MATRICES WHOSE HERMITIAN PART IS POSITIVE DEFINITE Thesis by Charles Royal Johnson in Partial Fulfillment of the Requirements Fo
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Math 4571 (Advanced Linear Algebra) Lecture #27
Math 4571 (Advanced Linear Algebra) Lecture #27 Applications of Diagonalization and the Jordan Canonical Form (Part 1): Spectral Mapping and the Cayley-Hamilton Theorem Transition Matrices and Markov Chains The Spectral Theorem for Hermitian Operators This material represents x4.4.1 + x4.4.4 +x4.4.5 from the course notes. Overview In this lecture and the next, we discuss a variety of applications of diagonalization and the Jordan canonical form. This lecture will discuss three essentially unrelated topics: A proof of the Cayley-Hamilton theorem for general matrices Transition matrices and Markov chains, used for modeling iterated changes in systems over time The spectral theorem for Hermitian operators, in which we establish that Hermitian operators (i.e., operators with T ∗ = T ) are diagonalizable In the next lecture, we will discuss another fundamental application: solving systems of linear differential equations. Cayley-Hamilton, I First, we establish the Cayley-Hamilton theorem for arbitrary matrices: Theorem (Cayley-Hamilton) If p(x) is the characteristic polynomial of a matrix A, then p(A) is the zero matrix 0. The same result holds for the characteristic polynomial of a linear operator T : V ! V on a finite-dimensional vector space. Cayley-Hamilton, II Proof: Since the characteristic polynomial of a matrix does not depend on the underlying field of coefficients, we may assume that the characteristic polynomial factors completely over the field (i.e., that all of the eigenvalues of A lie in the field) by replacing the field with its algebraic closure. Then by our results, the Jordan canonical form of A exists. -
Lecture Notes: Qubit Representations and Rotations
Phys 711 Topics in Particles & Fields | Spring 2013 | Lecture 1 | v0.3 Lecture notes: Qubit representations and rotations Jeffrey Yepez Department of Physics and Astronomy University of Hawai`i at Manoa Watanabe Hall, 2505 Correa Road Honolulu, Hawai`i 96822 E-mail: [email protected] www.phys.hawaii.edu/∼yepez (Dated: January 9, 2013) Contents mathematical object (an abstraction of a two-state quan- tum object) with a \one" state and a \zero" state: I. What is a qubit? 1 1 0 II. Time-dependent qubits states 2 jqi = αj0i + βj1i = α + β ; (1) 0 1 III. Qubit representations 2 A. Hilbert space representation 2 where α and β are complex numbers. These complex B. SU(2) and O(3) representations 2 numbers are called amplitudes. The basis states are or- IV. Rotation by similarity transformation 3 thonormal V. Rotation transformation in exponential form 5 h0j0i = h1j1i = 1 (2a) VI. Composition of qubit rotations 7 h0j1i = h1j0i = 0: (2b) A. Special case of equal angles 7 In general, the qubit jqi in (1) is said to be in a superpo- VII. Example composite rotation 7 sition state of the two logical basis states j0i and j1i. If References 9 α and β are complex, it would seem that a qubit should have four free real-valued parameters (two magnitudes and two phases): I. WHAT IS A QUBIT? iθ0 α φ0 e jqi = = iθ1 : (3) Let us begin by introducing some notation: β φ1 e 1 state (called \minus" on the Bloch sphere) Yet, for a qubit to contain only one classical bit of infor- 0 mation, the qubit need only be unimodular (normalized j1i = the alternate symbol is |−i 1 to unity) α∗α + β∗β = 1: (4) 0 state (called \plus" on the Bloch sphere) 1 Hence it lives on the complex unit circle, depicted on the j0i = the alternate symbol is j+i: 0 top of Figure 1. -
MATH 2370, Practice Problems
