On the Generalized Geometry Origin of Noncommutative Gauge Theory
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Supergravity and Its Legacy Prelude and the Play
Supergravity and its Legacy Prelude and the Play Sergio FERRARA (CERN – LNF INFN) Celebrating Supegravity at 40 CERN, June 24 2016 S. Ferrara - CERN, 2016 1 Supergravity as carved on the Iconic Wall at the «Simons Center for Geometry and Physics», Stony Brook S. Ferrara - CERN, 2016 2 Prelude S. Ferrara - CERN, 2016 3 In the early 1970s I was a staff member at the Frascati National Laboratories of CNEN (then the National Nuclear Energy Agency), and with my colleagues Aurelio Grillo and Giorgio Parisi we were investigating, under the leadership of Raoul Gatto (later Professor at the University of Geneva) the consequences of the application of “Conformal Invariance” to Quantum Field Theory (QFT), stimulated by the ongoing Experiments at SLAC where an unexpected Bjorken Scaling was observed in inclusive electron- proton Cross sections, which was suggesting a larger space-time symmetry in processes dominated by short distance physics. In parallel with Alexander Polyakov, at the time in the Soviet Union, we formulated in those days Conformal invariant Operator Product Expansions (OPE) and proposed the “Conformal Bootstrap” as a non-perturbative approach to QFT. S. Ferrara - CERN, 2016 4 Conformal Invariance, OPEs and Conformal Bootstrap has become again a fashionable subject in recent times, because of the introduction of efficient new methods to solve the “Bootstrap Equations” (Riccardo Rattazzi, Slava Rychkov, Erik Tonni, Alessandro Vichi), and mostly because of their role in the AdS/CFT correspondence. The latter, pioneered by Juan Maldacena, Edward Witten, Steve Gubser, Igor Klebanov and Polyakov, can be regarded, to some extent, as one of the great legacies of higher dimensional Supergravity. -
Geometric Polarimetry − Part I: Spinors and Wave States
1 Geometric Polarimetry Part I: Spinors and − Wave States David Bebbington, Laura Carrea, and Ernst Krogager, Member, IEEE Abstract A new approach to polarization algebra is introduced. It exploits the geometric properties of spinors in order to represent wave states consistently in arbitrary directions in three dimensional space. In this first expository paper of an intended series the basic derivation of the spinorial wave state is seen to be geometrically related to the electromagnetic field tensor in the spatio-temporal Fourier domain. Extracting the polarization state from the electromagnetic field requires the introduction of a new element, which enters linearly into the defining relation. We call this element the phase flag and it is this that keeps track of the polarization reference when the coordinate system is changed and provides a phase origin for both wave components. In this way we are able to identify the sphere of three dimensional unit wave vectors with the Poincar´esphere. Index Terms state of polarization, geometry, covariant and contravariant spinors and tensors, bivectors, phase flag, Poincar´esphere. I. INTRODUCTION arXiv:0804.0745v1 [physics.optics] 4 Apr 2008 The development of applications in radar polarimetry has been vigorous over the past decade, and is anticipated to continue with ambitious new spaceborne remote sensing missions (for example TerraSAR-X [1] and TanDEM-X [2]). As technical capabilities increase, and new application areas open up, innovations in data analysis often result. Because polarization data are This work was supported by the Marie Curie Research Training Network AMPER (Contract number HPRN-CT-2002-00205). D. Bebbington and L. -
A Fast Method for Computing the Inverse of Symmetric Block Arrowhead Matrices
