Lie Matrix Groups: the Flip Transpose Group
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The Universal Property of Inverse Semigroup Equivariant KK-Theory
KYUNGPOOK Math. J. 61(2021), 111-137 https://doi.org/10.5666/KMJ.2021.61.1.111 pISSN 1225-6951 eISSN 0454-8124 c Kyungpook Mathematical Journal The Universal Property of Inverse Semigroup Equivariant KK-theory Bernhard Burgstaller Departamento de Matematica, Universidade Federal de Santa Catarina, CEP 88.040-900 Florian´opolis-SC, Brasil e-mail : [email protected] Abstract. Higson proved that every homotopy invariant, stable and split exact func- tor from the category of C∗-algebras to an additive category factors through Kasparov's KK-theory. By adapting a group equivariant generalization of this result by Thomsen, we generalize Higson's result to the inverse semigroup and locally compact, not necessarily Hausdorff groupoid equivariant setting. 1. Introduction In [3], Cuntz noted that if F is a homotopy invariant, stable and split exact func- tor from the category of separable C∗-algebras to the category of abelian groups then Kasparov's KK-theory acts on F , that is, every element of KK(A; B) induces a natural map F (A) ! F (B). Higson [4], on the other hand, developed Cuntz' find- ings further and proved that every such functor F factorizes through the category K consisting of separable C∗-algebras as the object class and KK-theory together with the Kasparov product as the morphism class, that is, F is the composition F^ ◦ κ of a universal functor κ from the class of C∗-algebras to K and a functor F^ from K to abelian groups. In [12], Thomsen generalized Higson's findings to the group equivariant setting by replacing everywhere in the above statement algebras by equivariant algebras, ∗- homomorphisms by equivariant ∗-homomorphisms and KK-theory by equivariant KK-theory (the proof is however far from such a straightforward replacement). -
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. -
Appendix a Spinors in Four Dimensions
Appendix A Spinors in Four Dimensions In this appendix we collect the conventions used for spinors in both Minkowski and Euclidean spaces. In Minkowski space the flat metric has the 0 1 2 3 form ηµν = diag(−1, 1, 1, 1), and the coordinates are labelled (x ,x , x , x ). The analytic continuation into Euclidean space is madethrough the replace- ment x0 = ix4 (and in momentum space, p0 = −ip4) the coordinates in this case being labelled (x1,x2, x3, x4). The Lorentz group in four dimensions, SO(3, 1), is not simply connected and therefore, strictly speaking, has no spinorial representations. To deal with these types of representations one must consider its double covering, the spin group Spin(3, 1), which is isomorphic to SL(2, C). The group SL(2, C) pos- sesses a natural complex two-dimensional representation. Let us denote this representation by S andlet us consider an element ψ ∈ S with components ψα =(ψ1,ψ2) relative to some basis. The action of an element M ∈ SL(2, C) is β (Mψ)α = Mα ψβ. (A.1) This is not the only action of SL(2, C) which one could choose. Instead of M we could have used its complex conjugate M, its inverse transpose (M T)−1,or its inverse adjoint (M †)−1. All of them satisfy the same group multiplication law. These choices would correspond to the complex conjugate representation S, the dual representation S,and the dual complex conjugate representation S. We will use the following conventions for elements of these representations: α α˙ ψα ∈ S, ψα˙ ∈ S, ψ ∈ S, ψ ∈ S. -
Material on Algebraic and Lie Groups
2 Lie groups and algebraic groups. 2.1 Basic Definitions. In this subsection we will introduce the class of groups to be studied. We first recall that a Lie group is a group that is also a differentiable manifold 1 and multiplication (x, y xy) and inverse (x x ) are C1 maps. An algebraic group is a group7! that is also an algebraic7! variety such that multi- plication and inverse are morphisms. Before we can introduce our main characters we first consider GL(n, C) as an affi ne algebraic group. Here Mn(C) denotes the space of n n matrices and GL(n, C) = g Mn(C) det(g) =) . Now Mn(C) is given the structure nf2 2 j 6 g of affi ne space C with the coordinates xij for X = [xij] . This implies that GL(n, C) is Z-open and as a variety is isomorphic with the affi ne variety 1 Mn(C) det . This implies that (GL(n, C)) = C[xij, det ]. f g O Lemma 1 If G is an algebraic group over an algebraically closed field, F , then every point in G is smooth. Proof. Let Lg : G G be given by Lgx = gx. Then Lg is an isomorphism ! 1 1 of G as an algebraic variety (Lg = Lg ). Since isomorphisms preserve the set of smooth points we see that if x G is smooth so is every element of Gx = G. 2 Proposition 2 If G is an algebraic group over an algebraically closed field F then the Z-connected components Proof. -
An Introduction to Inverse Semigroups and Their Connections to C*-Algebras