MATH 2370, Practice Problems Kiumars Kaveh Problem: Prove that an n × n complex matrix A is diagonalizable if and only if there is a basis consisting of eigenvectors of A. Problem: Let A : V ! W be a one-to-one linear map between two finite dimensional vector spaces V and W . Show that the dual map A0 : W 0 ! V 0 is surjective. Problem: Determine if the curve 2 2 2 f(x; y) 2 R j x + y + xy = 10g is an ellipse or hyperbola or union of two lines. Problem: Show that if a nilpotent matrix is diagonalizable then it is the zero matrix. Problem: Let P be a permutation matrix. Show that P is diagonalizable. Show that if λ is an eigenvalue of P then for some integer m > 0 we have λm = 1 (i.e. λ is an m-th root of unity). Hint: Note that P m = I for some integer m > 0. Problem: Show that if λ is an eigenvector of an orthogonal matrix A then jλj = 1. n Problem: Take a vector v 2 R and let H be the hyperplane orthogonal n n to v. Let R : R ! R be the reflection with respect to a hyperplane H. Prove that R is a diagonalizable linear map. Problem: Prove that if λ1; λ2 are distinct eigenvalues of a complex matrix A then the intersection of the generalized eigenspaces Eλ1 and Eλ2 is zero (this is part of the Spectral Theorem). 1 Problem: Let H = (hij) be a 2 × 2 Hermitian matrix. Use the Min- imax Principle to show that if λ1 ≤ λ2 are the eigenvalues of H then λ1 ≤ h11 ≤ λ2. -
Parametrization of 3×3 Unitary Matrices Based on Polarization
Parametrization of 33 unitary matrices based on polarization algebra (May, 2018) José J. Gil Parametrization of 33 unitary matrices based on polarization algebra José J. Gil Universidad de Zaragoza. Pedro Cerbuna 12, 50009 Zaragoza Spain [email protected] Abstract A parametrization of 33 unitary matrices is presented. This mathematical approach is inspired by polarization algebra and is formulated through the identification of a set of three orthonormal three-dimensional Jones vectors representing respective pure polarization states. This approach leads to the representation of a 33 unitary matrix as an orthogonal similarity transformation of a particular type of unitary matrix that depends on six independent parameters, while the remaining three parameters correspond to the orthogonal matrix of the said transformation. The results obtained are applied to determine the structure of the second component of the characteristic decomposition of a 33 positive semidefinite Hermitian matrix. 1 Introduction In many branches of Mathematics, Physics and Engineering, 33 unitary matrices appear as key elements for solving a great variety of problems, and therefore, appropriate parameterizations in terms of minimum sets of nine independent parameters are required for the corresponding mathematical treatment. In this way, some interesting parametrizations have been obtained [1-8]. In particular, the Cabibbo-Kobayashi-Maskawa matrix (CKM matrix) [6,7], which represents information on the strength of flavour-changing weak decays and depends on four parameters, constitutes the core of a family of parametrizations of a 33 unitary matrix [8]. In this paper, a new general parametrization is presented, which is inspired by polarization algebra [9] through the structure of orthonormal sets of three-dimensional Jones vectors [10]. -
Math 223 Symmetric and Hermitian Matrices. Richard Anstee an N × N Matrix Q Is Orthogonal If QT = Q−1
Math 223 Symmetric and Hermitian Matrices. Richard Anstee An n × n matrix Q is orthogonal if QT = Q−1. The columns of Q would form an orthonormal basis for Rn. The rows would also form an orthonormal basis for Rn. A matrix A is symmetric if AT = A. Theorem 0.1 Let A be a symmetric n × n matrix of real entries. Then there is an orthogonal matrix Q and a diagonal matrix D so that AQ = QD; i.e. QT AQ = D: Note that the entries of M and D are real. There are various consequences to this result: A symmetric matrix A is diagonalizable A symmetric matrix A has an othonormal basis of eigenvectors. A symmetric matrix A has real eigenvalues. Proof: The proof begins with an appeal to the fundamental theorem of algebra applied to det(A − λI) which asserts that the polynomial factors into linear factors and one of which yields an eigenvalue λ which may not be real. Our second step it to show λ is real. Let x be an eigenvector for λ so that Ax = λx. Again, if λ is not real we must allow for the possibility that x is not a real vector. Let xH = xT denote the conjugate transpose. It applies to matrices as AH = AT . Now xH x ≥ 0 with xH x = 0 if and only if x = 0. We compute xH Ax = xH (λx) = λxH x. Now taking complex conjugates and transpose (xH Ax)H = xH AH x using that (xH )H = x. Then (xH Ax)H = xH Ax = λxH x using AH = A. -
