Differential Geometry on Matrix Groups
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Math 317: Linear Algebra Notes and Problems
Math 317: Linear Algebra Notes and Problems Nicholas Long SFASU Contents Introduction iv 1 Efficiently Solving Systems of Linear Equations and Matrix Op- erations 1 1.1 Warmup Problems . .1 1.2 Solving Linear Systems . .3 1.2.1 Geometric Interpretation of a Solution Set . .9 1.3 Vector and Matrix Equations . 11 1.4 Solution Sets of Linear Systems . 15 1.5 Applications . 18 1.6 Matrix operations . 20 1.6.1 Special Types of Matrices . 21 1.6.2 Matrix Multiplication . 22 2 Vector Spaces 25 2.1 Subspaces . 28 2.2 Span . 29 2.3 Linear Independence . 31 2.4 Linear Transformations . 33 2.5 Applications . 38 3 Connecting Ideas 40 3.1 Basis and Dimension . 40 3.1.1 rank and nullity . 41 3.1.2 Coordinate Vectors relative to a basis . 42 3.2 Invertible Matrices . 44 3.2.1 Elementary Matrices . 44 ii CONTENTS iii 3.2.2 Computing Inverses . 45 3.2.3 Invertible Matrix Theorem . 46 3.3 Invertible Linear Transformations . 47 3.4 LU factorization of matrices . 48 3.5 Determinants . 49 3.5.1 Computing Determinants . 49 3.5.2 Properties of Determinants . 51 3.6 Eigenvalues and Eigenvectors . 52 3.6.1 Diagonalizability . 55 3.6.2 Eigenvalues and Eigenvectors of Linear Transfor- mations . 56 4 Inner Product Spaces 58 4.1 Inner Products . 58 4.2 Orthogonal Complements . 59 4.3 QR Decompositions . 59 Nicholas Long Introduction In this course you will learn about linear algebra by solving a carefully designed sequence of problems. It is important that you understand every problem and proof. -
Notes, Since the Quadratic Form Associated with an Inner Product (Kuk2 = Hu, Ui) Must Be Positive Def- Inite
Bindel, Spring 2012 Intro to Scientific Computing (CS 3220) Week 3: Wednesday, Feb 8 Spaces and bases 1 n I have two favorite vector spaces : R and the space Pd of polynomials of degree at most d. For Rn, we have a canonical basis: n R = spanfe1; e2; : : : ; eng; where ek is the kth column of the identity matrix. This basis is frequently convenient both for analysis and for computation. For Pd, an obvious- seeming choice of basis is the power basis: 2 d Pd = spanf1; x; x ; : : : ; x g: But this obvious-looking choice turns out to often be terrible for computation. Why? The short version is that powers of x aren't all that strongly linearly dependent, but we need to develop some more concepts before that short description will make much sense. The range space of a matrix or a linear map A is just the set of vectors y that can be written in the form y = Ax. If A is full (column) rank, then the columns of A are linearly independent, and they form a basis for the range space. Otherwise, A is rank-deficient, and there is a non-trivial null space consisting of vectors x such that Ax = 0. Rank deficiency is a delicate property2. For example, consider the matrix 1 1 A = : 1 1 This matrix is rank deficient, but the matrix 1 + δ 1 A^ = : 1 1 is not rank deficient for any δ 6= 0. Technically, the columns of A^ form a basis for R2, but we should be disturbed by the fact that A^ is so close to a singular matrix. -
The General Linear Group
18.704 Gabe Cunningham 2/18/05 [email protected] The General Linear Group Definition: Let F be a field. Then the general linear group GLn(F ) is the group of invert- ible n × n matrices with entries in F under matrix multiplication. It is easy to see that GLn(F ) is, in fact, a group: matrix multiplication is associative; the identity element is In, the n × n matrix with 1’s along the main diagonal and 0’s everywhere else; and the matrices are invertible by choice. It’s not immediately clear whether GLn(F ) has infinitely many elements when F does. However, such is the case. Let a ∈ F , a 6= 0. −1 Then a · In is an invertible n × n