CARLETON UNIVERSITY SCHOOL OF MATHEMATICS AND STATISTICS HONOURS PROJECT TITLE: An Introduction to Inverse Semigroups and Their Connections To C*-Algebras AUTHOR: Kyle Cheaney SUPERVISOR: Dr. Charles Starling DATE: August 24, 2020 AN INTRODUCTION TO INVERSE SEMIGROUPS AND THEIR CONNECTIONS TO C∗-ALGEBRAS KYLE CHEANEY: 101012022 Abstract. This paper sets out to provide the reader with a basic introduction to inverse semigroups, as well as a proof of the Wagner-Preston Representation Theorem. It then focuses on finite directed graphs, specifically the graph inverse semigroup and how properties of the semigroup correspond to properties of C∗ algebras associated to such graphs. 1. Introduction The study of inverse semigroups was founded in the 1950's by three mathematicians: Ehresmann, Preston, and Wagner, as a way to algebraically describe pseudogroups of trans- formations. The purpose of this paper is to introduce the topic of inverse semigroups and some of the basic properties and theorems. We use these basics to relate the inverse semi- group formed by a directed graph to the corresponding graph C∗ algebra. The study of such C∗ algebras is still an active topic of research in analysis, thus providing a connection between abstract algebraic objects and complex analysis. In section 2, we outline some preliminary definitions and results which will be our basic tools for working with inverse semigroups. Section 3 provides a more detailed introduction to inverse semigroups and contains a proof of the Wagner-Preston representation theorem, a cornerstone in the study of inverse semigroups. In section 4, we present some detailed examples of inverse semigroups, including proving said sets are in fact inverse semigroups and discussing what their idempotents look like. -
Handout 9 More Matrix Properties; the Transpose
Handout 9 More matrix properties; the transpose Square matrix properties These properties only apply to a square matrix, i.e. n £ n. ² The leading diagonal is the diagonal line consisting of the entries a11, a22, a33, . ann. ² A diagonal matrix has zeros everywhere except the leading diagonal. ² The identity matrix I has zeros o® the leading diagonal, and 1 for each entry on the diagonal. It is a special case of a diagonal matrix, and A I = I A = A for any n £ n matrix A. ² An upper triangular matrix has all its non-zero entries on or above the leading diagonal. ² A lower triangular matrix has all its non-zero entries on or below the leading diagonal. ² A symmetric matrix has the same entries below and above the diagonal: aij = aji for any values of i and j between 1 and n. ² An antisymmetric or skew-symmetric matrix has the opposite entries below and above the diagonal: aij = ¡aji for any values of i and j between 1 and n. This automatically means the digaonal entries must all be zero. Transpose To transpose a matrix, we reect it across the line given by the leading diagonal a11, a22 etc. In general the result is a di®erent shape to the original matrix: a11 a21 a11 a12 a13 > > A = A = 0 a12 a22 1 [A ]ij = A : µ a21 a22 a23 ¶ ji a13 a23 @ A > ² If A is m £ n then A is n £ m. > ² The transpose of a symmetric matrix is itself: A = A (recalling that only square matrices can be symmetric). -
SOME ALGEBRAIC DEFINITIONS and CONSTRUCTIONS Definition
SOME ALGEBRAIC DEFINITIONS AND CONSTRUCTIONS Definition 1. A monoid is a set M with an element e and an associative multipli- cation M M M for which e is a two-sided identity element: em = m = me for all m M×. A−→group is a monoid in which each element m has an inverse element m−1, so∈ that mm−1 = e = m−1m. A homomorphism f : M N of monoids is a function f such that f(mn) = −→ f(m)f(n) and f(eM )= eN . A “homomorphism” of any kind of algebraic structure is a function that preserves all of the structure that goes into the definition. When M is commutative, mn = nm for all m,n M, we often write the product as +, the identity element as 0, and the inverse of∈m as m. As a convention, it is convenient to say that a commutative monoid is “Abelian”− when we choose to think of its product as “addition”, but to use the word “commutative” when we choose to think of its product as “multiplication”; in the latter case, we write the identity element as 1. Definition 2. The Grothendieck construction on an Abelian monoid is an Abelian group G(M) together with a homomorphism of Abelian monoids i : M G(M) such that, for any Abelian group A and homomorphism of Abelian monoids−→ f : M A, there exists a unique homomorphism of Abelian groups f˜ : G(M) A −→ −→ such that f˜ i = f. ◦ We construct G(M) explicitly by taking equivalence classes of ordered pairs (m,n) of elements of M, thought of as “m n”, under the equivalence relation generated by (m,n) (m′,n′) if m + n′ = −n + m′. -
Matrix Lie Groups
Maths Seminar 2007 MATRIX LIE GROUPS Claudiu C Remsing Dept of Mathematics (Pure and Applied) Rhodes University Grahamstown 6140 26 September 2007 RhodesUniv CCR 0 Maths Seminar 2007 TALK OUTLINE 1. What is a matrix Lie group ? 2. Matrices revisited. 3. Examples of matrix Lie groups. 4. Matrix Lie algebras. 5. A glimpse at elementary Lie theory. 6. Life beyond elementary Lie theory. RhodesUniv CCR 1 Maths Seminar 2007 1. What is a matrix Lie group ? Matrix Lie groups are groups of invertible • matrices that have desirable geometric features. So matrix Lie groups are simultaneously algebraic and geometric objects. Matrix Lie groups naturally arise in • – geometry (classical, algebraic, differential) – complex analyis – differential equations – Fourier analysis – algebra (group theory, ring theory) – number theory – combinatorics. RhodesUniv CCR 2 Maths Seminar 2007 Matrix Lie groups are encountered in many • applications in – physics (geometric mechanics, quantum con- trol) – engineering (motion control, robotics) – computational chemistry (molecular mo- tion) – computer science (computer animation, computer vision, quantum computation). “It turns out that matrix [Lie] groups • pop up in virtually any investigation of objects with symmetries, such as molecules in chemistry, particles in physics, and projective spaces in geometry”. (K. Tapp, 2005) RhodesUniv CCR 3 Maths Seminar 2007 EXAMPLE 1 : The Euclidean group E (2). • E (2) = F : R2 R2 F is an isometry . → | n o The vector space R2 is equipped with the standard Euclidean structure (the “dot product”) x y = x y + x y (x, y R2), • 1 1 2 2 ∈ hence with the Euclidean distance d (x, y) = (y x) (y x) (x, y R2). -
