes . 13 6 Dot Product . 14 7 Orthonormal bases . 18 8 Dot product and norm in Rn (Optional) . 20 9 Cross Product . 22 10 Index notation . 29 10.1 Levi-Civita symbol . 29 10.2 Einstein summation convention . 30 11 Mixed product and Determinant . 34 12 Points, Lines, Planes, etc. 39 13 Orthogonal Transformations and Matrices . 42 13.1 Change of cartesian basis . 42 13.2 Matrices . 46 13.3 Spherical coordinates with a twist: Euler Angles . 48 13.4 Determinant of a matrix (Optional) . 52 13.5 Three views of Ax = b (Optional) . 53 13.6 Eigenvalues and Eigenvectors (Math 320 not 321) . 55 2 Vector Calculus 57 1 Vector function of a scalar variable . 57 2 Motion of a particle . 59 3 Motion of a system of particles (optional) . 62 iii iv 4 Motion of a rigid body (optional) . 63 5 Curves . 66 5.1 Elementary curves . 66 5.2 Speeding through Curves . 67 5.3 Integrals along curves, or `line integrals' . 70 6 Surfaces . 73 6.1 Parameterizations . 73 6.2 Surface integrals . 80 6.3 Curves on surfaces . 81 7 Volumes . 82 8 Maps, curvilinear coordinates . 85 9 Change of variables . 88 10 Grad, div, curl . 93 10.1 Scalar fields and iso-contours . 93 10.2 Geometric concept of the Gradient . 93 10.3 Directional derivative, gradient and the r operator . 95 10.4 Div and Curl . 97 10.5 Vector identities . 97 10.6 Grad, Div, Curl in cylindrical and spherical coordinates . 99 11 Fundamental examples of vector fields . 103 12 Fundamental theorems of vector calculus . 104 12.1 Integration in R2 and R3 .................. 104 12.2 Fundamental theorem of Calculus . 105 12.3 Fundamental theorem in R2 ................. 105 12.4 Green and Stokes' theorems . 107 12.5 Divergence form of Green's theorem . 110 12.6 Gauss' theorem . 111 12.7 Other forms of the fundamental theorem in 3D . 113 3 Complex Calculus 117 1 Complex Numbers and Elementary functions . 117 1.1 Complex Algebra and Geometry . 117 1.2 Limits and Derivatives . 122 1.3 Geometric sums and series . 124 1.4 Ratio test . 125 1.5 Power series . 126 1.6 Complex transcendentals . 128 1.7 Polar representation . 131 1.8 Logs and complex powers . 132 2 Functions of a complex variable . 137 2.1 Visualization of complex functions . 137 2.2 Cauchy-Riemann equations . 138 2.3 Geometry of Cauchy-Riemann, Conformal Mapping . 142 2.4 Conformal mapping examples . 145 2.5 Joukowski Map . 149 3 Integration of Complex Functions . 151 c F. Waleffe, Math 321, 2016/1/18 v 3.1 Complex integrals are path integrals . 151 3.2 Cauchy's theorem . 152 3.3 Poles and Residues . 153 3.4 Cauchy's formula . 158 4 Real examples of complex integration . 161 Chapter 1 Vector Geometry and Algebra What is a vector? In calculus, you may have defined vectors as lists of numbers such as (a1; a2) or (a1; a2; a3), mathematicians favor that approach nowadays because it readily generalizes to n dimensional space (a1; a2; : : : ; an) and re- duces geometry to arithmetic. But in physics, and in geometry before that, you encountered vectors as quantities with both magnitude and direction such as displacements, velocities and forces. The geometric point of view emphasizes the invariance of these quantities with respect to the observer and the system of coordinates. 1 Get your bearings 1.1 Magnitude and direction We write a = a a^ (1) for a vector a of magnitude jaj = a in the direction a^. The magnitude of vector a is denoted jaj which is a positive real number with appropriate physical units N (meters, Newtons, . ), jaj ≥ 0. The direction of vector a is denoted a^ which is a vector of unit magnitude, ja^j = 1. For that reason, direction vectors are often called unit vectors, but `unit' vectors have no physical units. For example, a ≡ 2 km heading 30◦clockwise from North a 30◦ specifies a horizontal displacement a of magnitude jaj = 2 kilometers and di- rection a^ ≡ 30◦ clockwise from North. −−! Two points A and B specify a displacement vector a = AB. The same −−! −−! displacement a starting from point C leads to point D, with CD = a = AB. Conversely, we can specify points (locations) by specifying displacements from 1 2 a reference point, thus point B is located displacement a from point