Linear Algebra using Dirac Notation: Pt. 2
|
|
- Clyde Todd
- 6 years ago
- Views:
Transcription
1 Linear Algebra using Dirac Notation: Pt. 2 PHYS 476Q - Southern Illinois University February 6, 2018 PHYS 476Q - Southern Illinois University Linear Algebra using Dirac Notation: Pt. 2 February 6, / 15
2 Adjoint Operators For every operator R L(H 1, H 2 ), there exists a unique operator R L(H 2, H 1 ) called the adjoint of R that is defined by the condition that β R α = α R β α H 1, β H 2. Bra-ket rule: ket: R β bra: β R. In terms of its matrix representation, we have j R i = i R j = R ij. The matrix for R is the conjugate transpose of the matrix for R. PHYS 476Q - Southern Illinois University Linear Algebra using Dirac Notation: Pt. 2 February 6, / 15
3 Adjoint Operators Example Let R = , where ± = 1/2( 0 ± i 1 ). What is R? PHYS 476Q - Southern Illinois University Linear Algebra using Dirac Notation: Pt. 2 February 6, / 15
4 Eigenvalues and Eigenvectors The eigenvalue of a linear operator R is a complex number λ such that R ψ = λ ψ for some vector ψ. For a fixed eigenvalue λ, the set V λ = { ϕ : R ϕ = λ ϕ } forms a vector space. This vector space V λ is called the eigenspace associated with λ (or the λ-eigenspace), and any vector from this space is an eigenvector of R with eigenvalue λ. The kernel (or null space) of an operator R is the eigenspace associated with the eigenvalue 0, and it is denoted by ker(r). The rank of an operator R : H 1 H 2, denoted rk(r) is the number given rk(r) = dim(h 1 ) dim(ker(r)). PHYS 476Q - Southern Illinois University Linear Algebra using Dirac Notation: Pt. 2 February 6, / 15
5 Important Classes of Operators The Picture: PHYS 476Q - Southern Illinois University Linear Algebra using Dirac Notation: Pt. 2 February 6, / 15
6 Projections A projection on a d-dimensional space H is any operator of the form P V = s ɛ i ɛ i, i=1 where the { ɛ i } s i=1 1 s d. is an orthonormal set of vectors for some integer The { ɛ i } s i=1 form a basis for some s-dimensional vector subspace V H, and P V is called the subspace projector onto V : P V ψ V for any ψ H. The identity I is the trivial projector onto all of H. PHYS 476Q - Southern Illinois University Linear Algebra using Dirac Notation: Pt. 2 February 6, / 15
7 Projections Example: For any normalized vector ψ, the operator ψ ψ is a rank-one projector. Example: Prove that every projector satisfies P 2 = P. Example: Prove that 0 and 1 are the only possible eigenvalues for a projector. PHYS 476Q - Southern Illinois University Linear Algebra using Dirac Notation: Pt. 2 February 6, / 15
8 Normal Operators An operator N L(H) is called normal if N N = NN. Lemma: If N L(H) is normal with distinct eigenvalues λ 1 and λ 2, then the associated eigenspaces V λ1 and V λ2 are orthogonal. Furthermore, H = λ i V λi, where the union is taken over all distinct eigenvalues of N. The eigenspace decomposition picture: PHYS 476Q - Southern Illinois University Linear Algebra using Dirac Notation: Pt. 2 February 6, / 15
9 Normal Operators: The Spectral Decomposition Spectral Decomposition Theorem: Every normal operator N L(H) can be uniquely written as N = n λ i P λi, i=1 where the {λ i } n i=1 are the distinct eigenvalues of N and the {P λ i } n i=1 are the corresponding eigenspace projectors. We can further decompose the eigenspace projectors and write: N = dim(h) k=1 Here the λ k are not necessarily distinct. λ k λ k λ k. PHYS 476Q - Southern Illinois University Linear Algebra using Dirac Notation: Pt. 2 February 6, / 15
10 Unitary Operators An operator U L(H) is called unitary if U U = UU = I. Example For arbitrary α, β, γ R the operator U(α, β, γ) on C 2 given by is unitary. U(α, β, γ). = ( e i(α+γ)/2 cos β/2 e i(α γ)/2 ) sin β/2 e i( α+γ)/2 sin β/2 e i(α+γ)/2 cos β/2 PHYS 476Q - Southern Illinois University Linear Algebra using Dirac Notation: Pt. 2 February 6, / 15