Geometric-Algebra Adaptive Filters Wilder B
1 Geometric-Algebra Adaptive Filters Wilder B. Lopes∗, Member, IEEE, Cassio G. Lopesy, Senior Member, IEEE Abstract—This paper presents a new class of adaptive filters, namely Geometric-Algebra Adaptive Filters (GAAFs). They are Faces generated by formulating the underlying minimization problem (a deterministic cost function) from the perspective of Geometric Algebra (GA), a comprehensive mathematical language well- Edges suited for the description of geometric transformations. Also, (directed lines) differently from standard adaptive-filtering theory, Geometric Calculus (the extension of GA to differential calculus) allows Fig. 1. A polyhedron (3-dimensional polytope) can be completely described for applying the same derivation techniques regardless of the by the geometric multiplication of its edges (oriented lines, vectors), which type (subalgebra) of the data, i.e., real, complex numbers, generate the faces and hypersurfaces (in the case of a general n-dimensional quaternions, etc. Relying on those characteristics (among others), polytope). a deterministic quadratic cost function is posed, from which the GAAFs are devised, providing a generalization of regular adaptive filters to subalgebras of GA. From the obtained update rule, it is shown how to recover the following least-mean squares perform calculus with hypercomplex quantities, i.e., elements (LMS) adaptive filter variants: real-entries LMS, complex LMS, that generalize complex numbers for higher dimensions [2]– and quaternions LMS. Mean-square analysis and simulations in [10]. a system identification scenario are provided, showing very good agreement for different levels of measurement noise. GA-based AFs were first introduced in [11], [12], where they were successfully employed to estimate the geometric Index Terms—Adaptive filtering, geometric algebra, quater- transformation (rotation and translation) that aligns a pair of nions. -
MAS4107 Linear Algebra 2 Linear Maps And
Introduction Groups and Fields Vector Spaces Subspaces, Linear . Bases and Coordinates MAS4107 Linear Algebra 2 Linear Maps and . Change of Basis Peter Sin More on Linear Maps University of Florida Linear Endomorphisms email: [email protected]fl.edu Quotient Spaces Spaces of Linear . General Prerequisites Direct Sums Minimal polynomial Familiarity with the notion of mathematical proof and some experience in read- Bilinear Forms ing and writing proofs. Familiarity with standard mathematical notation such as Hermitian Forms summations and notations of set theory. Euclidean and . Self-Adjoint Linear . Linear Algebra Prerequisites Notation Familiarity with the notion of linear independence. Gaussian elimination (reduction by row operations) to solve systems of equations. This is the most important algorithm and it will be assumed and used freely in the classes, for example to find JJ J I II coordinate vectors with respect to basis and to compute the matrix of a linear map, to test for linear dependence, etc. The determinant of a square matrix by cofactors Back and also by row operations. Full Screen Close Quit Introduction 0. Introduction Groups and Fields Vector Spaces These notes include some topics from MAS4105, which you should have seen in one Subspaces, Linear . form or another, but probably presented in a totally different way. They have been Bases and Coordinates written in a terse style, so you should read very slowly and with patience. Please Linear Maps and . feel free to email me with any questions or comments. The notes are in electronic Change of Basis form so sections can be changed very easily to incorporate improvements. -
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.. -
Differentiable Manifolds
Gerardo F. Torres del Castillo Differentiable Manifolds ATheoreticalPhysicsApproach Gerardo F. Torres del Castillo Instituto de Ciencias Universidad Autónoma de Puebla Ciudad Universitaria 72570 Puebla, Puebla, Mexico [email protected] ISBN 978-0-8176-8270-5 e-ISBN 978-0-8176-8271-2 DOI 10.1007/978-0-8176-8271-2 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2011939950 Mathematics Subject Classification (2010): 22E70, 34C14, 53B20, 58A15, 70H05 © Springer Science+Business Media, LLC 2012 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.birkhauser-science.com) Preface The aim of this book is to present in an elementary manner the basic notions related with differentiable manifolds and some of their applications, especially in physics. The book is aimed at advanced undergraduate and graduate students in physics and mathematics, assuming a working knowledge of calculus in several variables, linear algebra, and differential equations.