Bilinear Covariants and Spinor Field Classification in Quantum Clifford Algebras
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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. -
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]. -
28. Exterior Powers
28. Exterior powers 28.1 Desiderata 28.2 Definitions, uniqueness, existence 28.3 Some elementary facts 28.4 Exterior powers Vif of maps 28.5 Exterior powers of free modules 28.6 Determinants revisited 28.7 Minors of matrices 28.8 Uniqueness in the structure theorem 28.9 Cartan's lemma 28.10 Cayley-Hamilton Theorem 28.11 Worked examples While many of the arguments here have analogues for tensor products, it is worthwhile to repeat these arguments with the relevant variations, both for practice, and to be sensitive to the differences. 1. Desiderata Again, we review missing items in our development of linear algebra. We are missing a development of determinants of matrices whose entries may be in commutative rings, rather than fields. We would like an intrinsic definition of determinants of endomorphisms, rather than one that depends upon a choice of coordinates, even if we eventually prove that the determinant is independent of the coordinates. We anticipate that Artin's axiomatization of determinants of matrices should be mirrored in much of what we do here. We want a direct and natural proof of the Cayley-Hamilton theorem. Linear algebra over fields is insufficient, since the introduction of the indeterminate x in the definition of the characteristic polynomial takes us outside the class of vector spaces over fields. We want to give a conceptual proof for the uniqueness part of the structure theorem for finitely-generated modules over principal ideal domains. Multi-linear algebra over fields is surely insufficient for this. 417 418 Exterior powers 2. Definitions, uniqueness, existence Let R be a commutative ring with 1. -
Multilinear Algebra in Data Analysis: Tensors, Symmetric Tensors, Nonnegative Tensors
Multilinear Algebra in Data Analysis: tensors, symmetric tensors, nonnegative tensors Lek-Heng Lim Stanford University Workshop on Algorithms for Modern Massive Datasets Stanford, CA June 21–24, 2006 Thanks: G. Carlsson, L. De Lathauwer, J.M. Landsberg, M. Mahoney, L. Qi, B. Sturmfels; Collaborators: P. Comon, V. de Silva, P. Drineas, G. Golub References http://www-sccm.stanford.edu/nf-publications-tech.html [CGLM2] P. Comon, G. Golub, L.-H. Lim, and B. Mourrain, “Symmetric tensors and symmetric tensor rank,” SCCM Tech. Rep., 06-02, 2006. [CGLM1] P. Comon, B. Mourrain, L.-H. Lim, and G.H. Golub, “Genericity and rank deficiency of high order symmetric tensors,” Proc. IEEE Int. Con- ference on Acoustics, Speech, and Signal Processing (ICASSP), 31 (2006), no. 3, pp. 125–128. [dSL] V. de Silva and L.-H. Lim, “Tensor rank and the ill-posedness of the best low-rank approximation problem,” SCCM Tech. Rep., 06-06 (2006). [GL] G. Golub and L.-H. Lim, “Nonnegative decomposition and approximation of nonnegative matrices and tensors,” SCCM Tech. Rep., 06-01 (2006), forthcoming. [L] L.-H. Lim, “Singular values and eigenvalues of tensors: a variational approach,” Proc. IEEE Int. Workshop on Computational Advances in Multi- Sensor Adaptive Processing (CAMSAP), 1 (2005), pp. 129–132. 2 What is not a tensor, I • What is a vector? – Mathematician: An element of a vector space. – Physicist: “What kind of physical quantities can be rep- resented by vectors?” Answer: Once a basis is chosen, an n-dimensional vector is something that is represented by n real numbers only if those real numbers transform themselves as expected (ie. -
Multilinear Algebra