matrix with inverse a · In. In fact, the set of all such × matrices forms a subgroup of GLn(F ) that is isomorphic to F = F \{0}. It is clear that if F is a finite field, then GLn(F ) has only finitely many elements. An interesting question to ask is how many elements it has. Before addressing that question fully, let’s look at some examples. ∼ × Example 1: Let n = 1. Then GLn(Fq) = Fq , which has q − 1 elements. a b Example 2: Let n = 2; let M = ( c d ). Then for M to be invertible, it is necessary and sufficient that ad 6= bc. If a, b, c, and d are all nonzero, then we can fix a, b, and c arbitrarily, and d can be anything but a−1bc. This gives us (q − 1)3(q − 2) matrices. -
Lie Group and Geometry on the Lie Group SL2(R)
INDIAN INSTITUTE OF TECHNOLOGY KHARAGPUR Lie group and Geometry on the Lie Group SL2(R) PROJECT REPORT – SEMESTER IV MOUSUMI MALICK 2-YEARS MSc(2011-2012) Guided by –Prof.DEBAPRIYA BISWAS Lie group and Geometry on the Lie Group SL2(R) CERTIFICATE This is to certify that the project entitled “Lie group and Geometry on the Lie group SL2(R)” being submitted by Mousumi Malick Roll no.-10MA40017, Department of Mathematics is a survey of some beautiful results in Lie groups and its geometry and this has been carried out under my supervision. Dr. Debapriya Biswas Department of Mathematics Date- Indian Institute of Technology Khargpur 1 Lie group and Geometry on the Lie Group SL2(R) ACKNOWLEDGEMENT I wish to express my gratitude to Dr. Debapriya Biswas for her help and guidance in preparing this project. Thanks are also due to the other professor of this department for their constant encouragement. Date- place-IIT Kharagpur Mousumi Malick 2 Lie group and Geometry on the Lie Group SL2(R) CONTENTS 1.Introduction ................................................................................................... 4 2.Definition of general linear group: ............................................................... 5 3.Definition of a general Lie group:................................................................... 5 4.Definition of group action: ............................................................................. 5 5. Definition of orbit under a group action: ...................................................... 5 6.1.The general linear -
The Pure State Space of Quantum Mechanics As Hermitian Symmetric
The Pure State Space of Quantum Mechanics as Hermitian Symmetric Space. R. Cirelli, M. Gatti, A. Mani`a Dipartimento di Fisica, Universit`adegli Studi di Milano, Italy Istituto Nazionale di Fisica Nucleare, Sezione di Milano, Italy Abstract The pure state space of Quantum Mechanics is investigated as Hermitian Symmetric K¨ahler manifold. The classical principles of Quantum Mechan- ics (Quantum Superposition Principle, Heisenberg Uncertainty Principle, Quantum Probability Principle) and Spectral Theory of observables are dis- cussed in this non linear geometrical context. Subj. Class.: Quantum mechanics 1991 MSC : 58BXX; 58B20; 58FXX; 58HXX; 53C22 Keywords: Superposition principle, quantum states; Riemannian mani- folds; Poisson algebras; Geodesics. 1. Introduction Several models of delinearization of Quantum Mechanics have been pro- arXiv:quant-ph/0202076v1 14 Feb 2002 posed (see f.i. [20] [26], [11], [2] and [5] for a complete list of references). Frequentely these proposals are supported by different motivations, but it appears that a common feature is that, more or less, the delinearization must be paid essentially by the superposition principle. This attitude can be understood if the delinearization program is worked out in the setting of a Hilbert space as a ground mathematical structure. However, as is well known, the groundH mathematical structure of QM is the manifold of (pure) states P( ), the projective space of the Hilbert space . Since, obviously, P( ) is notH a linear object, the popular way of thinking thatH the superposition principleH compels the linearity of the space of states is untenable. The delinearization program, by itself, is not related in our opinion to attempts to construct a non linear extension of QM with operators which 1 2 act non linearly on the Hilbert space . -