Math 408 Advanced Linear Algebra Chi-Kwong Li Chapter 2 Unitary
Math 408 Advanced Linear Algebra Chi-Kwong Li Chapter 2 Unitary similarity and normal matrices n ∗ Definition A set of vector fx1; : : : ; xkg in C is orthogonal if xi xj = 0 for i 6= j. If, in addition, ∗ that xj xj = 1 for each j, then the set is orthonormal. Theorem An orthonormal set of vectors in Cn is linearly independent. n Definition An orthonormal set fx1; : : : ; xng in C is an orthonormal basis. A matrix U 2 Mn ∗ is unitary if it has orthonormal columns, i.e., U U = In. Theorem Let U 2 Mn. The following are equivalent. (a) U is unitary. (b) U is invertible and U ∗ = U −1. ∗ ∗ (c) UU = In, i.e., U is unitary. (d) kUxk = kxk for all x 2 Cn, where kxk = (x∗x)1=2. Remarks (1) Real unitary matrices are orthogonal matrices. (2) Unitary (real orthogonal) matrices form a group in the set of complex (real) matrices and is a subgroup of the group of invertible matrices. ∗ Definition Two matrices A; B 2 Mn are unitarily similar if A = U BU for some unitary U. Theorem If A; B 2 Mn are unitarily similar, then X 2 ∗ ∗ X 2 jaijj = tr (A A) = tr (B B) = jbijj : i;j i;j Specht's Theorem Two matrices A and B are similar if and only if tr W (A; A∗) = tr W (B; B∗) for all words of length of degree at most 2n2. Schur's Theorem Every A 2 Mn is unitarily similar to a upper triangular matrix. Theorem Every A 2 Mn(R) is orthogonally similar to a matrix in upper triangular block form so that the diagonal blocks have size at most 2. -
Linear Operators
C H A P T E R 10 Linear Operators Recall that a linear transformation T ∞ L(V) of a vector space into itself is called a (linear) operator. In this chapter we shall elaborate somewhat on the theory of operators. In so doing, we will define several important types of operators, and we will also prove some important diagonalization theorems. Much of this material is directly useful in physics and engineering as well as in mathematics. While some of this chapter overlaps with Chapter 8, we assume that the reader has studied at least Section 8.1. 10.1 LINEAR FUNCTIONALS AND ADJOINTS Recall that in Theorem 9.3 we showed that for a finite-dimensional real inner product space V, the mapping u ’ Lu = Óu, Ô was an isomorphism of V onto V*. This mapping had the property that Lauv = Óau, vÔ = aÓu, vÔ = aLuv, and hence Lau = aLu for all u ∞ V and a ∞ ®. However, if V is a complex space with a Hermitian inner product, then Lauv = Óau, vÔ = a*Óu, vÔ = a*Luv, and hence Lau = a*Lu which is not even linear (this was the definition of an anti- linear (or conjugate linear) transformation given in Section 9.2). Fortunately, there is a closely related result that holds even for complex vector spaces. Let V be finite-dimensional over ç, and assume that V has an inner prod- uct Ó , Ô defined on it (this is just a positive definite Hermitian form on V). Thus for any X, Y ∞ V we have ÓX, YÔ ∞ ç. -
Arxiv:1901.01378V2 [Math-Ph] 8 Apr 2020 Where A(P, Q) Is the Arithmetic Mean of the Vectors P and Q, G(P, Q) Is Their P Geometric Mean, and Tr X Stands for Xi