11 Unitary Operators Unitary operators preserve inner products: For vectors U ψ and U φ, their inner product is ψ U U φ = ψ I φ = ψ φ. If { ɛ i } d i=1 and { δ i } d i=1 are orthonormal bases for H, then U = d i=1 ɛ i δ i is a unitary operator transforming one basis to another U U = d d δ i ɛ i ɛ j δ j = i=1 j=1 d δ i δ i = I. i=1 The spectral decomposition can be written as N = UΛU, where Λ is a diagonal matrix in the computational basis with the eigenvalues of N along the diagonal. PHYS 476Q - Southern Illinois University Linear Algebra using Dirac Notation: Pt. 2 February 6, / 15
12 Hermitian and Positive Operators An operator A is called hermitian (or self-adjoint) if A = A. Fact: Every hermitian operator has real eigenvalues. Proof: A positive operator is any normal operator A with non-negative eigenvalues. Equivalently, A is positive iff ψ A ψ 0 for all ψ H. For any operator R L(H 1, H 2 ), the operators R R and RR are positive. PHYS 476Q - Southern Illinois University Linear Algebra using Dirac Notation: Pt. 2 February 6, / 15
13 Functions of Normal Operators The spectral decomposition allows us to define functions of operators. Notice that N = n λ k P λk N m = k=1 n λ m k P λ k for all m = 1,. Then for any complex function f (z) with Taylor expansion f (z) = k=0 α kz k, we define the operator function ˆf (N) := k=0 α kn k. Consequently, for a function f : X C, if N has eigenvalues lying in X, then we can define a new operator k=1 ˆf (N) = n f (λ i )P λi. i=1 PHYS 476Q - Southern Illinois University Linear Algebra using Dirac Notation: Pt. 2 February 6, / 15
14 Functions of Normal Operators Example: For the function f (z) = e z, we have ˆf (N) = e N = n e λ i P λi. i=1 For functions f (z) not defined at z = 0, define ˆf (N) = λ i 0 f (λ i)p λi. Example: For the multiplicative inverse f (z) = z 1 = 1 z, define N 1 := λ i 0 λ 1 i P λi. Note that N 1 N = NN 1 = λ i P λi λ 1 j P λj = P λi = P supp(n). λ i 0 λ j 0 λ i 0 where supp(n) := λi 0V λi is the support of N. PHYS 476Q - Southern Illinois University Linear Algebra using Dirac Notation: Pt. 2 February 6, / 15
15 Singular Value Decomposition Singular Value Decomposition Theorem: Every operator R L(H) can be written as R = n σ k UP σk, k=1 where {σ k } n k=1 are the distinct singular values of R, U is a unitary operator, and the {P σk } n k=1 are projections on the eigenspaces of R R. Equivalently, R can be written as R = V Λ σ W, where V and W are unitaries, and Λ σ is a diagonal matrix in the computational basis with diagonal elements being the singular values of R. PHYS 476Q - Southern Illinois University Linear Algebra using Dirac Notation: Pt. 2 February 6, / 15
Linear Algebra and Dirac Notation, Pt. 2
Linear Algebra and Dirac Notation, Pt. 2 PHYS 500 - Southern Illinois University February 1, 2017 PHYS 500 - Southern Illinois University Linear Algebra and Dirac Notation, Pt. 2 February 1, 2017 1 / 14
More informationLinear Algebra and Dirac Notation, Pt. 1
Linear Algebra and Dirac Notation, Pt. 1 PHYS 500 - Southern Illinois University February 1, 2017 PHYS 500 - Southern Illinois University Linear Algebra and Dirac Notation, Pt. 1 February 1, 2017 1 / 13
More informationQuantum Computing Lecture 2. Review of Linear Algebra
Quantum Computing Lecture 2 Review of Linear Algebra Maris Ozols Linear algebra States of a quantum system form a vector space and their transformations are described by linear operators Vector spaces
More informationLinear Algebra and Dirac Notation, Pt. 3
Linear Algebra and Dirac Notation, Pt. 3 PHYS 500 - Southern Illinois University February 1, 2017 PHYS 500 - Southern Illinois University Linear Algebra and Dirac Notation, Pt. 3 February 1, 2017 1 / 16
More informationThe Principles of Quantum Mechanics: Pt. 1
The Principles of Quantum Mechanics: Pt. 1 PHYS 476Q - Southern Illinois University February 15, 2018 PHYS 476Q - Southern Illinois University The Principles of Quantum Mechanics: Pt. 1 February 15, 2018
More informationLinear Algebra and the Basic Mathematical Tools for Finite-Dimensional Entanglement Theory
PHYS 476Q: An Introduction to Entanglement Theory (Spring 2018) Eric Chitambar Linear Algebra and the Basic Mathematical Tools for Finite-Dimensional Entanglement Theory Quantum mechanics describes nature
More informationMATH 423 Linear Algebra II Lecture 33: Diagonalization of normal operators.
