Introduction to Vectors and Tensors Volume 1
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Tensors and Differential Forms on Vector Spaces
APPENDIX A TENSORS AND DIFFERENTIAL FORMS ON VECTOR SPACES Since only so much of the vast and growing field of differential forms and differentiable manifolds will be actually used in this survey, we shall attempt to briefly review how the calculus of exterior differential forms on vector spaces can serve as a replacement for the more conventional vector calculus and then introduce only the most elementary notions regarding more topologically general differentiable manifolds, which will mostly be used as the basis for the discussion of Lie groups, in the following appendix. Since exterior differential forms are special kinds of tensor fields – namely, completely-antisymmetric covariant ones – and tensors are important to physics, in their own right, we shall first review the basic notions concerning tensors and multilinear algebra. Presumably, the reader is familiar with linear algebra as it is usually taught to physicists, but for the “basis-free” approach to linear and multilinear algebra (which we shall not always adhere to fanatically), it would also help to have some familiarity with the more “abstract-algebraic” approach to linear algebra, such as one might learn from Hoffman and Kunze [ 1], for instance. 1. Tensor algebra. – A tensor algebra is a type of algebra in which multiplication takes the form of the tensor product. a. Tensor product. – Although the tensor product of vector spaces can be given a rigorous definition in a more abstract-algebraic context (See Greub [ 2], for instance), for the purposes of actual calculations with tensors and tensor fields, it is usually sufficient to say that if V and W are vector spaces of dimensions n and m, respectively, then the tensor product V ⊗ W will be a vector space of dimension nm whose elements are finite linear combinations of elements of the form v ⊗ w, where v is a vector in V and w is a vector in W. -
Equivalence of A-Approximate Continuity for Self-Adjoint
Equivalence of A-Approximate Continuity for Self-Adjoint Expansive Linear Maps a,1 b, ,2 Sz. Gy. R´ev´esz , A. San Antol´ın ∗ aA. R´enyi Institute of Mathematics, Hungarian Academy of Sciences, Budapest, P.O.B. 127, 1364 Hungary bDepartamento de Matem´aticas, Universidad Aut´onoma de Madrid, 28049 Madrid, Spain Abstract Let A : Rd Rd, d 1, be an expansive linear map. The notion of A-approximate −→ ≥ continuity was recently used to give a characterization of scaling functions in a multiresolution analysis (MRA). The definition of A-approximate continuity at a point x – or, equivalently, the definition of the family of sets having x as point of A-density – depend on the expansive linear map A. The aim of the present paper is to characterize those self-adjoint expansive linear maps A , A : Rd Rd for which 1 2 → the respective concepts of Aµ-approximate continuity (µ = 1, 2) coincide. These we apply to analyze the equivalence among dilation matrices for a construction of systems of MRA. In particular, we give a full description for the equivalence class of the dyadic dilation matrix among all self-adjoint expansive maps. If the so-called “four exponentials conjecture” of algebraic number theory holds true, then a similar full description follows even for general self-adjoint expansive linear maps, too. arXiv:math/0703349v2 [math.CA] 7 Sep 2007 Key words: A-approximate continuity, multiresolution analysis, point of A-density, self-adjoint expansive linear map. 1 Supported in part in the framework of the Hungarian-Spanish Scientific and Technological Governmental Cooperation, Project # E-38/04. -
Tensors Notation • Contravariant Denoted by Superscript Ai Took a Vector and Gave for Vector Calculus Us a Vector