Appl. Math. Inf. Sci. 9, No. 2L, 319-324 (2015) 319 Applied Mathematics & Information Sciences An International Journal http://dx.doi.org/10.12785/amis/092L06 A Fast Method for Computing the Inverse of Symmetric Block Arrowhead Matrices Waldemar Hołubowski1, Dariusz Kurzyk1,∗ and Tomasz Trawi´nski2 1 Institute of Mathematics, Silesian University of Technology, Kaszubska 23, Gliwice 44–100, Poland 2 Mechatronics Division, Silesian University of Technology, Akademicka 10a, Gliwice 44–100, Poland Received: 6 Jul. 2014, Revised: 7 Oct. 2014, Accepted: 8 Oct. 2014 Published online: 1 Apr. 2015 Abstract: We propose an effective method to find the inverse of symmetric block arrowhead matrices which often appear in areas of applied science and engineering such as head-positioning systems of hard disk drives or kinematic chains of industrial robots. Block arrowhead matrices can be considered as generalisation of arrowhead matrices occurring in physical problems and engineering. The proposed method is based on LDLT decomposition and we show that the inversion of the large block arrowhead matrices can be more effective when one uses our method. Numerical results are presented in the examples. Keywords: matrix inversion, block arrowhead matrices, LDLT decomposition, mechatronic systems 1 Introduction thermal and many others. Considered subsystems are highly differentiated, hence formulation of uniform and simple mathematical model describing their static and A square matrix which has entries equal zero except for dynamic states becomes problematic. The process of its main diagonal, a one row and a column, is called the preparing a proper mathematical model is often based on arrowhead matrix. Wide area of applications causes that the formulation of the equations associated with this type of matrices is popular subject of research related Lagrangian formalism [9], which is a convenient way to with mathematics, physics or engineering, such as describe the equations of mechanical, electromechanical computing spectral decomposition [1], solving inverse and other components. -
Structured Factorizations in Scalar Product Spaces
STRUCTURED FACTORIZATIONS IN SCALAR PRODUCT SPACES D. STEVEN MACKEY¤, NILOUFER MACKEYy , AND FRANC»OISE TISSEURz Abstract. Let A belong to an automorphism group, Lie algebra or Jordan algebra of a scalar product. When A is factored, to what extent do the factors inherit structure from A? We answer this question for the principal matrix square root, the matrix sign decomposition, and the polar decomposition. For general A, we give a simple derivation and characterization of a particular generalized polar decomposition, and we relate it to other such decompositions in the literature. Finally, we study eigendecompositions and structured singular value decompositions, considering in particular the structure in eigenvalues, eigenvectors and singular values that persists across a wide range of scalar products. A key feature of our analysis is the identi¯cation of two particular classes of scalar products, termed unitary and orthosymmetric, which serve to unify assumptions for the existence of structured factorizations. A variety of di®erent characterizations of these scalar product classes is given. Key words. automorphism group, Lie group, Lie algebra, Jordan algebra, bilinear form, sesquilinear form, scalar product, inde¯nite inner product, orthosymmetric, adjoint, factorization, symplectic, Hamiltonian, pseudo-orthogonal, polar decomposition, matrix sign function, matrix square root, generalized polar decomposition, eigenvalues, eigenvectors, singular values, structure preservation. AMS subject classi¯cations. 15A18, 15A21, 15A23, 15A57, -
Linear Algebra - Part II Projection, Eigendecomposition, SVD
Linear Algebra - Part II Projection, Eigendecomposition, SVD (Adapted from Punit Shah's slides) 2019 Linear Algebra, Part II 2019 1 / 22 Brief Review from Part 1 Matrix Multiplication is a linear tranformation. Symmetric Matrix: A = AT Orthogonal Matrix: AT A = AAT = I and A−1 = AT L2 Norm: s X 2 jjxjj2 = xi i Linear Algebra, Part II 2019 2 / 22 Angle Between Vectors Dot product of two vectors can be written in terms of their L2 norms and the angle θ between them. T a b = jjajj2jjbjj2 cos(θ) Linear Algebra, Part II 2019 3 / 22 Cosine Similarity Cosine between two vectors is a measure of their similarity: a · b cos(θ) = jjajj jjbjj Orthogonal Vectors: Two vectors a and b are orthogonal to each other if a · b = 0. Linear Algebra, Part II 2019 4 / 22 Vector Projection ^ b Given two vectors a and b, let b = jjbjj be the unit vector in the direction of b. ^ Then a1 = a1 · b is the orthogonal projection of