Tensor Algebra, Linear Algebra, Matrix Algebra, Multilinear Algebra
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Arxiv:2009.07259V1 [Math.AP] 15 Sep 2020
A GEOMETRIC TRAPPING APPROACH TO GLOBAL REGULARITY FOR 2D NAVIER-STOKES ON MANIFOLDS AYNUR BULUT AND KHANG MANH HUYNH Abstract. In this paper, we use frequency decomposition techniques to give a direct proof of global existence and regularity for the Navier-Stokes equations on two-dimensional Riemannian manifolds without boundary. Our techniques are inspired by an approach of Mattingly and Sinai [15] which was developed in the context of periodic boundary conditions on a flat background, and which is based on a maximum principle for Fourier coefficients. The extension to general manifolds requires several new ideas, connected to the less favorable spectral localization properties in our setting. Our argu- ments make use of frequency projection operators, multilinear estimates that originated in the study of the non-linear Schr¨odingerequation, and ideas from microlocal analysis. 1. Introduction Let (M; g) be a closed, oriented, connected, compact smooth two-dimensional Riemannian manifold, and let X(M) denote the space of smooth vector fields on M. We consider the incompressible Navier-Stokes equations on M, with viscosity coefficient ν > 0, @ U + div (U ⊗ U) − ν∆ U = − grad p in M t M ; (1) div U = 0 in M with initial data U0 2 X(M); where I ⊂ R is an open interval, and where U : I ! X(M) and p : I × M ! R represent the velocity and pressure of the fluid, respectively. Here, the operator ∆M is any choice of Laplacian defined on vector fields on M, discussed below. arXiv:2009.07259v1 [math.AP] 15 Sep 2020 The theory of two-dimensional fluid flows on flat spaces is well-developed, and a variety of global regularity results are well-known. -
Lecture Notes in Mathematics
Lecture Notes in Mathematics Edited by A. Dold and B. Eckmann Subseries: Mathematisches Institut der Universit~it und Max-Planck-lnstitut for Mathematik, Bonn - voL 5 Adviser: E Hirzebruch 1111 Arbeitstagung Bonn 1984 Proceedings of the meeting held by the Max-Planck-lnstitut fur Mathematik, Bonn June 15-22, 1984 Edited by E Hirzebruch, J. Schwermer and S. Suter I IIII Springer-Verlag Berlin Heidelberg New York Tokyo Herausgeber Friedrich Hirzebruch Joachim Schwermer Silke Suter Max-Planck-lnstitut fLir Mathematik Gottfried-Claren-Str. 26 5300 Bonn 3, Federal Republic of Germany AMS-Subject Classification (1980): 10D15, 10D21, 10F99, 12D30, 14H10, 14H40, 14K22, 17B65, 20G35, 22E47, 22E65, 32G15, 53C20, 57 N13, 58F19 ISBN 3-54045195-8 Springer-Verlag Berlin Heidelberg New York Tokyo ISBN 0-387-15195-8 Springer-Verlag New York Heidelberg Berlin Tokyo CIP-Kurztitelaufnahme der Deutschen Bibliothek. Mathematische Arbeitstagung <25. 1984. Bonn>: Arbeitstagung Bonn: 1984; proceedings of the meeting, held in Bonn, June 15-22, 1984 / [25. Math. Arbeitstagung]. Ed. by E Hirzebruch ... - Berlin; Heidelberg; NewYork; Tokyo: Springer, 1985. (Lecture notes in mathematics; Vol. 1t11: Subseries: Mathematisches I nstitut der U niversit~it und Max-Planck-lnstitut for Mathematik Bonn; VoL 5) ISBN 3-540-t5195-8 (Berlin...) ISBN 0-387q5195-8 (NewYork ...) NE: Hirzebruch, Friedrich [Hrsg.]; Lecture notes in mathematics / Subseries: Mathematischee Institut der UniversitAt und Max-Planck-lnstitut fur Mathematik Bonn; HST This work ts subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically those of translation, reprinting, re~use of illustrations, broadcasting, reproduction by photocopying machine or similar means, and storage in data banks. -
Arxiv:0911.0334V2 [Gr-Qc] 4 Jul 2020