Tensors Notation • contravariant denoted by superscript Ai took a vector and gave For vector calculus us a vector • covariant denoted by subscript Ai took a scaler and gave us a vector Review To avoid confusion in cartesian coordinates both types are the same so • Vectors we just opt for the subscript. Thus a vector x would be x1,x2,x3 R3 • Summation representation of an n by n array As it turns out in an cartesian space and other rectilinear coordi- nate systems there is no difference between contravariant and covariant • Gradient, Divergence and Curl vectors. This will not be the case for other coordinate systems such a • Spherical Harmonics (maybe) curvilinear coordinate systems or in 4 dimensions. These definitions are closely related to the Jacobian. Motivation If you tape a book shut and try to spin it in the air on each indepen- Definitions for Tensors of Rank 2 dent axis you will notice that it spins fine on two axes but not on the Rank 2 tensors can be written as a square array. They have con- third. That’s the inertia tensor in your hands. Similar are the polar- travariant, mixed, and covariant forms. As we might expect in cartesian izations tensor, index of refraction tensor and stress tensor. But tensors coordinates these are the same. also show up in all sorts of places that don’t connect to an anisotropic material property, in fact even spherical harmonics are tensors. What are the similarities and differences between such a plethora of tensors? Vector Calculus and Identifers The mathematics of tensors is particularly useful for de- Tensor analysis extends deep into coordinate transformations of all scribing properties of substances which vary in direction– kinds of spaces and coordinate systems. -
LECTURES on PURE SPINORS and MOMENT MAPS Contents 1
LECTURES ON PURE SPINORS AND MOMENT MAPS E. MEINRENKEN Contents 1. Introduction 1 2. Volume forms on conjugacy classes 1 3. Clifford algebras and spinors 3 4. Linear Dirac geometry 8 5. The Cartan-Dirac structure 11 6. Dirac structures 13 7. Group-valued moment maps 16 References 20 1. Introduction This article is an expanded version of notes for my lectures at the summer school on `Poisson geometry in mathematics and physics' at Keio University, Yokohama, June 5{9 2006. The plan of these lectures was to give an elementary introduction to the theory of Dirac structures, with applications to Lie group valued moment maps. Special emphasis was given to the pure spinor approach to Dirac structures, developed in Alekseev-Xu [7] and Gualtieri [20]. (See [11, 12, 16] for the more standard approach.) The connection to moment maps was made in the work of Bursztyn-Crainic [10]. Parts of these lecture notes are based on a forthcoming joint paper [1] with Anton Alekseev and Henrique Bursztyn. I would like to thank the organizers of the school, Yoshi Maeda and Guiseppe Dito, for the opportunity to deliver these lectures, and for a greatly enjoyable meeting. I also thank Yvette Kosmann-Schwarzbach and the referee for a number of helpful comments. 2. Volume forms on conjugacy classes We will begin with the following FACT, which at first sight may seem quite unrelated to the theme of these lectures: FACT. Let G be a simply connected semi-simple real Lie group. Then every conjugacy class in G carries a canonical invariant volume form. -
21. Orthonormal Bases
21. Orthonormal Bases The canonical/standard basis 011 001 001 B C B C B C B0C B1C B0C e1 = B.C ; e2 = B.C ; : : : ; en = B.C B.C B.C B.C @.A @.A @.A 0 0 1 has many useful properties. • Each of the standard basis vectors has unit length: q p T jjeijj = ei ei = ei ei = 1: • The standard basis vectors are orthogonal (in other words, at right angles or perpendicular). T ei ej = ei ej = 0 when i 6= j This is summarized by ( 1 i = j eT e = δ = ; i j ij 0 i 6= j where δij is the Kronecker delta. Notice that the Kronecker delta gives the entries of the identity matrix. Given column vectors v and w, we have seen that the dot product v w is the same as the matrix multiplication vT w. This is the inner product on n T R . We can also form the outer product vw , which gives a square matrix. 1 The outer product on the standard basis vectors is interesting. Set T Π1 = e1e1 011 B C B0C = B.C 1 0 ::: 0 B.C @.A 0 01 0 ::: 01 B C B0 0 ::: 0C = B. .C B. .C @. .A 0 0 ::: 0 . T Πn = enen 001 B C B0C = B.C 0 0 ::: 1 B.C @.A 1 00 0 ::: 01 B C B0 0 ::: 0C = B. .C B. .C @. .A 0 0 ::: 1 In short, Πi is the diagonal square matrix with a 1 in the ith diagonal position and zeros everywhere else. -