MATH 423 Linear Algebra II Lecture 33: Diagonalization of normal operators. Adjoint operator and adjoint matrix Given a linear operator L on an inner product space V, the adjoint of L is a transformation
More informationLinear Algebra: Matrix Eigenvalue Problems
CHAPTER8 Linear Algebra: Matrix Eigenvalue Problems Chapter 8 p1 A matrix eigenvalue problem considers the vector equation (1) Ax = λx. 8.0 Linear Algebra: Matrix Eigenvalue Problems Here A is a given
More informationLinear Algebra 2 Spectral Notes
Linear Algebra 2 Spectral Notes In what follows, V is an inner product vector space over F, where F = R or C. We will use results seen so far; in particular that every linear operator T L(V ) has a complex
More informationAnalysis Preliminary Exam Workshop: Hilbert Spaces
Analysis Preliminary Exam Workshop: Hilbert Spaces 1. Hilbert spaces A Hilbert space H is a complete real or complex inner product space. Consider complex Hilbert spaces for definiteness. If (, ) : H H
More informationContents. Preface for the Instructor. Preface for the Student. xvii. Acknowledgments. 1 Vector Spaces 1 1.A R n and C n 2
Contents Preface for the Instructor xi Preface for the Student xv Acknowledgments xvii 1 Vector Spaces 1 1.A R n and C n 2 Complex Numbers 2 Lists 5 F n 6 Digression on Fields 10 Exercises 1.A 11 1.B Definition
More informationReal symmetric matrices/1. 1 Eigenvalues and eigenvectors
Real symmetric matrices 1 Eigenvalues and eigenvectors We use the convention that vectors are row vectors and matrices act on the right. Let A be a square matrix with entries in a field F; suppose that
More informationLecture 7: Positive Semidefinite Matrices
Lecture 7: Positive Semidefinite Matrices Rajat Mittal IIT Kanpur The main aim of this lecture note is to prepare your background for semidefinite programming. We have already seen some linear algebra.
More informationIntroduction to Quantum Mechanics Physics Thursday February 21, Problem # 1 (10pts) We are given the operator U(m, n) defined by
Department of Physics Introduction to Quantum Mechanics Physics 5701 Temple University Z.-E. Meziani Thursday February 1, 017 Problem # 1 10pts We are given the operator Um, n defined by Ûm, n φ m >< φ
More informationIr O D = D = ( ) Section 2.6 Example 1. (Bottom of page 119) dim(v ) = dim(l(v, W )) = dim(v ) dim(f ) = dim(v )
Section 3.2 Theorem 3.6. Let A be an m n matrix of rank r. Then r m, r n, and, by means of a finite number of elementary row and column operations, A can be transformed into the matrix ( ) Ir O D = 1 O
More informationMath 108b: Notes on the Spectral Theorem
Math 108b: Notes on the Spectral Theorem From section 6.3, we know that every linear operator T on a finite dimensional inner product space V has an adjoint. (T is defined as the unique linear operator
More informationLINEAR ALGEBRA SUMMARY SHEET.
LINEAR ALGEBRA SUMMARY SHEET RADON ROSBOROUGH https://intuitiveexplanationscom/linear-algebra-summary-sheet/ This document is a concise collection of many of the important theorems of linear algebra, organized
More informationMATH 20F: LINEAR ALGEBRA LECTURE B00 (T. KEMP)
MATH 20F: LINEAR ALGEBRA LECTURE B00 (T KEMP) Definition 01 If T (x) = Ax is a linear transformation from R n to R m then Nul (T ) = {x R n : T (x) = 0} = Nul (A) Ran (T ) = {Ax R m : x R n } = {b R m
More informationReview of Linear Algebra Definitions, Change of Basis, Trace, Spectral Theorem
Review of Linear Algebra Definitions, Change of Basis, Trace, Spectral Theorem Steven J. Miller June 19, 2004 Abstract Matrices can be thought of as rectangular (often square) arrays of numbers, or as
More informationEigenvalues and Eigenvectors
/88 Chia-Ping Chen Department of Computer Science and Engineering National Sun Yat-sen University Linear Algebra Eigenvalue Problem /88 Eigenvalue Equation By definition, the eigenvalue equation for matrix
More informationFormalism of Quantum Mechanics
Dirac Notation Formalism of Quantum Mechanics We can use a shorthand notation for the normalization integral I = "! (r,t) 2 dr = "! * (r,t)! (r,t) dr =!! The state! is called a ket. The complex conjugate
More informationLINEAR ALGEBRA BOOT CAMP WEEK 4: THE SPECTRAL THEOREM