Classical Physics: Spacetime and Fields Nikodem Poplawski Department of Mathematics and Physics, University of New Haven, CT, USA Preface We present a self-contained introduction to the classical theory of spacetime and fields. This expo- sition is based on the most general principles: the principle of general covariance (relativity) and the principle of least action. The order of the exposition is: 1. Spacetime (principle of general covariance and tensors, affine connection, curvature, metric, tetrad and spin connection, Lorentz group, spinors); 2. Fields (principle of least action, action for gravitational field, matter, symmetries and conservation laws, gravitational field equations, spinor fields, electromagnetic field, action for particles). In this order, a particle is a special case of a field existing in spacetime, and classical mechanics can be derived from field theory. I dedicate this book to my Parents: Bo_zennaPop lawska and Janusz Pop lawski. I am also grateful to Chris Cox for inspiring this book. The Laws of Physics are simple, beautiful, and universal. arXiv:0911.0334v2 [gr-qc] 4 Jul 2020 1 Contents 1 Spacetime 5 1.1 Principle of general covariance and tensors . 5 1.1.1 Vectors . 5 1.1.2 Tensors . 6 1.1.3 Densities . 7 1.1.4 Contraction . 7 1.1.5 Kronecker and Levi-Civita symbols . 8 1.1.6 Dual densities . 8 1.1.7 Covariant integrals . 9 1.1.8 Antisymmetric derivatives . 9 1.2 Affine connection . 10 1.2.1 Covariant differentiation of tensors . 10 1.2.2 Parallel transport . 11 1.2.3 Torsion tensor . 11 1.2.4 Covariant differentiation of densities . -
Lecture 13: Simple Linear Regression in Matrix Format
11:55 Wednesday 14th October, 2015 See updates and corrections at http://www.stat.cmu.edu/~cshalizi/mreg/ Lecture 13: Simple Linear Regression in Matrix Format 36-401, Section B, Fall 2015 13 October 2015 Contents 1 Least Squares in Matrix Form 2 1.1 The Basic Matrices . .2 1.2 Mean Squared Error . .3 1.3 Minimizing the MSE . .4 2 Fitted Values and Residuals 5 2.1 Residuals . .7 2.2 Expectations and Covariances . .7 3 Sampling Distribution of Estimators 8 4 Derivatives with Respect to Vectors 9 4.1 Second Derivatives . 11 4.2 Maxima and Minima . 11 5 Expectations and Variances with Vectors and Matrices 12 6 Further Reading 13 1 2 So far, we have not used any notions, or notation, that goes beyond basic algebra and calculus (and probability). This has forced us to do a fair amount of book-keeping, as it were by hand. This is just about tolerable for the simple linear model, with one predictor variable. It will get intolerable if we have multiple predictor variables. Fortunately, a little application of linear algebra will let us abstract away from a lot of the book-keeping details, and make multiple linear regression hardly more complicated than the simple version1. These notes will not remind you of how matrix algebra works. However, they will review some results about calculus with matrices, and about expectations and variances with vectors and matrices. Throughout, bold-faced letters will denote matrices, as a as opposed to a scalar a. 1 Least Squares in Matrix Form Our data consists of n paired observations of the predictor variable X and the response variable Y , i.e., (x1; y1);::: (xn; yn). -
1.2 Topological Tensor Calculus
PH211 Physical Mathematics Fall 2019 1.2 Topological tensor calculus 1.2.1 Tensor fields Finite displacements in Euclidean space can be represented by arrows and have a natural vector space structure, but finite displacements in more general curved spaces, such as on the surface of a sphere, do not. However, an infinitesimal neighborhood of a point in a smooth curved space1 looks like an infinitesimal neighborhood of Euclidean space, and infinitesimal displacements dx~ retain the vector space structure of displacements in Euclidean space. An infinitesimal neighborhood of a point can be infinitely rescaled to generate a finite vector space, called the tangent space, at the point. A vector lives in the tangent space of a point. Note that vectors do not stretch from one point to vector tangent space at p p space Figure 1.2.1: A vector in the tangent space of a point. another, and vectors at different