Symmetric Spaces with Conformal Symmetry
Symmetric Spaces with Conformal Symmetry A. Wehner∗ Department of Physics, Utah State University, Logan, Utah 84322 October 30, 2018 Abstract We consider an involutive automorphism of the conformal algebra and the resulting symmetric space. We display a new action of the conformal group which gives rise to this space. The space has an in- trinsic symplectic structure, a group-invariant metric and connection, and serves as the model space for a new conformal gauge theory. ∗Electronic-mail: [email protected] arXiv:hep-th/0107008v1 1 Jul 2001 1 1 Introduction Symmetric spaces are the most widely studied class of homogeneous spaces. They form a subclass of the reductive homogeneous spaces, which can es- sentially be characterized by the fact that they admit a unique torsion-free group-invariant connection. For symmetric spaces the curvature is covari- antly constant with respect to this connection. Many essential results and extensive bibliographies on symmetric spaces can be found, for example, in the standard texts by Kobayashi and Nomizu [1] and Helgason [2]. The physicists’ definition of a (maximally) symmetric space as a metric space which admits a maximum number of Killing vectors is considerably more restrictive. A thorough treatment of symmetric spaces in general rel- ativity, where a pseudo-Riemannian metric and the metric-compatible con- nection are assumed, can be found in Weinberg [3]. The most important symmetric space in gravitational theories is Minkowski space, which is based on the symmetric pair (Poincar´egroup, Lorentz group). Since the Poincar´e group is not semi-simple, it does not admit an intrinsic group-invariant met- ric, which would project in the canonical fashion to the Minkowski metric ηab = diag(−1, 1 .. -
Linear Algebra) 2
MATH 680 Computation Intensive Statistics December 3, 2018 Matrix Basics Contents 1 Rank (linear algebra) 2 1.1 Proofs that column rank = row rank . .2 1.2 Properties . .4 2 Kernel (linear algebra) 5 2.1 Properties . .6 2.2 Illustration . .9 3 Matrix Norm 12 3.1 Matrix norms induced by vector norms . 12 3.2 Entrywise matrix norms . 14 3.3 Schatten norms . 18 1 1 RANK (LINEAR ALGEBRA) 2 4 Projection 19 4.1 Properties and classification . 20 4.2 Orthogonal projections . 21 1 Rank (linear algebra) In linear algebra, the rank of a matrix A is the dimension of the vector space generated (or spanned) by its columns. This corresponds to the maximal number of linearly independent columns of A. This, in turn, is identical to the dimension of the space spanned by its rows. The column rank of A is the dimension of the column space of A, while the row rank of A is the dimension of the row space of A. A fundamental result in linear algebra is that the column rank and the row rank are always equal. 1.1 Proofs that column rank = row rank Let A be an m × n matrix with entries in the real numbers whose row rank is r. There- fore, the dimension of the row space of A is r. Let x1; x2; : : : ; xr be a basis of the row space of A. We claim that the vectors Ax1; Ax2; : : : ; Axr are linearly independent. To see why, consider a linear homogeneous relation involving these vectors with scalar coefficients c1; c2; : : : ; cr : 0 = c1Ax1 + c2Ax2 + ··· + crAxr = A(c1x1 + c2x2 + ··· + crxr) = Av; 1 RANK (LINEAR ALGEBRA) 3 where v = c1x1 + c2x2 + ··· + crxr: We make two observations: 1. -
Inner Products and Orthogonality