LINEAR ALGEBRA BOOT CAMP WEEK 4: THE SPECTRAL THEOREM Unless otherwise stated, all vector spaces in this worksheet are finite dimensional and the scalar field F is R or C. Definition 1. A linear operator
More informationMathematical Methods wk 2: Linear Operators
John Magorrian, magog@thphysoxacuk These are work-in-progress notes for the second-year course on mathematical methods The most up-to-date version is available from http://www-thphysphysicsoxacuk/people/johnmagorrian/mm
More informationMATHEMATICS 217 NOTES
MATHEMATICS 27 NOTES PART I THE JORDAN CANONICAL FORM The characteristic polynomial of an n n matrix A is the polynomial χ A (λ) = det(λi A), a monic polynomial of degree n; a monic polynomial in the variable
More informationMATH 583A REVIEW SESSION #1
MATH 583A REVIEW SESSION #1 BOJAN DURICKOVIC 1. Vector Spaces Very quick review of the basic linear algebra concepts (see any linear algebra textbook): (finite dimensional) vector space (or linear space),
More informationLecture notes on Quantum Computing. Chapter 1 Mathematical Background
Lecture notes on Quantum Computing Chapter 1 Mathematical Background Vector states of a quantum system with n physical states are represented by unique vectors in C n, the set of n 1 column vectors 1 For
More informationFoundations of Matrix Analysis
1 Foundations of Matrix Analysis In this chapter we recall the basic elements of linear algebra which will be employed in the remainder of the text For most of the proofs as well as for the details, the
More informationLecture 2: Linear operators
Lecture 2: Linear operators Rajat Mittal IIT Kanpur The mathematical formulation of Quantum computing requires vector spaces and linear operators So, we need to be comfortable with linear algebra to study
More informationFunctional Analysis Review
Outline 9.520: Statistical Learning Theory and Applications February 8, 2010 Outline 1 2 3 4 Vector Space Outline A vector space is a set V with binary operations +: V V V and : R V V such that for all
More informationLinear Algebra Review. Vectors
Linear Algebra Review 9/4/7 Linear Algebra Review By Tim K. Marks UCSD Borrows heavily from: Jana Kosecka http://cs.gmu.edu/~kosecka/cs682.html Virginia de Sa (UCSD) Cogsci 8F Linear Algebra review Vectors
More informationBASIC ALGORITHMS IN LINEAR ALGEBRA. Matrices and Applications of Gaussian Elimination. A 2 x. A T m x. A 1 x A T 1. A m x
BASIC ALGORITHMS IN LINEAR ALGEBRA STEVEN DALE CUTKOSKY Matrices and Applications of Gaussian Elimination Systems of Equations Suppose that A is an n n matrix with coefficents in a field F, and x = (x,,
More informationLecture 8 : Eigenvalues and Eigenvectors
CPS290: Algorithmic Foundations of Data Science February 24, 2017 Lecture 8 : Eigenvalues and Eigenvectors Lecturer: Kamesh Munagala Scribe: Kamesh Munagala Hermitian Matrices It is simpler to begin with
More informationThen x 1,..., x n is a basis as desired. Indeed, it suffices to verify that it spans V, since n = dim(v ). We may write any v V as r
Practice final solutions. I did not include definitions which you can find in Axler or in the course notes. These solutions are on the terse side, but would be acceptable in the final. However, if you
More informationMath 113 Final Exam: Solutions
Math 113 Final Exam: Solutions Thursday, June 11, 2013, 3.30-6.30pm. 1. (25 points total) Let P 2 (R) denote the real vector space of polynomials of degree 2. Consider the following inner product on P
More informationNumerical Methods for Solving Large Scale Eigenvalue Problems
Peter Arbenz Computer Science Department, ETH Zürich E-mail: arbenz@inf.ethz.ch arge scale eigenvalue problems, Lecture 2, February 28, 2018 1/46 Numerical Methods for Solving Large Scale Eigenvalue Problems
More informationThe Spectral Theorem for normal linear maps
MAT067 University of California, Davis Winter 2007 The Spectral Theorem for normal linear maps Isaiah Lankham, Bruno Nachtergaele, Anne Schilling (March 14, 2007) In this section we come back to the question
More informationALGEBRA QUALIFYING EXAM PROBLEMS LINEAR ALGEBRA
ALGEBRA QUALIFYING EXAM PROBLEMS LINEAR ALGEBRA Kent State University Department of Mathematical Sciences Compiled and Maintained by Donald L. White Version: August 29, 2017 CONTENTS LINEAR ALGEBRA AND
More informationReview problems for MA 54, Fall 2004.