Abstract Tensor Systems and Diagrammatic Representations
Abstract tensor systems and diagrammatic representations J¯anisLazovskis September 28, 2012 Abstract The diagrammatic tensor calculus used by Roger Penrose (most notably in [7]) is introduced without a solid mathematical grounding. We will attempt to derive the tools of such a system, but in a broader setting. We show that Penrose's work comes from the diagrammisation of the symmetric algebra. Lie algebra representations and their extensions to knot theory are also discussed. Contents 1 Abstract tensors and derived structures 2 1.1 Abstract tensor notation . 2 1.2 Some basic operations . 3 1.3 Tensor diagrams . 3 2 A diagrammised abstract tensor system 6 2.1 Generation . 6 2.2 Tensor concepts . 9 3 Representations of algebras 11 3.1 The symmetric algebra . 12 3.2 Lie algebras . 13 3.3 The tensor algebra T(g)....................................... 16 3.4 The symmetric Lie algebra S(g)................................... 17 3.5 The universal enveloping algebra U(g) ............................... 18 3.6 The metrized Lie algebra . 20 3.6.1 Diagrammisation with a bilinear form . 20 3.6.2 Diagrammisation with a symmetric bilinear form . 24 3.6.3 Diagrammisation with a symmetric bilinear form and an orthonormal basis . 24 3.6.4 Diagrammisation under ad-invariance . 29 3.7 The universal enveloping algebra U(g) for a metrized Lie algebra g . 30 4 Ensuing connections 32 A Appendix 35 Note: This work relies heavily upon the text of Chapter 12 of a draft of \An Introduction to Quantum and Vassiliev Invariants of Knots," by David M.R. Jackson and Iain Moffatt, a yet-unpublished book at the time of writing. -
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. -
Tensor Products
Tensor products Joel Kamnitzer April 5, 2011 1 The definition Let V, W, X be three vector spaces. A bilinear map from V × W to X is a function H : V × W → X such that H(av1 + v2, w) = aH(v1, w) + H(v2, w) for v1,v2 ∈ V, w ∈ W, a ∈ F H(v, aw1 + w2) = aH(v, w1) + H(v, w2) for v ∈ V, w1, w2 ∈ W, a ∈ F Let V and W be vector spaces. A tensor product of V and W is a vector space V ⊗ W along with a bilinear map φ : V × W → V ⊗ W , such that for every vector space X and every bilinear map H : V × W → X, there exists a unique linear map T : V ⊗ W → X such that H = T ◦ φ. In other words, giving a linear map from V ⊗ W to X is the same thing as giving a bilinear map from V × W to X. If V ⊗ W is a tensor product, then we write v ⊗ w := φ(v ⊗ w). Note that there are two pieces of data in a tensor product: a vector space V ⊗ W and a bilinear map φ : V × W → V ⊗ W . Here are the main results about tensor products summarized in one theorem. Theorem 1.1. (i) Any two tensor products of V, W are isomorphic. (ii) V, W has a tensor product. (iii) If v1,...,vn is a basis for V and w1,...,wm is a basis for W , then {vi ⊗ wj }1≤i≤n,1≤j≤m is a basis for V ⊗ W . -
Multilinear Algebra and Applications July 15, 2014
Multilinear Algebra and Applications July 15, 2014. Contents Chapter 1. Introduction 1 Chapter 2. Review of Linear Algebra 5 2.1. Vector Spaces and Subspaces 5 2.2. Bases 7 2.3. The Einstein convention 10 2.3.1. Change of bases, revisited 12 2.3.2. The Kronecker delta symbol 13 2.4. Linear Transformations 14 2.4.1. Similar matrices 18 2.5. Eigenbases 19 Chapter 3. Multilinear Forms 23 3.1. Linear Forms 23 3.1.1. Definition, Examples, Dual and Dual Basis 23 3.1.2. Transformation of Linear Forms under a Change of Basis 26 3.2. Bilinear Forms 30 3.2.1. Definition, Examples and Basis 30 3.2.2. Tensor product of two linear forms on V 32 3.2.3. Transformation of Bilinear Forms under a Change of Basis 33 3.3. Multilinear forms 34 3.4. Examples 35 3.4.1. A Bilinear Form 35 3.4.2. A Trilinear Form 36 3.5. Basic Operation on Multilinear Forms 37 Chapter 4. Inner Products 39 4.1. Definitions and First Properties 39 4.1.1. Correspondence Between Inner Products and Symmetric Positive Definite Matrices 40 4.1.1.1. From Inner Products to Symmetric Positive Definite Matrices 42 4.1.1.2. From Symmetric Positive Definite Matrices to Inner Products 42 4.1.2. Orthonormal Basis 42 4.2. Reciprocal Basis 46 4.2.1. Properties of Reciprocal Bases 48 4.2.2. Change of basis from a basis to its reciprocal basis g 50 B B III IV CONTENTS 4.2.3. -