Appendix A Multilinear Algebra This chapter presents concepts from multilinear algebra based on the basic properties of finite dimensional vector spaces and linear maps. The primary aim of the chapter is to give a concise introduction to alternating tensors which are necessary to define differential forms on manifolds. Many of the stated definitions and propositions can be found in Lee [1], Chaps. 11, 12 and 14. Some definitions and propositions are complemented by short and simple examples. First, in Sect. A.1 dual and bidual vector spaces are discussed. Subsequently, in Sects. A.2–A.4, tensors and alternating tensors together with operations such as the tensor and wedge product are introduced. Lastly, in Sect. A.5, the concepts which are necessary to introduce the wedge product are summarized in eight steps. A.1 The Dual Space Let V be a real vector space of finite dimension dim V = n.Let(e1,...,en) be a basis of V . Then every v ∈ V can be uniquely represented as a linear combination i v = v ei , (A.1) where summation convention over repeated indices is applied. The coefficients vi ∈ R arereferredtoascomponents of the vector v. Throughout the whole chapter, only finite dimensional real vector spaces, typically denoted by V , are treated. When not stated differently, summation convention is applied. Definition A.1 (Dual Space)Thedual space of V is the set of real-valued linear functionals ∗ V := {ω : V → R : ω linear} . (A.2) The elements of the dual space V ∗ are called linear forms on V . © Springer International Publishing Switzerland 2015 123 S.R. -
THE DIRAC OPERATOR 1. First Properties 1.1. Definition. Let X Be A
THE DIRAC OPERATOR AKHIL MATHEW 1. First properties 1.1. Definition. Let X be a Riemannian manifold. Then the tangent bundle TX is a bundle of real inner product spaces, and we can form the corresponding Clifford bundle Cliff(TX). By definition, the bundle Cliff(TX) is a vector bundle of Z=2-graded R-algebras such that the fiber Cliff(TX)x is just the Clifford algebra Cliff(TxX) (for the inner product structure on TxX). Definition 1. A Clifford module on X is a vector bundle V over X together with an action Cliff(TX) ⊗ V ! V which makes each fiber Vx into a module over the Clifford algebra Cliff(TxX). Suppose now that V is given a connection r. Definition 2. The Dirac operator D on V is defined in local coordinates by sending a section s of V to X Ds = ei:rei s; for feig a local orthonormal frame for TX and the multiplication being Clifford multiplication. It is easy to check that this definition is independent of the coordinate system. A more invariant way of defining it is to consider the composite of differential operators (1) V !r T ∗X ⊗ V ' TX ⊗ V,! Cliff(TX) ⊗ V ! V; where the first map is the connection, the second map is the isomorphism T ∗X ' TX determined by the Riemannian structure, the third map is the natural inclusion, and the fourth map is Clifford multiplication. The Dirac operator is clearly a first-order differential operator. We can work out its symbol fairly easily. The symbol1 of the connection r is ∗ Sym(r)(v; t) = iv ⊗ t; v 2 Vx; t 2 Tx X: All the other differential operators in (1) are just morphisms of vector bundles. -
Multilinear Algebra for Visual Geometry
multilinear algebra for visual geometry Alberto Ruiz http://dis.um.es/~alberto DIS, University of Murcia MVIGRO workshop Berlin, june 28th 2013 contents 1. linear spaces 2. tensor diagrams 3. Grassmann algebra 4. multiview geometry 5. tensor equations intro diagrams multiview equations linear algebra the most important computational tool: linear = solvable nonlinear problems: linearization + iteration multilinear problems are easy (' linear) visual geometry is multilinear (' solved) intro diagrams multiview equations linear spaces I objects are represented by arrays of coordinates (depending on chosen basis, with proper rules of transformation) I linear transformations are extremely simple (just multiply and add), and invertible in closed form (in least squares sense) I duality: linear transformations are also a linear space intro diagrams multiview equations linear spaces objects are linear combinations of other elements: i vector x has coordinates x in basis B = feig X i x = x ei i Einstein's convention: i x = x ei intro diagrams multiview equations change of basis 1 1 0 0 c1 c2 3 1 0 i [e1; e2] = [e1; e2]C; C = 2 2 = ej = cj ei c1 c2 1 2 x1 x1 x01 x = x1e + x2e = [e ; e ] = [e ; e ]C C−1 = [e0 ; e0 ] 1 2 1 2 x2 1 2 x2 1 2 x02 | 0{z 0 } [e1;e2] | {z } 2x013 4x025 intro diagrams multiview equations covariance and contravariance if the basis transforms according to C: 0 0 [e1; e2] = [e1; e2]C vector coordinates transform according to C−1: x01 x1 = C−1 x02 x2 in the previous example: 7 3 1 2 2 0;4 −0;2 7 = ; = 4 1 2 1 1 −0;2 0;6 4 intro -
Lecture Notes on Linear and Multilinear Algebra 2301-610