Review problems for MA 54, Fall 2004. Below are the review problems for the final. They are mostly homework problems, or very similar. If you are comfortable doing these problems, you should be fine on
More information1. General Vector Spaces
1.1. Vector space axioms. 1. General Vector Spaces Definition 1.1. Let V be a nonempty set of objects on which the operations of addition and scalar multiplication are defined. By addition we mean a rule
More informationSummary of Week 9 B = then A A =
Summary of Week 9 Finding the square root of a positive operator Last time we saw that positive operators have a unique positive square root We now briefly look at how one would go about calculating the
More informationMath 102, Winter Final Exam Review. Chapter 1. Matrices and Gaussian Elimination
Math 0, Winter 07 Final Exam Review Chapter. Matrices and Gaussian Elimination { x + x =,. Different forms of a system of linear equations. Example: The x + 4x = 4. [ ] [ ] [ ] vector form (or the column
More informationGlossary of Linear Algebra Terms. Prepared by Vince Zaccone For Campus Learning Assistance Services at UCSB
Glossary of Linear Algebra Terms Basis (for a subspace) A linearly independent set of vectors that spans the space Basic Variable A variable in a linear system that corresponds to a pivot column in the
More informationLINEAR ALGEBRA REVIEW
LINEAR ALGEBRA REVIEW JC Stuff you should know for the exam. 1. Basics on vector spaces (1) F n is the set of all n-tuples (a 1,... a n ) with a i F. It forms a VS with the operations of + and scalar multiplication
More informationSPECTRAL THEOREM FOR COMPACT SELF-ADJOINT OPERATORS
SPECTRAL THEOREM FOR COMPACT SELF-ADJOINT OPERATORS G. RAMESH Contents Introduction 1 1. Bounded Operators 1 1.3. Examples 3 2. Compact Operators 5 2.1. Properties 6 3. The Spectral Theorem 9 3.3. Self-adjoint
More information1 Algebra of State Vectors
J. Rothberg October 6, Introduction to Quantum Mechanics: Part Algebra of State Vectors What does the State Vector mean? A state vector is not a property of a physical system, but rather represents an
More informationLECTURE VI: SELF-ADJOINT AND UNITARY OPERATORS MAT FALL 2006 PRINCETON UNIVERSITY
LECTURE VI: SELF-ADJOINT AND UNITARY OPERATORS MAT 204 - FALL 2006 PRINCETON UNIVERSITY ALFONSO SORRENTINO 1 Adjoint of a linear operator Note: In these notes, V will denote a n-dimensional euclidean vector
More informationlinearly indepedent eigenvectors as the multiplicity of the root, but in general there may be no more than one. For further discussion, assume matrice
3. Eigenvalues and Eigenvectors, Spectral Representation 3.. Eigenvalues and Eigenvectors A vector ' is eigenvector of a matrix K, if K' is parallel to ' and ' 6, i.e., K' k' k is the eigenvalue. If is
More informationPhysics 215 Quantum Mechanics 1 Assignment 1
Physics 5 Quantum Mechanics Assignment Logan A. Morrison January 9, 06 Problem Prove via the dual correspondence definition that the hermitian conjugate of α β is β α. By definition, the hermitian conjugate
More informationDimension. Eigenvalue and eigenvector
Dimension. Eigenvalue and eigenvector Math 112, week 9 Goals: Bases, dimension, rank-nullity theorem. Eigenvalue and eigenvector. Suggested Textbook Readings: Sections 4.5, 4.6, 5.1, 5.2 Week 9: Dimension,
More informationLinear Algebra- Final Exam Review
Linear Algebra- Final Exam Review. Let A be invertible. Show that, if v, v, v 3 are linearly independent vectors, so are Av, Av, Av 3. NOTE: It should be clear from your answer that you know the definition.
More informationSingular Value Decomposition (SVD) and Polar Form
Chapter 2 Singular Value Decomposition (SVD) and Polar Form 2.1 Polar Form In this chapter, we assume that we are dealing with a real Euclidean space E. Let f: E E be any linear map. In general, it may
More informationThroughout these notes we assume V, W are finite dimensional inner product spaces over C.