a onto a straight line parallel to b, where b a = jjajj cos(θ) = a · b^ = a · 1 jjbjj Image taken from wikipedia. Linear Algebra, Part II 2019 5 / 22 Diagonal Matrix Diagonal matrix has mostly zeros with non-zero entries only in the diagonal, e.g. identity matrix. A square diagonal matrix with diagonal elements given by entries of vector v is denoted: diag(v) Multiplying vector x by a diagonal matrix is efficient: diag(v)x = v x is the entrywise product. Inverting a square diagonal matrix is efficient: 1 1 diag(v)−1 = diag [ ;:::; ]T v1 vn Linear Algebra, Part II 2019 6 / 22 Determinant Determinant of a square matrix is a mapping to a scalar. -
Unit 6: Matrix Decomposition
Unit 6: Matrix decomposition Juan Luis Melero and Eduardo Eyras October 2018 1 Contents 1 Matrix decomposition3 1.1 Definitions.............................3 2 Singular Value Decomposition6 3 Exercises 13 4 R practical 15 4.1 SVD................................ 15 2 1 Matrix decomposition 1.1 Definitions Matrix decomposition consists in transforming a matrix into a product of other matrices. Matrix diagonalization is a special case of decomposition and is also called diagonal (eigen) decomposition of a matrix. Definition: there is a diagonal decomposition of a square matrix A if we can write A = UDU −1 Where: • D is a diagonal matrix • The diagonal elements of D are the eigenvalues of A • Vector columns of U are the eigenvectors of A. For symmetric matrices there is a special decomposition: Definition: given a symmetric matrix A (i.e. AT = A), there is a unique decomposition of the form A = UDU T Where • U is an orthonormal matrix • Vector columns of U are the unit-norm orthogonal eigenvectors of A • D is a diagonal matrix formed by the eigenvalues of A This special decomposition is known as spectral decomposition. Definition: An orthonormal matrix is a square matrix whose columns and row vectors are orthogonal unit vectors (orthonormal vectors). Orthonormal matrices have the property that their transposed matrix is the inverse matrix. 3 Proposition: if a matrix Q is orthonormal, then QT = Q−1 Proof: consider the row vectors in a matrix Q: 0 1 u1 B . C T u j ::: ju Q = @ . A and Q = 1 n un 0 1 0 1 0 1 u1 hu1; u1i ::: hu1; uni 1 ::: 0 T B . -
Born-Infeld Action, Supersymmetry and String Theory
Imperial/TP/98-99/67 hep-th/9908105 Born-Infeld action, supersymmetry and string theory ⋆ A.A. Tseytlin † Theoretical Physics Group, Blackett Laboratory, Imperial College, London SW7 2BZ, U.K. Abstract We review and elaborate on some aspects of Born-Infeld action and its supersymmetric generalizations in connection with string theory. Contents: BI action from string theory; some properties of bosonic D = 4 BI action; = 1 and = 2 supersymmetric BI actions with manifest linear D = 4 supersymmetry; four-derivativeN N terms in = 4 supersymmetric BI action; BI actions with ‘deformed’ supersymmetry from D-braneN actions; non-abelian generalization of BI action; derivative corrections to BI action in open superstring theory. arXiv:hep-th/9908105v5 11 Dec 1999 To appear in the Yuri Golfand memorial volume, ed. M. Shifman, World Scientific (2000) August 1999 ⋆ e-mail address: [email protected] † Also at Lebedev Physics Institute, Moscow. 1. Introduction It is a pleasure for me to contribute to Yuri Golfand’s memorial volume. I met Yuri several times during his occasional visits of Lebedev Institute in the 80’s. Two of our discussions in 1985 I remember quite vividly. Golfand found appealing the interpretation of string theory as a theory of ‘quantized coordinates’, viewing it as a generalization of some old ideas of noncommuting coordinates. In what should be an early spring of 1985 he read our JETP Letter [1] which was a brief Russian version of our approach with Fradkin [2] to string theory effective action based on representation of generating functional for string amplitudes as Polyakov string path integral with a covariant 2-d sigma model in the exponent. -
LU Decomposition - Wikipedia, the Free Encyclopedia