Lecture Notes on Linear and Multilinear Algebra 2301-610 Wicharn Lewkeeratiyutkul Department of Mathematics and Computer Science Faculty of Science Chulalongkorn University August 2014 Contents Preface iii 1 Vector Spaces 1 1.1 Vector Spaces and Subspaces . 2 1.2 Basis and Dimension . 10 1.3 Linear Maps . 18 1.4 Matrix Representation . 32 1.5 Change of Bases . 42 1.6 Sums and Direct Sums . 48 1.7 Quotient Spaces . 61 1.8 Dual Spaces . 65 2 Multilinear Algebra 73 2.1 Free Vector Spaces . 73 2.2 Multilinear Maps and Tensor Products . 78 2.3 Determinants . 95 2.4 Exterior Products . 101 3 Canonical Forms 107 3.1 Polynomials . 107 3.2 Diagonalization . 115 3.3 Minimal Polynomial . 128 3.4 Jordan Canonical Forms . 141 i ii CONTENTS 4 Inner Product Spaces 161 4.1 Bilinear and Sesquilinear Forms . 161 4.2 Inner Product Spaces . 167 4.3 Operators on Inner Product Spaces . 180 4.4 Spectral Theorem . 190 Bibliography 203 Preface This book grew out of the lecture notes for the course 2301-610 Linear and Multilinaer Algebra given at the Deparment of Mathematics, Faculty of Science, Chulalongkorn University that I have taught in the past 5 years. Linear Algebra is one of the most important subjects in Mathematics, with numerous applications in pure and applied sciences. A more theoretical linear algebra course will emphasize on linear maps between vector spaces, while an applied-oriented course will mainly work with matrices. Matrices have an ad- vantage of being easier to compute, while it is easier to establish the results by working with linear maps. -
Numerical Multilinear Algebra I
Numerical Multilinear Algebra I Lek-Heng Lim University of California, Berkeley January 5{7, 2009 L.-H. Lim (ICM Lecture) Numerical Multilinear Algebra I January 5{7, 2009 1 / 55 Hope Past 50 years, Numerical Linear Algebra played indispensable role in the statistical analysis of two-way data, the numerical solution of partial differential equations arising from vector fields, the numerical solution of second-order optimization methods. Next step | development of Numerical Multilinear Algebra for the statistical analysis of multi-way data, the numerical solution of partial differential equations arising from tensor fields, the numerical solution of higher-order optimization methods. L.-H. Lim (ICM Lecture) Numerical Multilinear Algebra I January 5{7, 2009 2 / 55 DARPA mathematical challenge eight One of the twenty three mathematical challenges announced at DARPA Tech 2007. Problem Beyond convex optimization: can linear algebra be replaced by algebraic geometry in a systematic way? Algebraic geometry in a slogan: polynomials are to algebraic geometry what matrices are to linear algebra. Polynomial f 2 R[x1;:::; xn] of degree d can be expressed as > > f (x) = a0 + a1 x + x A2x + A3(x; x; x) + ··· + Ad (x;:::; x): n n×n n×n×n n×···×n a0 2 R; a1 2 R ; A2 2 R ; A3 2 R ;:::; Ad 2 R . Numerical linear algebra: d = 2. Numerical multilinear algebra: d > 2. L.-H. Lim (ICM Lecture) Numerical Multilinear Algebra I January 5{7, 2009 3 / 55 Motivation Why multilinear: \Classification of mathematical problems as linear and nonlinear is like classification of the Universe as bananas and non-bananas." Nonlinear | too general. -
Tensor)Algebraic Frameworkfor Computer Graphics,Computer Vision, and Machine Learning
AMULTILINEAR (TENSOR)ALGEBRAIC FRAMEWORK FOR COMPUTER GRAPHICS,COMPUTER VISION, AND MACHINE LEARNING by M. Alex O. Vasilescu A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Graduate Department of Computer Science University of Toronto Copyright c 2009 by M. Alex O. Vasilescu A Multilinear (Tensor) Algebraic Framework for Computer Graphics, Computer Vision, and Machine Learning M. Alex O. Vasilescu Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2009 Abstract This thesis introduces a multilinear algebraic framework for computer graphics, computer vision, and machine learning, particularly for the fundamental purposes of image synthesis, analysis, and recog- nition. Natural images result from the multifactor interaction between the imaging process, the scene illumination, and the scene geometry. We assert that a principled mathematical approach to disentan- gling and explicitly representing these causal factors, which are essential to image formation, is through numerical multilinear algebra, the algebra of higher-order tensors. Our new image modeling framework is based on (i) a multilinear generalization of principal compo- nents analysis (PCA), (ii) a novel multilinear generalization of independent components analysis (ICA), and (iii) a multilinear projection for use in recognition that maps images to the multiple causal factor spaces associated with their formation. Multilinear PCA employs a tensor extension of the conventional matrix singular value decomposition -
