Math 4377/6308 Advanced Linear Algebra

Similar documents
Math 4377/6308 Advanced Linear Algebra

Math 4377/6308 Advanced Linear Algebra

Math 4377/6308 Advanced Linear Algebra

Math 4377/6308 Advanced Linear Algebra

Math 2331 Linear Algebra

Math 4377/6308 Advanced Linear Algebra

Math 4377/6308 Advanced Linear Algebra

Math 2331 Linear Algebra

Math 2331 Linear Algebra

4.3 Linear, Homogeneous Equations with Constant Coefficients. Jiwen He

Di erential Equations

Math 2331 Linear Algebra

Lecture 4. Section 2.5 The Pinching Theorem Section 2.6 Two Basic Properties of Continuity. Jiwen He. Department of Mathematics, University of Houston

Ir O D = D = ( ) Section 2.6 Example 1. (Bottom of page 119) dim(v ) = dim(l(v, W )) = dim(v ) dim(f ) = dim(v )

1.1 Differential Equation Models. Jiwen He

Linear Algebra Notes. Lecture Notes, University of Toronto, Fall 2016

Math 2331 Linear Algebra

MATH 323 Linear Algebra Lecture 12: Basis of a vector space (continued). Rank and nullity of a matrix.

Properties of the Determinant Function

Advanced Linear Algebra Math 4377 / 6308 (Spring 2015) March 5, 2015

Math 2331 Linear Algebra

7.5 Operations with Matrices. Copyright Cengage Learning. All rights reserved.

Evaluating Determinants by Row Reduction

Vector Spaces and SubSpaces

Chapter 1 Vector Spaces

Math 110, Spring 2015: Midterm Solutions

Linear Algebra Lecture Notes-I

Lecture 9. Section 3.4 Derivative as a Rate of Change Section 3.8 Rates of Change per Unit Time. Jiwen He

Math 2331 Linear Algebra

Math 2331 Linear Algebra

MATH 101A: ALGEBRA I PART C: TENSOR PRODUCT AND MULTILINEAR ALGEBRA. This is the title page for the notes on tensor products and multilinear algebra.

Math 2331 Linear Algebra

Math 2331 Linear Algebra

Lecture 21. Section 6.1 More on Area Section 6.2 Volume by Parallel Cross Section. Jiwen He. Department of Mathematics, University of Houston

Components and change of basis

MATH 423 Linear Algebra II Lecture 10: Inverse matrix. Change of coordinates.

LINEAR ALGEBRA BOOT CAMP WEEK 1: THE BASICS

Review 1 Math 321: Linear Algebra Spring 2010

Math 3191 Applied Linear Algebra

The following definition is fundamental.

Vector Spaces and Linear Transformations

Linear algebra 2. Yoav Zemel. March 1, 2012

MATH 323 Linear Algebra Lecture 6: Matrix algebra (continued). Determinants.

Math 321: Linear Algebra

MATH 423 Linear Algebra II Lecture 33: Diagonalization of normal operators.

MAC Module 2 Systems of Linear Equations and Matrices II. Learning Objectives. Upon completing this module, you should be able to :

Math 121 Homework 4: Notes on Selected Problems

Lecture Summaries for Linear Algebra M51A

Linear Algebra Practice Problems

Dimension. Eigenvalue and eigenvector

Mathematics 13: Lecture 10

MATH 240 Spring, Chapter 1: Linear Equations and Matrices

MATH 115A: SAMPLE FINAL SOLUTIONS

Elementary maths for GMT

Matrix operations Linear Algebra with Computer Science Application

Introduction to Matrix Algebra

10. Linear Systems of ODEs, Matrix multiplication, superposition principle (parts of sections )

Lecture notes - Math 110 Lec 002, Summer The reference [LADR] stands for Axler s Linear Algebra Done Right, 3rd edition.

Math 321: Linear Algebra

MATH 423 Linear Algebra II Lecture 12: Review for Test 1.

Math 60. Rumbos Spring Solutions to Assignment #17

MATH 304 Linear Algebra Lecture 10: Linear independence. Wronskian.

INTRODUCTION TO REPRESENTATION THEORY AND CHARACTERS

Linear and Bilinear Algebra (2WF04) Jan Draisma

MATH 423 Linear Algebra II Lecture 20: Geometry of linear transformations. Eigenvalues and eigenvectors. Characteristic polynomial.