LU decomposition - Wikipedia, the free encyclopedia http://en.wikipedia.org/wiki/LU_decomposition From Wikipedia, the free encyclopedia In linear algebra, LU decomposition (also called LU factorization) is a matrix decomposition which writes a matrix as the product of a lower triangular matrix and an upper triangular matrix. The product sometimes includes a permutation matrix as well. This decomposition is used in numerical analysis to solve systems of linear equations or calculate the determinant of a matrix. LU decomposition can be viewed as a matrix form of Gaussian elimination. LU decomposition was introduced by mathematician Alan Turing [1] 1 Definitions 2 Existence and uniqueness 3 Positive definite matrices 4 Explicit formulation 5 Algorithms 5.1 Doolittle algorithm 5.2 Crout and LUP algorithms 5.3 Theoretical complexity 6 Small example 7 Sparse matrix decomposition 8 Applications 8.1 Solving linear equations 8.2 Inverse matrix 8.3 Determinant 9 See also 10 References 11 External links Let A be a square matrix. An LU decomposition is a decomposition of the form where L and U are lower and upper triangular matrices (of the same size), respectively. This means that L has only zeros above the diagonal and U has only zeros below the diagonal. For a matrix, this becomes: An LDU decomposition is a decomposition of the form where D is a diagonal matrix and L and U are unit triangular matrices, meaning that all the entries on the diagonals of L and U LDU decomposition of a Walsh matrix 1 of 7 1/11/2012 5:26 PM LU decomposition - Wikipedia, the free encyclopedia http://en.wikipedia.org/wiki/LU_decomposition are one. -
Arxiv:1105.1185V1 [Math.NA] 5 May 2011 Ento 2.2
ITERATIVE METHODS FOR COMPUTING EIGENVALUES AND EIGENVECTORS MAYSUM PANJU Abstract. We examine some numerical iterative methods for computing the eigenvalues and eigenvectors of real matrices. The five methods examined here range from the simple power iteration method to the more complicated QR iteration method. The derivations, procedure, and advantages of each method are briefly discussed. 1. Introduction Eigenvalues and eigenvectors play an important part in the applications of linear algebra. The naive method of finding the eigenvalues of a matrix involves finding the roots of the characteristic polynomial of the matrix. In industrial sized matrices, however, this method is not feasible, and the eigenvalues must be obtained by other means. Fortunately, there exist several other techniques for finding eigenvalues and eigenvectors of a matrix, some of which fall under the realm of iterative methods. These methods work by repeatedly refining approximations to the eigenvectors or eigenvalues, and can be terminated whenever the approximations reach a suitable degree of accuracy. Iterative methods form the basis of much of modern day eigenvalue computation. In this paper, we outline five such iterative methods, and summarize their derivations, procedures, and advantages. The methods to be examined are the power iteration method, the shifted inverse iteration method, the Rayleigh quotient method, the simultaneous iteration method, and the QR method. This paper is meant to be a survey over existing algorithms for the eigenvalue computation problem. Section 2 of this paper provides a brief review of some of the linear algebra background required to understand the concepts that are discussed. In section 3, the iterative methods are each presented, in order of complexity, and are studied in brief detail. -
Dyson, Feynman, Schwinger, and Tomonaga
QED and the Men Who Made It: Dyson, Feynman, Schwinger, and Tomonaga Silvan S. Schweber Princeton University Press Princeton, New Jersey Copyright ©1994 by Princeton University Press Published by Princeton University Press, 41 William Street, Princeton, New Jersey 08540 In the United Kingdom: Princeton University Press, Chichester, West Sussex All Rights Reserved Library of Congress Cataloging-in-Publication Data Schweber, S. S. (Silvan S.) QED and the men who made it : Dyson, Feynman, Schwinger, and Tomonaga / Silvan S. Schweber. p. cm. - (Princeton series in physics) Includes bibliographical references and index. ISBN 0-691-03685-3; (pbk) 0-691-03327-7 1. Quantum electrodynamics—History. 