On Manifolds of Tensors of Fixed Tt-Rank
ON MANIFOLDS OF TENSORS OF FIXED TT-RANK SEBASTIAN HOLTZ, THORSTEN ROHWEDDER, AND REINHOLD SCHNEIDER Abstract. Recently, the format of TT tensors [19, 38, 34, 39] has turned out to be a promising new format for the approximation of solutions of high dimensional problems. In this paper, we prove some new results for the TT representation of a tensor U ∈ Rn1×...×nd and for the manifold of tensors of TT-rank r. As a first result, we prove that the TT (or compression) ranks ri of a tensor U are unique and equal to the respective seperation ranks of U if the components of the TT decomposition are required to fulfil a certain maximal rank condition. We then show that d the set T of TT tensors of fixed rank r forms an embedded manifold in Rn , therefore preserving the essential theoretical properties of the Tucker format, but often showing an improved scaling behaviour. Extending a similar approach for matrices [7], we introduce certain gauge conditions to obtain a unique representation of the tangent space TU T of T and deduce a local parametrization of the TT manifold. The parametrisation of TU T is often crucial for an algorithmic treatment of high-dimensional time-dependent PDEs and minimisation problems [33]. We conclude with remarks on those applications and present some numerical examples. 1. Introduction The treatment of high-dimensional problems, typically of problems involving quantities from Rd for larger dimensions d, is still a challenging task for numerical approxima- tion. This is owed to the principal problem that classical approaches for their treatment normally scale exponentially in the dimension d in both needed storage and computa- tional time and thus quickly become computationally infeasable for sensible discretiza- tions of problems of interest. -
Causalx: Causal Explanations and Block Multilinear Factor Analysis
To appear: Proc. of the 2020 25th International Conference on Pattern Recognition (ICPR 2020) Milan, Italy, Jan. 10-15, 2021. CausalX: Causal eXplanations and Block Multilinear Factor Analysis M. Alex O. Vasilescu1;2 Eric Kim2;1 Xiao S. Zeng2 [email protected] [email protected] [email protected] 1Tensor Vision Technologies, Los Angeles, California 2Department of Computer Science,University of California, Los Angeles Abstract—By adhering to the dictum, “No causation without I. INTRODUCTION:PROBLEM DEFINITION manipulation (treatment, intervention)”, cause and effect data Developing causal explanations for correct results or for failures analysis represents changes in observed data in terms of changes from mathematical equations and data is important in developing in the causal factors. When causal factors are not amenable for a trustworthy artificial intelligence, and retaining public trust. active manipulation in the real world due to current technological limitations or ethical considerations, a counterfactual approach Causal explanations are germane to the “right to an explanation” performs an intervention on the model of data formation. In the statute [15], [13] i.e., to data driven decisions, such as those case of object representation or activity (temporal object) rep- that rely on images. Computer graphics and computer vision resentation, varying object parts is generally unfeasible whether problems, also known as forward and inverse imaging problems, they be spatial and/or temporal. Multilinear algebra, the algebra have been cast as causal inference questions [40], [42] consistent of higher order tensors, is a suitable and transparent framework for disentangling the causal factors of data formation. Learning a with Donald Rubin’s quantitative definition of causality, where part-based intrinsic causal factor representations in a multilinear “A causes B” means “the effect of A is B”, a measurable framework requires applying a set of interventions on a part- and experimentally repeatable quantity [14], [17]. -
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..