Math 342 - Linear Algebra II Notes Throughout these notes we assume V, W are finite dimensional inner product spaces over C 1 Upper Triangular Representation Proposition: Let T L(V ) There exists an orthonormal
More informationLecture 3: Hilbert spaces, tensor products
CS903: Quantum computation and Information theory (Special Topics In TCS) Lecture 3: Hilbert spaces, tensor products This lecture will formalize many of the notions introduced informally in the second
More informationBackground Mathematics (2/2) 1. David Barber
Background Mathematics (2/2) 1 David Barber University College London Modified by Samson Cheung (sccheung@ieee.org) 1 These slides accompany the book Bayesian Reasoning and Machine Learning. The book and
More informationLinear algebra 2. Yoav Zemel. March 1, 2012
Linear algebra 2 Yoav Zemel March 1, 2012 These notes were written by Yoav Zemel. The lecturer, Shmuel Berger, should not be held responsible for any mistake. Any comments are welcome at zamsh7@gmail.com.
More informationReview of some mathematical tools
MATHEMATICAL FOUNDATIONS OF SIGNAL PROCESSING Fall 2016 Benjamín Béjar Haro, Mihailo Kolundžija, Reza Parhizkar, Adam Scholefield Teaching assistants: Golnoosh Elhami, Hanjie Pan Review of some mathematical
More informationChapter 7. Canonical Forms. 7.1 Eigenvalues and Eigenvectors
Chapter 7 Canonical Forms 7.1 Eigenvalues and Eigenvectors Definition 7.1.1. Let V be a vector space over the field F and let T be a linear operator on V. An eigenvalue of T is a scalar λ F such that there
More informationLinear Algebra (Review) Volker Tresp 2018
Linear Algebra (Review) Volker Tresp 2018 1 Vectors k, M, N are scalars A one-dimensional array c is a column vector. Thus in two dimensions, ( ) c1 c = c 2 c i is the i-th component of c c T = (c 1, c
More informationMATH 581D FINAL EXAM Autumn December 12, 2016
MATH 58D FINAL EXAM Autumn 206 December 2, 206 NAME: SIGNATURE: Instructions: there are 6 problems on the final. Aim for solving 4 problems, but do as much as you can. Partial credit will be given on all
More informationSymmetric and anti symmetric matrices
Symmetric and anti symmetric matrices In linear algebra, a symmetric matrix is a square matrix that is equal to its transpose. Formally, matrix A is symmetric if. A = A Because equal matrices have equal
More informationMATH 240 Spring, Chapter 1: Linear Equations and Matrices
MATH 240 Spring, 2006 Chapter Summaries for Kolman / Hill, Elementary Linear Algebra, 8th Ed. Sections 1.1 1.6, 2.1 2.2, 3.2 3.8, 4.3 4.5, 5.1 5.3, 5.5, 6.1 6.5, 7.1 7.2, 7.4 DEFINITIONS Chapter 1: Linear
More informationFinal Exam, Linear Algebra, Fall, 2003, W. Stephen Wilson
Final Exam, Linear Algebra, Fall, 2003, W. Stephen Wilson Name: TA Name and section: NO CALCULATORS, SHOW ALL WORK, NO OTHER PAPERS ON DESK. There is very little actual work to be done on this exam if
More informationChap 3. Linear Algebra
Chap 3. Linear Algebra Outlines 1. Introduction 2. Basis, Representation, and Orthonormalization 3. Linear Algebraic Equations 4. Similarity Transformation 5. Diagonal Form and Jordan Form 6. Functions
More informationMATRICES ARE SIMILAR TO TRIANGULAR MATRICES
MATRICES ARE SIMILAR TO TRIANGULAR MATRICES 1 Complex matrices Recall that the complex numbers are given by a + ib where a and b are real and i is the imaginary unity, ie, i 2 = 1 In what we describe below,
More informationElementary linear algebra
Chapter 1 Elementary linear algebra 1.1 Vector spaces Vector spaces owe their importance to the fact that so many models arising in the solutions of specific problems turn out to be vector spaces. The
More informationLinear Algebra 1. M.T.Nair Department of Mathematics, IIT Madras. and in that case x is called an eigenvector of T corresponding to the eigenvalue λ.