2. Physicists—Biography. I. Title. II. Series. QC680.S34 1944 93-33550 537.6'7'09—<lc20 This book has been composed in TIMES ROMAN and KABEL Princeton University Press books are printed on acid-free paper, and meet the guidelines for permanence and durability of the Committee on Production Guidelines for Book Longevity of the Council on Library Resources Printed in the United States of America 10 9 8 7 ISBN-13: 978-0-691-03327-3 ISBN-10: 0-691-03327-7 QED and the Men Who Made It PRINCETON SERIES IN PHYSICS Edited by Philip W. Anderson, Arthur S. Wightman, and Sam B. Treiman (published since 1976) Studies in Mathematical Physics: Essays in Honor of Valentine Bargmann edited by Elliott H. Lieb, B. Simon, and A. S. Wightman Convexity in the Theory of Lattice Gasses by Robert B. Israel Works on the Foundations of Statistical Physics by N. S. Krylov Surprises in Theoretical Physics by Rudolf Peierls The Large-Scale Structure of the Universe by P. -
Householder Matrix H( W )= E ( W , W ,2) = I − 2 Wwh for Wh W =1
Outline Elementary Matrix Full-rank Decomposition Triangular Matrix Decomposition QR Decomposition Schur Theorem and Normal Matrix 1 Elementary Matrix Elementary Matrix E(,, u vσσ )= I − uvH Identity matrix plus a rank-one matrix Properties on the Elementary Matrix Determinant Euv(,,σσ )= 1 − vH u σ inverse matrix Euv(,,σ )−1 = Euv (,, ) σ vuH −1 for any two nonzero vectors a and b, their exists an elementary matrix such that Euv(,,σ ) a= b 2 Elementary Matrix Determinant Euv(,,σσ )= 1 − vH u uH uuHH u H E(,, u v σσ )=−=−uvHH u σ v , u uu u H uuHH −=−uσσ v1, vu uu uH E(,,) u vσ = u HH if u u = 0. 0 00 σ Inverse Matrix Euv(,,σ )−1 = Euv (,, ) σ vuH −1 brute-force proof For any two nonzero vectors a and b, their exists an elementary matrix such that E(u,v,σ )a = b 3 Elementary Matrix For any two nonzero vectors a and b, their exists an elementary matrix such that Euv(,,σ ) a= b E(,, u vσσ ) a=−= a uvH a b ⇒=−σuvH a a b ⇒=−u a b,1σ vaH = 4 Elementary Matrix Householder Matrix H( w )= E ( w , w ,2) = I − 2 wwH for wH w =1 Properties on the Householder Matrix determinant det(Hw ( ))=−=− 1 2 wwH 1 inverse Hw()−1 = Hw ()H = Hw () 5 Full-Rank Decomposition Full-rank Decomposition Theorem: For m*n matrix A and det(A) = r, there exists m*r full- rank matrix B and r*n full-rank matrix C, such that A = BC. -
Electronic Reprint Wedge Reversion Antisymmetry and 41 Types of Physical Quantities in Arbitrary Dimensions Iucr Journals
electronic reprint ISSN: 2053-2733 journals.iucr.org/a Wedge reversion antisymmetry and 41 types of physical quantities in arbitrary dimensions Venkatraman Gopalan Acta Cryst. (2020). A76, 318–327 IUCr Journals CRYSTALLOGRAPHY JOURNALS ONLINE Copyright c International Union of Crystallography Author(s) of this article may load this reprint on their own web site or institutional repository provided that this cover page is retained. Republication of this article or its storage in electronic databases other than as specified above is not permitted without prior permission in writing from the IUCr. For further information see https://journals.iucr.org/services/authorrights.html Acta Cryst. (2020). A76, 318–327 Venkatraman Gopalan · Wedge reversion antisymmetry research papers Wedge reversion antisymmetry and 41 types of physical quantities in arbitrary dimensions ISSN 2053-2733 Venkatraman Gopalan* Department of Materials Science and Engineering, Department of Physics, and Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA 16802, USA. *Correspondence e-mail: [email protected] Received 18 October 2019 Accepted 16 February 2020 It is shown that there are 41 types of multivectors representing physical quantities in non-relativistic physics in arbitrary dimensions within the formalism of Clifford algebra. The classification is based on the action of three Edited by S. J. L. Billinge, Columbia University, symmetry operations on a general multivector: spatial inversion, 1, time- USA reversal, 10, and a third that is introduced here, namely wedge reversion, 1†.Itis shown that the traits of ‘axiality’ and ‘chirality’ are not good bases for extending Keywords: multivectors; wedge reversion antisymmetry; Clifford algebra. the classification of multivectors into arbitrary dimensions, and that introducing 1† would allow for such a classification.