MATRIX VERSIONS OF THE HELLINGER DISTANCE RAJENDRA BHATIA, STEPHANE GAUBERT, AND TANVI JAIN Abstract. On the space of positive definite matrices we consider dis- tance functions of the form d(A; B) = [trA(A; B) − trG(A; B)]1=2 ; where A(A; B) is the arithmetic mean and G(A; B) is one of the different versions of the geometric mean. When G(A; B) = A1=2B1=2 this distance is kA1=2− 1=2 1=2 1=2 1=2 B k2; and when G(A; B) = (A BA ) it is the Bures-Wasserstein metric. We study two other cases: G(A; B) = A1=2(A−1=2BA−1=2)1=2A1=2; log A+log B the Pusz-Woronowicz geometric mean, and G(A; B) = exp 2 ; the log Euclidean mean. With these choices d(A; B) is no longer a metric, but it turns out that d2(A; B) is a divergence. We establish some (strict) convexity properties of these divergences. We obtain characterisations of barycentres of m positive definite matrices with respect to these distance measures. 1. Introduction Let p and q be two discrete probability distributions; i.e. p = (p1; : : : ; pn) and q = (q1; : : : ; qn) are n -vectors with nonnegative coordinates such that P P pi = qi = 1: The Hellinger distance between p and q is the Euclidean norm of the difference between the square roots of p and q ; i.e. 1=2 1=2 p p hX p p 2i hX X p i d(p; q) = k p− qk2 = ( pi − qi) = (pi + qi) − 2 piqi : (1) This distance and its continuous version, are much used in statistics, where it is customary to take d (p; q) = p1 d(p; q) as the definition of the Hellinger H 2 distance. -
On Some Extensions of Bernstein's Inequality for Self-Adjoint Operators
On Some Extensions of Bernstein's Inequality for Self-Adjoint Operators Stanislav Minsker∗ e-mail: [email protected] Abstract: We present some extensions of Bernstein's concentration inequality for random matrices. This inequality has become a useful and powerful tool for many problems in statis- tics, signal processing and theoretical computer science. The main feature of our bounds is that, unlike the majority of previous related results, they do not depend on the dimension d of the ambient space. Instead, the dimension factor is replaced by the “effective rank" associated with the underlying distribution that is bounded from above by d. In particular, this makes an extension to the infinite-dimensional setting possible. Our inequalities refine earlier results in this direction obtained by D. Hsu, S. M. Kakade and T. Zhang. Keywords and phrases: Bernstein's inequality, Random Matrix, Effective rank, Concen- tration inequality, Large deviations. 1. Introduction Theoretical analysis of many problems in applied mathematics can often be reduced to problems about random matrices. Examples include numerical linear algebra (randomized matrix decom- positions, approximate matrix multiplication), statistics and machine learning (low-rank matrix recovery, covariance estimation), mathematical signal processing, among others. n P Often, resulting questions can be reduced to estimates for the expectation E Xi − EXi or i=1 n P n probability P Xi − EXi > t , where fXigi=1 is a finite sequence of random matrices and i=1 k · k is the operator norm. Some of the early work on the subject includes the papers on noncom- mutative Khintchine inequalities [LPP91, LP86]; these tools were used by M. -
Matrices That Commute with a Permutation Matrix
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Matrices That Commute With a Permutation Matrix Jeffrey L. Stuart Department of Mathematics University of Southern Mississippi Hattiesburg, Mississippi 39406-5045 and James R. Weaver Department of Mathematics and Statistics University of West Florida Pensacola. Florida 32514 Submitted by Donald W. Robinson ABSTRACT Let P be an n X n permutation matrix, and let p be the corresponding permuta- tion. Let A be a matrix such that AP = PA. It is well known that when p is an n-cycle, A is permutation similar to a circulant matrix. We present results for the band patterns in A and for the eigenstructure of A when p consists of several disjoint cycles. These results depend on the greatest common divisors of pairs of cycle lengths. 1. INTRODUCTION A central theme in matrix theory concerns what can be said about a matrix if it commutes with a given matrix of some special type. In this paper, we investigate the combinatorial structure and the eigenstructure of a matrix that commutes with a permutation matrix. In doing so, we follow a long tradition of work on classes of matrices that commute with a permutation matrix of a specified type. In particular, our work is related both to work on the circulant matrices, the results for which can be found in Davis’s compre- hensive book [5], and to work on the centrosymmetric matrices, which can be found in the book by Aitken [l] and in the papers by Andrew [2], Bamett, LZNEAR ALGEBRA AND ITS APPLICATIONS 150:255-265 (1991) 255 Q Elsevier Science Publishing Co., Inc., 1991 655 Avenue of the Americas, New York, NY 10010 0024-3795/91/$3.50 256 JEFFREY L. -