Differential Forms As Spinors Annales De L’I
ANNALES DE L’I. H. P., SECTION A WOLFGANG GRAF Differential forms as spinors Annales de l’I. H. P., section A, tome 29, no 1 (1978), p. 85-109 <http://www.numdam.org/item?id=AIHPA_1978__29_1_85_0> © Gauthier-Villars, 1978, tous droits réservés. L’accès aux archives de la revue « Annales de l’I. H. P., section A » implique l’accord avec les conditions générales d’utilisation (http://www.numdam. org/conditions). Toute utilisation commerciale ou impression systématique est constitutive d’une infraction pénale. Toute copie ou impression de ce fichier doit contenir la présente mention de copyright. Article numérisé dans le cadre du programme Numérisation de documents anciens mathématiques http://www.numdam.org/ Ann. Inst. Henri Poincare, Section A : Vol. XXIX. n° L 1978. p. 85 Physique’ théorique.’ Differential forms as spinors Wolfgang GRAF Fachbereich Physik der Universitat, D-7750Konstanz. Germany ABSTRACT. 2014 An alternative notion of spinor fields for spin 1/2 on a pseudoriemannian manifold is proposed. Use is made of an algebra which allows the interpretation of spinors as elements of a global minimal Clifford- ideal of differential forms. The minimal coupling to an electromagnetic field is introduced by means of an « U(1 )-gauging ». Although local Lorentz transformations play only a secondary role and the usual two-valuedness is completely absent, all results of Dirac’s equation in flat space-time with electromagnetic coupling can be regained. 1. INTRODUCTION It is well known that in a riemannian manifold the laplacian D := - (d~ + ~d) operating on differential forms admits as « square root » the first-order operator ~ 2014 ~ ~ being the exterior derivative and e) the generalized divergence. -
Principal Bundles, Vector Bundles and Connections
Appendix A Principal Bundles, Vector Bundles and Connections Abstract The appendix defines fiber bundles, principal bundles and their associate vector bundles, recall the definitions of frame bundles, the orthonormal frame bun- dle, jet bundles, product bundles and the Whitney sums of bundles. Next, equivalent definitions of connections in principal bundles and in their associate vector bundles are presented and it is shown how these concepts are related to the concept of a covariant derivative in the base manifold of the bundle. Also, the concept of exterior covariant derivatives (crucial for the formulation of gauge theories) and the meaning of a curvature and torsion of a linear connection in a manifold is recalled. The concept of covariant derivative in vector bundles is also analyzed in details in a way which, in particular, is necessary for the presentation of the theory in Chap. 12. Propositions are in general presented without proofs, which can be found, e.g., in Choquet-Bruhat et al. (Analysis, Manifolds and Physics. North-Holland, Amsterdam, 1982), Frankel (The Geometry of Physics. Cambridge University Press, Cambridge, 1997), Kobayashi and Nomizu (Foundations of Differential Geometry. Interscience Publishers, New York, 1963), Naber (Topology, Geometry and Gauge Fields. Interactions. Applied Mathematical Sciences. Springer, New York, 2000), Nash and Sen (Topology and Geometry for Physicists. Academic, London, 1983), Nicolescu (Notes on Seiberg-Witten Theory. Graduate Studies in Mathematics. American Mathematical Society, Providence, RI, 2000), Osborn (Vector Bundles. Academic, New York, 1982), and Palais (The Geometrization of Physics. Lecture Notes from a Course at the National Tsing Hua University, Hsinchu, 1981). A.1 Fiber Bundles Definition A.1 A fiber bundle over M with Lie group G will be denoted by E D .E; M;;G; F/.