Advanced Linear Algebra – Week 5 Inner Products and Orthogonality This week we will learn about: • Inner products (and the dot product again), • The norm induced by the inner product, • The Cauchy–Schwarz and triangle inequalities, and • Orthogonality. Extra reading and watching: • Sections 1.3.4 and 1.4.1 in the textbook • Lecture videos 17, 18, 19, 20, 21, and 22 on YouTube • Inner product space at Wikipedia • Cauchy–Schwarz inequality at Wikipedia • Gram–Schmidt process at Wikipedia Extra textbook problems: ? 1.3.3, 1.3.4, 1.4.1 ?? 1.3.9, 1.3.10, 1.3.12, 1.3.13, 1.4.2, 1.4.5(a,d) ??? 1.3.11, 1.3.14, 1.3.15, 1.3.25, 1.4.16 A 1.3.18 1 Advanced Linear Algebra – Week 5 2 There are many times when we would like to be able to talk about the angle between vectors in a vector space V, and in particular orthogonality of vectors, just like we did in Rn in the previous course. This requires us to have a generalization of the dot product to arbitrary vector spaces. Definition 5.1 — Inner Product Suppose that F = R or F = C, and V is a vector space over F. Then an inner product on V is a function h·, ·i : V × V → F such that the following three properties hold for all c ∈ F and all v, w, x ∈ V: a) hv, wi = hw, vi (conjugate symmetry) b) hv, w + cxi = hv, wi + chv, xi (linearity in 2nd entry) c) hv, vi ≥ 0, with equality if and only if v = 0. -
Matrix Lie Groups and Their Lie Algebras
Matrix Lie groups and their Lie algebras Alen Alexanderian∗ Abstract We discuss matrix Lie groups and their corresponding Lie algebras. Some common examples are provided for purpose of illustration. 1 Introduction The goal of these brief note is to provide a quick introduction to matrix Lie groups which are a special class of abstract Lie groups. Study of matrix Lie groups is a fruitful endeavor which allows one an entry to theory of Lie groups without requiring knowl- edge of differential topology. After all, most interesting Lie groups turn out to be matrix groups anyway. An abstract Lie group is defined to be a group which is also a smooth manifold, where the group operations of multiplication and inversion are also smooth. We provide a much simple definition for a matrix Lie group in Section 4. Showing that a matrix Lie group is in fact a Lie group is discussed in standard texts such as [2]. We also discuss Lie algebras [1], and the computation of the Lie algebra of a Lie group in Section 5. We will compute the Lie algebras of several well known Lie groups in that section for the purpose of illustration. 2 Notation Let V be a vector space. We denote by gl(V) the space of all linear transformations on V. If V is a finite-dimensional vector space we may put an arbitrary basis on V and identify elements of gl(V) with their matrix representation. The following define various classes of matrices on Rn: ∗The University of Texas at Austin, USA. E-mail: [email protected] Last revised: July 12, 2013 Matrix Lie groups gl(n) : the space of n -
1 the Spin Homomorphism SL2(C) → SO1,3(R) a Summary from Multiple Sources Written by Y
1 The spin homomorphism SL2(C) ! SO1;3(R) A summary from multiple sources written by Y. Feng and Katherine E. Stange Abstract We will discuss the spin homomorphism SL2(C) ! SO1;3(R) in three manners. Firstly we interpret SL2(C) as acting on the Minkowski 1;3 spacetime R ; secondly by viewing the quadratic form as a twisted 1 1 P × P ; and finally using Clifford groups. 