points live in different tangent spaces and so cannot be added. For example, rescaling the infinitesimal displacement dx~ by dividing it by the in- finitesimal scalar dt gives the velocity dx~ ~v = (1.2.1) dt which is a vector. Similarly, we can picture the covector rφ as the infinitesimal contours of φ in a neighborhood of a point, infinitely rescaled to generate a finite covector in the point's cotangent space. More generally, infinitely rescaling the neighborhood of a point generates the tensor space and its algebra at the point. The tensor space contains the tangent and cotangent spaces as a vector subspaces. A tensor field is something that takes tensor values at every point in a space. -
Introducing the Game Design Matrix: a Step-By-Step Process for Creating Serious Games
Air Force Institute of Technology AFIT Scholar Theses and Dissertations Student Graduate Works 3-2020 Introducing the Game Design Matrix: A Step-by-Step Process for Creating Serious Games Aaron J. Pendleton Follow this and additional works at: https://scholar.afit.edu/etd Part of the Educational Assessment, Evaluation, and Research Commons, Game Design Commons, and the Instructional Media Design Commons Recommended Citation Pendleton, Aaron J., "Introducing the Game Design Matrix: A Step-by-Step Process for Creating Serious Games" (2020). Theses and Dissertations. 4347. https://scholar.afit.edu/etd/4347 This Thesis is brought to you for free and open access by the Student Graduate Works at AFIT Scholar. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of AFIT Scholar. For more information, please contact [email protected]. INTRODUCING THE GAME DESIGN MATRIX: A STEP-BY-STEP PROCESS FOR CREATING SERIOUS GAMES THESIS Aaron J. Pendleton, Captain, USAF AFIT-ENG-MS-20-M-054 DEPARTMENT OF THE AIR FORCE AIR UNIVERSITY AIR FORCE INSTITUTE OF TECHNOLOGY Wright-Patterson Air Force Base, Ohio DISTRIBUTION STATEMENT A APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED. The views expressed in this document are those of the author and do not reflect the official policy or position of the United States Air Force, the United States Department of Defense or the United States Government. This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. AFIT-ENG-MS-20-M-054 INTRODUCING THE GAME DESIGN MATRIX: A STEP-BY-STEP PROCESS FOR CREATING LEARNING OBJECTIVE BASED SERIOUS GAMES THESIS Presented to the Faculty Department of Electrical and Computer Engineering Graduate School of Engineering and Management Air Force Institute of Technology Air University Air Education and Training Command in Partial Fulfillment of the Requirements for the Degree of Master of Science in Cyberspace Operations Aaron J. -
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]. -
Stat 5102 Notes: Regression
Stat 5102 Notes: Regression Charles J. Geyer April 27, 2007 In these notes we do not use the “upper case letter means random, lower case letter means nonrandom” convention. Lower case normal weight letters (like x and β) indicate scalars (real variables). Lowercase bold weight letters (like x and β) indicate vectors. Upper case bold weight letters (like X) indicate matrices. 1 The Model The general linear model has the form p X yi = βjxij + ei (1.1) j=1 where i indexes individuals and j indexes different predictor variables. Ex- plicit use of (1.1) makes theory impossibly messy. We rewrite it as a vector equation y = Xβ + e, (1.2) where y is a vector whose components are yi, where X is a matrix whose components are xij, where β is a vector whose components are βj, and where e is a vector whose components are ei. Note that y and e have dimension n, but β has dimension p. The matrix X is called the design matrix or model matrix and has dimension n × p. As always in regression theory, we treat the predictor variables as non- random. So X is a nonrandom matrix, β is a nonrandom vector of unknown parameters. The only random quantities in (1.2) are e and y. As always in regression theory the errors ei are independent and identi- cally distributed mean zero normal. This is written as a vector equation e ∼ Normal(0, σ2I), where σ2 is another unknown parameter (the error variance) and I is the identity matrix. This implies y ∼ Normal(µ, σ2I), 1 where µ = Xβ. -