ECE 487 Lecture 3 : Foundations of Quantum Mechanics II
ECE 487 Lecture 12 : Tools to Understand Nanotechnology III Class Outline: •Inverse Operator •Unitary Operators •Hermitian Operators Things you should know when you leave… Key Questions • What is an inverse operator? • What is the definition of a unitary operator? • What is a Hermetian operator and what do we use them for? M. J. Gilbert ECE 487 – Lecture 1 2 02/24/1 1 Tools to Understand Nanotechnology- III Let’s begin with a problem… Prove that the sum of the modulus squared of the matrix elements of a linear operator  is independent of the complete orthonormal basis used to represent the operator. M. J. Gilbert ECE 487 – Lecture 1 2 02/24/1 1 Tools to Understand Nanotechnology- III Last time we considered operators and how to represent them… •Most of the time we spent trying to understand the most simple of the operator classes we consider…the identity operator. •But he was only one of four operator classes to consider. 1. The identity operator…important for operator algebra. 2. Inverse operators…finding these often solves a physical problem mathematically and the are also important in operator algebra. 3. Unitary operators…these are very useful for changing the basis for representing the vectors and describing the evolution of quantum mechanical systems. 4. Hermitian operators…these are used to represent measurable quantities in quantum mechanics and they have some very powerful mathematical properties. M. J. Gilbert ECE 487 – Lecture 1 2 02/24/1 1 Tools to Understand Nanotechnology- III So, let’s move on to start discussing the inverse operator… Consider an operator  operating on some arbitrary function |f>. -
An Introduction to Functional Analysis for Science and Engineering David A
An introduction to functional analysis for science and engineering David A. B. Miller Stanford University This article is a tutorial introduction to the functional analysis mathematics needed in many physical problems, such as in waves in continuous media. This mathematics takes us beyond that of finite matrices, allowing us to work meaningfully with infinite sets of continuous functions. It resolves important issues, such as whether, why and how we can practically reduce such problems to finite matrix approximations. This branch of mathematics is well developed and its results are widely used in many areas of science and engineering. It is, however, difficult to find a readable introduction that both is deep enough to give a solid and reliable grounding but yet is efficient and comprehensible. To keep this introduction accessible and compact, I have selected only the topics necessary for the most important results, but the argument is mathematically complete and self-contained. Specifically, the article starts from elementary ideas of sets and sequences of real numbers. It then develops spaces of vectors or functions, introducing the concepts of norms and metrics that allow us to consider the idea of convergence of vectors and of functions. Adding the inner product, it introduces Hilbert spaces, and proceeds to the key forms of operators that map vectors or functions to other vectors or functions in the same or a different Hilbert space. This leads to the central concept of compact operators, which allows us to resolve many difficulties of working with infinite sets of vectors or functions. We then introduce Hilbert-Schmidt operators, which are compact operators encountered extensively in physical problems, such as those involving waves.