1.1 Introduction The spin homomorphism SL2(C) ! SO1;3(R) is a homomorphism of classical matrix Lie groups. The lefthand group con- sists of 2 × 2 complex matrices with determinant 1. The righthand group consists of 4 × 4 real matrices with determinant 1 which preserve some fixed real quadratic form Q of signature (1; 3). This map is alternately called the spinor map and variations. The image of this map is the identity component + of SO1;3(R), denoted SO1;3(R). The kernel is {±Ig. Therefore, we obtain an isomorphism + PSL2(C) = SL2(C)= ± I ' SO1;3(R): This is one of a family of isomorphisms of Lie groups called exceptional iso- morphisms. In Section 1.3, we give the spin homomorphism explicitly, al- though these formulae are unenlightening by themselves. In Section 1.4 we describe O1;3(R) in greater detail as the group of Lorentz transformations. This document describes this homomorphism from three distinct per- spectives. The first is very concrete, and constructs, using the language of Minkowski space, Lorentz transformations and Hermitian matrices, an ex- 4 plicit action of SL2(C) on R preserving Q (Section 1.5). -
A STUDY on the ALGEBRAIC STRUCTURE of SL 2(Zpz)
A STUDY ON THE ALGEBRAIC STRUCTURE OF SL2 Z pZ ( ~ ) A Thesis Presented to The Honors Tutorial College Ohio University In Partial Fulfillment of the Requirements for Graduation from the Honors Tutorial College with the degree of Bachelor of Science in Mathematics by Evan North April 2015 Contents 1 Introduction 1 2 Background 5 2.1 Group Theory . 5 2.2 Linear Algebra . 14 2.3 Matrix Group SL2 R Over a Ring . 22 ( ) 3 Conjugacy Classes of Matrix Groups 26 3.1 Order of the Matrix Groups . 26 3.2 Conjugacy Classes of GL2 Fp ....................... 28 3.2.1 Linear Case . .( . .) . 29 3.2.2 First Quadratic Case . 29 3.2.3 Second Quadratic Case . 30 3.2.4 Third Quadratic Case . 31 3.2.5 Classes in SL2 Fp ......................... 33 3.3 Splitting of Classes of(SL)2 Fp ....................... 35 3.4 Results of SL2 Fp ..............................( ) 40 ( ) 2 4 Toward Lifting to SL2 Z p Z 41 4.1 Reduction mod p ...............................( ~ ) 42 4.2 Exploring the Kernel . 43 i 4.3 Generalizing to SL2 Z p Z ........................ 46 ( ~ ) 5 Closing Remarks 48 5.1 Future Work . 48 5.2 Conclusion . 48 1 Introduction Symmetries are one of the most widely-known examples of pure mathematics. Symmetry is when an object can be rotated, flipped, or otherwise transformed in such a way that its appearance remains the same. Basic geometric figures should create familiar examples, take for instance the triangle. Figure 1: The symmetries of a triangle: 3 reflections, 2 rotations. The red lines represent the reflection symmetries, where the trianlge is flipped over, while the arrows represent the rotational symmetry of the triangle. -
Holonomy and the Lie Algebra of Infinitesimal Motions of a Riemannian Manifold
HOLONOMY AND THE LIE ALGEBRA OF INFINITESIMAL MOTIONS OF A RIEMANNIAN MANIFOLD BY BERTRAM KOSTANT Introduction. .1. Let M be a differentiable manifold of class Cx. All tensor fields discussed below are assumed to be of class C°°. Let X be a vector field on M. If X vanishes at a point oCM then X induces, in a natural way, an endomorphism ax of the tangent space F0 at o. In fact if y£ V„ and F is any vector field whose value at o is y, then define axy = [X, Y]„. It is not hard to see that [X, Y]0 does not depend on F so long as the value of F at o is y. Now assume M has an affine connection or as we shall do in this paper, assume M is Riemannian and that it possesses the corresponding affine connection. One may now associate to X an endomorphism ax of F0 for any point oCM which agrees with the above definition and which heuristically indicates how X "winds around o" by defining for vC V0, axv = — VtX, where V. is the symbol of covariant differentiation with respect to v. X is called an infinitesmal motion if the Lie derivative of the metric tensor with respect to X is equal to zero. This is equivalent to the statement that ax is skew-symmetric (as an endomorphism of F0 where the latter is provided with the inner product generated by the metric tensor) for all oCM. §1 contains definitions and formulae which will be used in the remaining sections.