If A is a 4 6 matrix and B is a 6 3 matrix then the dimension of AB is A. 4 6 B. 6 6 C. 4 3 D. 3 4 E. Undefined

Linear maps. Matthew Macauley. Department of Mathematical Sciences Clemson University Math 8530, Spring 2017

spring, math 204 (mitchell) list of theorems 1 Linear Systems Linear Transformations Matrix Algebra

Lecture 5. Ch. 5, Norms for vectors and matrices. Norms for vectors and matrices Why?

A classification of sharp tridiagonal pairs. Tatsuro Ito, Kazumasa Nomura, Paul Terwilliger

We simply compute: for v = x i e i, bilinearity of B implies that Q B (v) = B(v, v) is given by xi x j B(e i, e j ) =

Multilinear forms. Joel Kamnitzer. April 1, 2011

HONORS LINEAR ALGEBRA (MATH V 2020) SPRING 2013

LORENTZIAN MATRIX MULTIPLICATION AND THE MOTIONS ON LORENTZIAN PLANE. Kırıkkale University, Turkey

Properties of Linear Transformations from R n to R m

Definition 2.3. We define addition and multiplication of matrices as follows.

Row Space, Column Space, and Nullspace

Tridiagonal pairs in algebraic graph theory

Elementary Matrices. MATH 322, Linear Algebra I. J. Robert Buchanan. Spring Department of Mathematics

Linear Algebra Section 2.6 : LU Decomposition Section 2.7 : Permutations and transposes Wednesday, February 13th Math 301 Week #4

Math 443 Differential Geometry Spring Handout 3: Bilinear and Quadratic Forms This handout should be read just before Chapter 4 of the textbook.

Choose three of: Choose three of: Choose three of:

Contents. Preface for the Instructor. Preface for the Student. xvii. Acknowledgments. 1 Vector Spaces 1 1.A R n and C n 2

I = i 0,

MATH Topics in Applied Mathematics Lecture 12: Evaluation of determinants. Cross product.

CHAPTER V DUAL SPACES

7.6 The Inverse of a Square Matrix

JORDAN AND RATIONAL CANONICAL FORMS

Linear Inverse Problems. A MATLAB Tutorial Presented by Johnny Samuels


Lectures on Linear Algebra for IT

Matrix Algebra. Matrix Algebra. Chapter 8 - S&B

MATH 423 Linear Algebra II Lecture 11: Change of coordinates (continued). Isomorphism of vector spaces.

Leonard pairs and the q-tetrahedron algebra. Tatsuro Ito, Hjalmar Rosengren, Paul Terwilliger

Where is matrix multiplication locally open?

Matrix Algebra 2.1 MATRIX OPERATIONS Pearson Education, Inc.

Uniqueness of the Solutions of Some Completion Problems

5.1 Introduction to Matrices

Weeks 6 and 7. November 24, 2013

Chapter 2:Determinants. Section 2.1: Determinants by cofactor expansion

Transcription:

2.5 Change of Bases 2.6 Dual Spaces Math 4377/6308 Advanced Linear Algebra 2.5 Change of Bases & 2.6 Dual Spaces Jiwen He Department of Mathematics, University of Houston jiwenhe@math.uh.edu math.uh.edu/ jiwenhe/math4377 Jiwen He, University of Houston Math 4377/6308, Advanced Linear Algebra Spring, 2015 1 / 14

2.5 Change of Bases 2.6 Dual Spaces 2.5 Change of bases Change of Coordinate Matrices. Similar Matrices. Jiwen He, University of Houston Math 4377/6308, Advanced Linear Algebra Spring, 2015 2 / 14

2.5 Change of Bases 2.6 Dual Spaces The Change of Coordinate Matrix Theorem (2.22) Let β and β be ordered bases for a finite-dimensional vector space V, and let Q = [I V ] β β. Then (a) Q is invertible. (b) For any v V, [v] β = Q[v] β. Jiwen He, University of Houston Math 4377/6308, Advanced Linear Algebra Spring, 2015 3 / 14

2.5 Change of Bases 2.6 Dual Spaces The Change of Coordinate Matrix (cont.) Q = [I V ] β β is called a change of coordinate matrix, and we say that Q changes β -coordinates into β-coordinates. Note that if Q changes from β into β coordinates, then Q 1 changes from β into β coordinates. Jiwen He, University of Houston Math 4377/6308, Advanced Linear Algebra Spring, 2015 4 / 14