A, and D D is a from C. B Vectors are denoted with a boldface: a, b, u, v, . , usually lower-case but some- a times upper-case as in a magnetic field B, electric field E or force F . Some authors write ~a for a and we use that notation to denote the displacement from a −−! C point A to point B as AB. Writing by hand, we use the typographical notation A for boldface which is a for a,a ^ for a^, etc. This notation allows distinguish- e e ing between a collection of unit vectors fa^1; a^2; a^3g ≡ fa^1,a ^2,a ^3g and the e e e components of a unit vector a^ ≡ a^ ≡ (^a1; a^2; a^3). e 1.2 Representations of 2D vectors For vectors in a plane (2D), a direction a^ can be specified by an angle from a reference direction as in the section 1.1 example. In navigation, that angle is usually defined clockwise from North and called an azimuthal angle or azimuth or heading. In mathematical physics, we specify the direction using an angle α counterclockwise from a reference direction x^. We can specify a 2D vector a by giving the pair (a; α) for vector a of magnitude jaj = a and direction a^ ≡ \angle α counterclockwise from x^." a a a ayy^ α x^ axx^ Figure 1.1: Polar and cartesian representations of vectors in a plane. We can also specify the vector a as a sum of displacements in two reference perpendicular directions, ax in direction x^ and ay in direction y^ perpendicular to x^. Now the pair (ax; ay) specifies a. The pairs (a; α) and (ax; ay) are equivalent representations of a, a ≡ (a; α) ≡ (ax; ay) but in general they are not equal (a; α) 6= (ax; ay): To express the equality of the representations, we need to include the direction vectors and write a = a a^ = ax x^ + ay y^ (2) then by Pythagoras and basic trigonometry ) ax = a cos α 2 2 2 , a = ax + ay; (3) ay = a sin α c F. Waleffe, Math 321, 2016/1/18 3 a^ = cos α x^ + sin α y^: (4) This confirms that a direction vector a^ is a vector of unit magnitude. The representation a = a a^, where a^ = a^(α) is a function of α, is a polar representation of the 2D vector a and a = axx^+ayy^ is a cartesian representation with x^ and y^ two arbitrary perpendicular directions. The vector a is specified by the pair (a; α) or (ax; ay), or many other pairs of numbers depending on how we specify the direction, for instance `20 feet toward the water tower then 5 meters toward the oak tree' specifies a displace- ment as (20ft,5m) but the reference directions may not be perpendicular. The same physical 2D vector can be represented by an infinity of pairs of numbers depending on how we choose our reference magnitudes and directions. Exercises: 1. If you move 2 miles heading 030◦ then 3 miles heading 290◦, how far are you from your original position? what heading from your original position? Make a sketch. All headings are measured clockwise from North. 2. If you move distance a heading α then distance b heading β, how far are you and in what heading from your original position? Make a sketch and show/explain your algorithm. Headings are clockwise from North. 3. Find jvj and v^ for the vector v ≡ −5 miles per hour heading Northeast. 4. True or False: in SI units ja^j = 1 meter while in CGS units ja^j = 1 cm. 1.3 Representations of 3D vectors How do you specify an arbitrary direction a^ in 3D space? Astronomers use an azimuth angle measured clockwise from North in the azelzen.gif 592×470 pixels 8/30/14 6:46 PM horizontal plane and an inclination (or zenith) angle measured from the vertical. An altitude (or elevation) angle measured up from the horizontal plane may be used instead of the inclination. Azimuth and elevation angles are also used in ballistics and computer graphics. The illustration is from NOAA, the National Ocean and Atmosphere Administration. Figure 1.2 shows the mathematical physics convention, where we use the angle β between a reference direction z^ (typically the vertical direction or the polar axis) and the arbitrary direction a^, as well as the angle α between the (z^; x^) and the (z^; a^) planes. Thus α is an azimuthal angle but defined to be posi- tive counterclockwise around z^ as opposed to the clockwise convention used in navigation.