Linear Algebra 1 M.T.Nair Department of Mathematics, IIT Madras 1 Eigenvalues and Eigenvectors 1.1 Definition and Examples Definition 1.1. Let V be a vector space (over a field F) and T : V V be a linear
More informationThe quantum state as a vector
The quantum state as a vector February 6, 27 Wave mechanics In our review of the development of wave mechanics, we have established several basic properties of the quantum description of nature:. A particle
More information33AH, WINTER 2018: STUDY GUIDE FOR FINAL EXAM
33AH, WINTER 2018: STUDY GUIDE FOR FINAL EXAM (UPDATED MARCH 17, 2018) The final exam will be cumulative, with a bit more weight on more recent material. This outline covers the what we ve done since the
More informationNovember 18, 2013 ANALYTIC FUNCTIONAL CALCULUS
November 8, 203 ANALYTIC FUNCTIONAL CALCULUS RODICA D. COSTIN Contents. The spectral projection theorem. Functional calculus 2.. The spectral projection theorem for self-adjoint matrices 2.2. The spectral
More informationExample Linear Algebra Competency Test
Example Linear Algebra Competency Test The 4 questions below are a combination of True or False, multiple choice, fill in the blank, and computations involving matrices and vectors. In the latter case,
More informationQuantum Physics II (8.05) Fall 2002 Assignment 3
Quantum Physics II (8.05) Fall 00 Assignment Readings The readings below will take you through the material for Problem Sets and 4. Cohen-Tannoudji Ch. II, III. Shankar Ch. 1 continues to be helpful. Sakurai
More informationPrinciples of Quantum Mechanics Pt. 2
Principles of Quantum Mechanics Pt. 2 PHYS 500 - Southern Illinois University February 9, 2017 PHYS 500 - Southern Illinois University Principles of Quantum Mechanics Pt. 2 February 9, 2017 1 / 13 The
More informationLinGloss. A glossary of linear algebra
LinGloss A glossary of linear algebra Contents: Decompositions Types of Matrices Theorems Other objects? Quasi-triangular A matrix A is quasi-triangular iff it is a triangular matrix except its diagonal
More informationMath 350 Fall 2011 Notes about inner product spaces. In this notes we state and prove some important properties of inner product spaces.
Math 350 Fall 2011 Notes about inner product spaces In this notes we state and prove some important properties of inner product spaces. First, recall the dot product on R n : if x, y R n, say x = (x 1,...,
More information2. Introduction to quantum mechanics
2. Introduction to quantum mechanics 2.1 Linear algebra Dirac notation Complex conjugate Vector/ket Dual vector/bra Inner product/bracket Tensor product Complex conj. matrix Transpose of matrix Hermitian
More information2. Linear algebra. matrices and vectors. linear equations. range and nullspace of matrices. function of vectors, gradient and Hessian
FE661 - Statistical Methods for Financial Engineering 2. Linear algebra Jitkomut Songsiri matrices and vectors linear equations range and nullspace of matrices function of vectors, gradient and Hessian
More informationSpectral Theorem for Self-adjoint Linear Operators
Notes for the undergraduate lecture by David Adams. (These are the notes I would write if I was teaching a course on this topic. I have included more material than I will cover in the 45 minute lecture;
More informationAPPENDIX A. Background Mathematics. A.1 Linear Algebra. Vector algebra. Let x denote the n-dimensional column vector with components x 1 x 2.
APPENDIX A Background Mathematics A. Linear Algebra A.. Vector algebra Let x denote the n-dimensional column vector with components 0 x x 2 B C @. A x n Definition 6 (scalar product). The scalar product
More informationMathematical Introduction
Chapter 1 Mathematical Introduction HW #1: 164, 165, 166, 181, 182, 183, 1811, 1812, 114 11 Linear Vector Spaces: Basics 111 Field A collection F of elements a,b etc (also called numbers or scalars) with
More informationPRACTICE FINAL EXAM. why. If they are dependent, exhibit a linear dependence relation among them.
Prof A Suciu MTH U37 LINEAR ALGEBRA Spring 2005 PRACTICE FINAL EXAM Are the following vectors independent or dependent? If they are independent, say why If they are dependent, exhibit a linear dependence
More informationThe Framework of Quantum Mechanics
The Framework of Quantum Mechanics We now use the mathematical formalism covered in the last lecture to describe the theory of quantum mechanics. In the first section we outline four axioms that lie at
More informationChapter III. Quantum Computation. Mathematical preliminaries. A.1 Complex numbers. A.2 Linear algebra review
Chapter III Quantum Computation These lecture notes are exclusively for the use of students in Prof. MacLennan s Unconventional Computation course. c 2017, B. J. MacLennan, EECS, University of Tennessee,
More informationMath 4153 Exam 3 Review. The syllabus for Exam 3 is Chapter 6 (pages ), Chapter 7 through page 137, and Chapter 8 through page 182 in Axler.