2.5 Change of Bases 2.6 Dual Spaces Linear Operators A linear operator is a linear transformation from a vector space V into itself. Theorem Let T be a linear operator on a finite-dimensional vector space V with ordered bases β, β. If Q is the change of coordinate matrix from β into β-coordinates, then [T ] β = Q 1 [T ] β Q Jiwen He, University of Houston Math 4377/6308, Advanced Linear Algebra Spring, 2015 5 / 14

2.5 Change of Bases 2.6 Dual Spaces Linear Operators (cont.) Corollary Let A M n n (F ), and γ an ordered basis for F n. Then [L A ] γ = Q 1 AQ, where Q is the n n matrix with the vectors in γ as column vectors. Definition For A, B M n n (F ), B is similar to A if the exists an invertible matrix Q such that B = Q 1 AQ. Jiwen He, University of Houston Math 4377/6308, Advanced Linear Algebra Spring, 2015 6 / 14

2.6 Dual Spaces Dual Spaces and Dual Bases Transposes Jiwen He, University of Houston Math 4377/6308, Advanced Linear Algebra Spring, 2015 7 / 14

Linear Functionals A linear functional on a vector space V is a linear transformation from V into its field of scalars F. Example Let V be the continuous real-valued functions on [0, 2π]. For a fix g V, a linear functional h : V R is given by Example h(x) = 1 2π x(t)g(t)dt 2π 0 Let V = M n n (F ), then f : V F with f (A) = tr(a) is a linear functional. Jiwen He, University of Houston Math 4377/6308, Advanced Linear Algebra Spring, 2015 8 / 14

Coordinate Functions Example Let β = {x 1,, x n } be a basis for a finite-dimensional vector space V. Define f i (x) = a i, where a 1 [x] β =. is the coordinate vector of x relative to β. Then f i is a linear functional on V called the ith coordinate function with respect to the basis β. Note that f i (x j ) = δ ij. a n Jiwen He, University of Houston Math 4377/6308, Advanced Linear Algebra Spring, 2015 9 / 14

Dual Spaces Definition For a vector space V over F, the dual space of V is the vector space V = L(V, F ). Note that for finite-dimensional V, dim(v ) = dim(l(v, F )) = dim(v ) dim(f ) = dim(v ) so V and V are isomorphic. Also, the double dual V of V is the dual of V. Jiwen He, University of Houston Math 4377/6308, Advanced Linear Algebra Spring, 2015 10 / 14

Dual Bases Theorem (2.24) Let β = {x 1,, x n } be an ordered basis for finite-dimensional vector space V, and let f i be the ith coordinate function w.r.t. β, and β = {f 1,, f n }. Then β is an ordered basis for V and for f V, n f = f (x i )f i. i=1 Definition The ordered basis β = {f 1,, f n } of V that satisfies f i (x j ) = δ ij is called the dual basis of β. Jiwen He, University of Houston Math 4377/6308, Advanced Linear Algebra Spring, 2015 11 / 14

Dual Bases (cont.) Theorem (2.25) Let V, W be finite-dimensional vector spaces over F with ordered bases β, γ. For any linear T : V W, the mapping T t : W V defined by T t (g) = gt for all g W is linear with the property [T t ] β γ = ([T ]γ β )t. Jiwen He, University of Houston Math 4377/6308, Advanced Linear Algebra Spring, 2015 12 / 14

Double Dual Isomorphism For a vector x V, define ˆx : V F by ˆx(f ) = f (x) for every f V. Note that ˆx is a linear functional on V, so ˆx V. Lemma For finite-dimensional vector space V and x V, if ˆx(f ) = 0 for all f V, then x = 0. Jiwen He, University of Houston Math 4377/6308, Advanced Linear Algebra Spring, 2015 13 / 14

Double Dual Isomorphism (cont.) Theorem (2.26) Let V be a finite-dimensional vector space, and define ψ : V V by ψ(x) = ˆx. Then ψ is an isomorphism. Corollary For finite-dimensional V with dual space V, every ordered basis for V is the dual basis for some basis for V. Jiwen He, University of Houston Math 4377/6308, Advanced Linear Algebra Spring, 2015 14 / 14