Recommended publications
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • Introduction to Vector and Tensor Analysis
    Introduction to vector and tensor analysis Jesper Ferkinghoff-Borg September 6, 2007 Contents 1 Physical space 3 1.1 Coordinate systems . 3 1.2 Distances . 3 1.3 Symmetries . 4 2 Scalars and vectors 5 2.1 Definitions . 5 2.2 Basic vector algebra . 5 2.2.1 Scalar product . 6 2.2.2 Cross product . 7 2.3 Coordinate systems and bases . 7 2.3.1 Components and notation . 9 2.3.2 Triplet algebra . 10 2.4 Orthonormal bases . 11 2.4.1 Scalar product . 11 2.4.2 Cross product . 12 2.5 Ordinary derivatives and integrals of vectors . 13 2.5.1 Derivatives . 14 2.5.2 Integrals . 14 2.6 Fields . 15 2.6.1 Definition . 15 2.6.2 Partial derivatives . 15 2.6.3 Differentials . 16 2.6.4 Gradient, divergence and curl . 17 2.6.5 Line, surface and volume integrals . 20 2.6.6 Integral theorems . 24 2.7 Curvilinear coordinates . 26 2.7.1 Cylindrical coordinates . 27 2.7.2 Spherical coordinates . 28 3 Tensors 30 3.1 Definition . 30 3.2 Outer product . 31 3.3 Basic tensor algebra . 31 1 3.3.1 Transposed tensors . 32 3.3.2 Contraction . 33 3.3.3 Special tensors . 33 3.4 Tensor components in orthonormal bases . 34 3.4.1 Matrix algebra . 35 3.4.2 Two-point components . 38 3.5 Tensor fields . 38 3.5.1 Gradient, divergence and curl . 39 3.5.2 Integral theorems . 40 4 Tensor calculus 42 4.1 Tensor notation and Einsteins summation rule .
    [Show full text]
  • Basics of Euclidean Geometry
    This is page 162 Printer: Opaque this 6 Basics of Euclidean Geometry Rien n'est beau que le vrai. |Hermann Minkowski 6.1 Inner Products, Euclidean Spaces In a±ne geometry it is possible to deal with ratios of vectors and barycen- ters of points, but there is no way to express the notion of length of a line segment or to talk about orthogonality of vectors. A Euclidean structure allows us to deal with metric notions such as orthogonality and length (or distance). This chapter and the next two cover the bare bones of Euclidean ge- ometry. One of our main goals is to give the basic properties of the transformations that preserve the Euclidean structure, rotations and re- ections, since they play an important role in practice. As a±ne geometry is the study of properties invariant under bijective a±ne maps and projec- tive geometry is the study of properties invariant under bijective projective maps, Euclidean geometry is the study of properties invariant under certain a±ne maps called rigid motions. Rigid motions are the maps that preserve the distance between points. Such maps are, in fact, a±ne and bijective (at least in the ¯nite{dimensional case; see Lemma 7.4.3). They form a group Is(n) of a±ne maps whose corresponding linear maps form the group O(n) of orthogonal transformations. The subgroup SE(n) of Is(n) corresponds to the orientation{preserving rigid motions, and there is a corresponding 6.1. Inner Products, Euclidean Spaces 163 subgroup SO(n) of O(n), the group of rotations.
    [Show full text]
  • Beyond Scalar, Vector and Tensor Harmonics in Maximally Symmetric Three-Dimensional Spaces
    Beyond scalar, vector and tensor harmonics in maximally symmetric three-dimensional spaces Cyril Pitrou1, ∗ and Thiago S. Pereira2, y 1Institut d'Astrophysique de Paris, CNRS UMR 7095, 98 bis Bd Arago, 75014 Paris, France. 2Departamento de F´ısica, Universidade Estadual de Londrina, Rod. Celso Garcia Cid, Km 380, 86057-970, Londrina, Paran´a,Brazil. (Dated: October 1, 2019) We present a comprehensive construction of scalar, vector and tensor harmonics on max- imally symmetric three-dimensional spaces. Our formalism relies on the introduction of spin-weighted spherical harmonics and a generalized helicity basis which, together, are ideal tools to decompose harmonics into their radial and angular dependencies. We provide a thorough and self-contained set of expressions and relations for these harmonics. Being gen- eral, our formalism also allows to build harmonics of higher tensor type by recursion among radial functions, and we collect the complete set of recursive relations which can be used. While the formalism is readily adapted to computation of CMB transfer functions, we also collect explicit forms of the radial harmonics which are needed for other cosmological ob- servables. Finally, we show that in curved spaces, normal modes cannot be factorized into a local angular dependence and a unit norm function encoding the orbital dependence of the harmonics, contrary to previous statements in the literature. 1. INTRODUCTION ables. Indeed, on cosmological scales, where lin- ear perturbation theory successfully accounts for the formation of structures, perturbation modes, Tensor harmonics are ubiquitous tools in that is the components in an expansion on tensor gravitational theories. Their applicability reach harmonics, evolve independently from one an- a wide spectrum of topics including black-hole other.