Math 453 Exam 3 Review The syllabus for Exam 3 is Chapter 6 (pages -2), Chapter 7 through page 37, and Chapter 8 through page 82 in Axler.. You should be sure to know precise definition of the terms we
More informationChapter 3 Transformations
Chapter 3 Transformations An Introduction to Optimization Spring, 2014 Wei-Ta Chu 1 Linear Transformations A function is called a linear transformation if 1. for every and 2. for every If we fix the bases
More informationC.6 Adjoints for Operators on Hilbert Spaces
C.6 Adjoints for Operators on Hilbert Spaces 317 Additional Problems C.11. Let E R be measurable. Given 1 p and a measurable weight function w: E (0, ), the weighted L p space L p s (R) consists of all
More informationThe University of Texas at Austin Department of Electrical and Computer Engineering. EE381V: Large Scale Learning Spring 2013.
The University of Texas at Austin Department of Electrical and Computer Engineering EE381V: Large Scale Learning Spring 2013 Assignment Two Caramanis/Sanghavi Due: Tuesday, Feb. 19, 2013. Computational
More informationThe Singular Value Decomposition
The Singular Value Decomposition Philippe B. Laval KSU Fall 2015 Philippe B. Laval (KSU) SVD Fall 2015 1 / 13 Review of Key Concepts We review some key definitions and results about matrices that will
More informationDesigning Information Devices and Systems II
EECS 16B Fall 2016 Designing Information Devices and Systems II Linear Algebra Notes Introduction In this set of notes, we will derive the linear least squares equation, study the properties symmetric
More informationLecture notes: Applied linear algebra Part 1. Version 2
Lecture notes: Applied linear algebra Part 1. Version 2 Michael Karow Berlin University of Technology karow@math.tu-berlin.de October 2, 2008 1 Notation, basic notions and facts 1.1 Subspaces, range and
More information08a. Operators on Hilbert spaces. 1. Boundedness, continuity, operator norms
(February 24, 2017) 08a. Operators on Hilbert spaces Paul Garrett garrett@math.umn.edu http://www.math.umn.edu/ garrett/ [This document is http://www.math.umn.edu/ garrett/m/real/notes 2016-17/08a-ops
More informationMAT Linear Algebra Collection of sample exams
MAT 342 - Linear Algebra Collection of sample exams A-x. (0 pts Give the precise definition of the row echelon form. 2. ( 0 pts After performing row reductions on the augmented matrix for a certain system
More informationEigenvectors and Hermitian Operators
7 71 Eigenvalues and Eigenvectors Basic Definitions Let L be a linear operator on some given vector space V A scalar λ and a nonzero vector v are referred to, respectively, as an eigenvalue and corresponding
More informationNATIONAL UNIVERSITY OF SINGAPORE DEPARTMENT OF MATHEMATICS SEMESTER 2 EXAMINATION, AY 2010/2011. Linear Algebra II. May 2011 Time allowed :
NATIONAL UNIVERSITY OF SINGAPORE DEPARTMENT OF MATHEMATICS SEMESTER 2 EXAMINATION, AY 2010/2011 Linear Algebra II May 2011 Time allowed : 2 hours INSTRUCTIONS TO CANDIDATES 1. This examination paper contains
More information235 Final exam review questions
5 Final exam review questions Paul Hacking December 4, 0 () Let A be an n n matrix and T : R n R n, T (x) = Ax the linear transformation with matrix A. What does it mean to say that a vector v R n is an
More information1. The Polar Decomposition
A PERSONAL INTERVIEW WITH THE SINGULAR VALUE DECOMPOSITION MATAN GAVISH Part. Theory. The Polar Decomposition In what follows, F denotes either R or C. The vector space F n is an inner product space with
More informationREAL ANALYSIS II HOMEWORK 3. Conway, Page 49
REAL ANALYSIS II HOMEWORK 3 CİHAN BAHRAN Conway, Page 49 3. Let K and k be as in Proposition 4.7 and suppose that k(x, y) k(y, x). Show that K is self-adjoint and if {µ n } are the eigenvalues of K, each
More informationQuantum Statistics -First Steps
Quantum Statistics -First Steps Michael Nussbaum 1 November 30, 2007 Abstract We will try an elementary introduction to quantum probability and statistics, bypassing the physics in a rapid first glance.
More information1 Linear Algebra Problems
Linear Algebra Problems. Let A be the conjugate transpose of the complex matrix A; i.e., A = A t : A is said to be Hermitian if A = A; real symmetric if A is real and A t = A; skew-hermitian if A = A and
More informationA Brief Introduction to Functional Analysis
A Brief Introduction to Functional Analysis Sungwook Lee Department of Mathematics University of Southern Mississippi sunglee@usm.edu July 5, 2007 Definition 1. An algebra A is a vector space over C with
More informationMathematical foundations - linear algebra
Mathematical foundations - linear algebra Andrea Passerini passerini@disi.unitn.it Machine Learning Vector space Definition (over reals) A set X is called a vector space over IR if addition and scalar
More information