On the Representation of Symmetric and Antisymmetric Tensors
Max-Planck-Institut fur¨ Mathematik in den Naturwissenschaften Leipzig On the Representation of Symmetric and Antisymmetric Tensors (revised version: April 2017) by Wolfgang Hackbusch Preprint no.: 72 2016 On the Representation of Symmetric and Antisymmetric Tensors Wolfgang Hackbusch Max-Planck-Institut Mathematik in den Naturwissenschaften Inselstr. 22–26, D-04103 Leipzig, Germany [email protected] Abstract Various tensor formats are used for the data-sparse representation of large-scale tensors. Here we investigate how symmetric or antiymmetric tensors can be represented. The analysis leads to several open questions. Mathematics Subject Classi…cation: 15A69, 65F99 Keywords: tensor representation, symmetric tensors, antisymmetric tensors, hierarchical tensor format 1 Introduction We consider tensor spaces of huge dimension exceeding the capacity of computers. Therefore the numerical treatment of such tensors requires a special representation technique which characterises the tensor by data of moderate size. These representations (or formats) should also support operations with tensors. Examples of operations are the addition, the scalar product, the componentwise product (Hadamard product), and the matrix-vector multiplication. In the latter case, the ‘matrix’belongs to the tensor space of Kronecker matrices, while the ‘vector’is a usual tensor. In certain applications the subspaces of symmetric or antisymmetric tensors are of interest. For instance, fermionic states in quantum chemistry require antisymmetry, whereas bosonic systems are described by symmetric tensors. The appropriate representation of (anti)symmetric tensors is seldom discussed in the literature. Of course, all formats are able to represent these tensors since they are particular examples of general tensors. However, the special (anti)symmetric format should exclusively produce (anti)symmetric tensors. -
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.. -
Discover Linear Algebra Incomplete Preliminary Draft
Discover Linear Algebra Incomplete Preliminary Draft Date: November 28, 2017 L´aszl´oBabai in collaboration with Noah Halford All rights reserved. Approved for instructional use only. Commercial distribution prohibited. c 2016 L´aszl´oBabai. Last updated: November 10, 2016 Preface TO BE WRITTEN. Babai: Discover Linear Algebra. ii This chapter last updated August 21, 2016 c 2016 L´aszl´oBabai. Contents Notation ix I Matrix Theory 1 Introduction to Part I 2 1 (F, R) Column Vectors 3 1.1 (F) Column vector basics . 3 1.1.1 The domain of scalars . 3 1.2 (F) Subspaces and span . 6 1.3 (F) Linear independence and the First Miracle of Linear Algebra . 8 1.4 (F) Dot product . 12 1.5 (R) Dot product over R ................................. 14 1.6 (F) Additional exercises . 14 2 (F) Matrices 15 2.1 Matrix basics . 15 2.2 Matrix multiplication . 18 2.3 Arithmetic of diagonal and triangular matrices . 22 2.4 Permutation Matrices . 24 2.5 Additional exercises . 26 3 (F) Matrix Rank 28 3.1 Column and row rank . 28 iii iv CONTENTS 3.2 Elementary operations and Gaussian elimination . 29 3.3 Invariance of column and row rank, the Second Miracle of Linear Algebra . 31 3.4 Matrix rank and invertibility . 33 3.5 Codimension (optional) . 34 3.6 Additional exercises . 35 4 (F) Theory of Systems of Linear Equations I: Qualitative Theory 38 4.1 Homogeneous systems of linear equations . 38 4.2 General systems of linear equations . 40 5 (F, R) Affine and Convex Combinations (optional) 42 5.1 (F) Affine combinations .