    [Show full text]
  • 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
    [Show full text]
  • 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.
    [Show full text]
  • The Grassmannian Sigma Model in SU(2) Yang-Mills Theory
    The Grassmannian σ-model in SU(2) Yang-Mills Theory David Marsh Undergraduate Thesis Supervisor: Antti Niemi Department of Theoretical Physics Uppsala University Contents 1 Introduction 4 1.1 Introduction ............................ 4 2 Some Quantum Field Theory 7 2.1 Gauge Theories .......................... 7 2.1.1 Basic Yang-Mills Theory ................. 8 2.1.2 Faddeev, Popov and Ghosts ............... 10 2.1.3 Geometric Digression ................... 14 2.2 The nonlinear sigma models .................. 15 2.2.1 The Linear Sigma Model ................. 15 2.2.2 The Historical Example ................. 16 2.2.3 Geometric Treatment ................... 17 3 Basic properties of the Grassmannian 19 3.1 Standard Local Coordinates ................... 19 3.2 Plücker coordinates ........................ 21 3.3 The Grassmannian as a homogeneous space .......... 23 3.4 The complexied four-vector ................... 25 4 The Grassmannian σ Model in SU(2) Yang-Mills Theory 30 4.1 The SU(2) Yang-Mills Theory in spin-charge separated variables 30 4.1.1 Variables .......................... 30 4.1.2 The Physics of Spin-Charge Separation ......... 34 4.2 Appearance of the ostensible model ............... 36 4.3 The Grassmannian Sigma Model ................ 38 4.3.1 Gauge Formalism ..................... 38 4.3.2 Projector Formalism ................... 39 4.3.3 Induced Metric Formalism ................ 40 4.4 Embedding Equations ...................... 43 4.5 Hodge Star Decomposition .................... 48 2 4.6 Some Complex Dierential Geometry .............. 50 4.7 Quaternionic Decomposition ................... 54 4.7.1 Verication ........................ 56 5 The Interaction 58 5.1 The Interaction Terms ...................... 58 5.1.1 Limits ........................... 59 5.2 Holomorphic forms and Dolbeault Cohomology ........ 60 5.3 The Form of the interaction ..................
    [Show full text]
  • Inner Product and Orthogonality
    Math 240 TA: Shuyi Weng Winter 2017 March 10, 2017 Inner Product and Orthogonality Inner Product The notion of inner product is important in linear algebra in the sense that it provides a sensible notion of length and angle in a vector space. This seems very natural in the Euclidean space Rn through the concept of dot product. However, the inner product is much more general and can be extended to other non-Euclidean vector spaces. For this course, you are not required to understand the non-Euclidean examples. I just want to show you a glimpse of linear algebra in a more general setting in mathematics. Definition. Let V be a vector space. An inner product on V is a function h−; −i: V × V ! R such that for all vectors u; v; w 2 V and scalar c 2 R, it satisfies the axioms 1. hu; vi = hv; ui; 2. hu + v; wi = hu; wi + hv; wi, and hu; v + wi = hu; vi + hu; wi; 3. hcu; vi = chu; vi + hu; cvi; 4. hu; ui ≥ 0, and hu; ui = 0 if and only if u = 0. Definition. In a Euclidean space Rn, the dot product of two vectors u and v is defined to be the function u · v = uT v: In coordinates, if we write 2 3 2 3 u1 v1 6 . 7 6 . 7 u = 6 . 7 ; and v = 6 . 7 ; 4 5 4 5 un vn then 2 3 v1 T h i 6 . 7 u · v = u v = u1 ··· un 6 . 7 = u1v1 + ··· unvn: 4 5 vn The definitions in the remainder of this note will assume the Euclidean vector space Rn, and the dot product as the natural inner product.
    [Show full text]