# Introduction to Matrix Algebra

Save this PDF as:

Size: px
Start display at page:

## Transcription

1 Psychology 7291: Multivariate Statistics (Carey) 8/27/98 Matrix Algebra - 1 Introduction to Matrix Algebra Definitions: A matrix is a collection of numbers ordered by rows and columns. It is customary to enclose the elements of a matrix in parentheses, brackets, or braces. For example, the following is a matrix: X = This matrix has two rows and three columns, so it is referred to as a 2 by 3 matrix. The elements of a matrix are numbered in the following way: x 11 x 12 x 13 X = x 21 x 22 x 23 That is, the first subscript in a matrix refers to the row and the second subscript refers to the column. It is important to remember this convention when matrix algebra is performed. A vector is a special type of matrix that has only one row (called a row vector) or one column (called a column vector). Below, a is a column vector while b is a row vector. 7 a = 2, b = ( ) 3 A scalar is a matrix with only one row and one column. It is customary to denote scalars by italicized, lower case letters (e.g., x), to denote vectors by bold, lower case letters (e.g., x), and to denote matrices with more than one row and one column by bold, upper case letters (e.g., X). A square matrix has as many rows as it has columns. Matrix A is square but matrix B is not square: A = , B = A symmetric matrix is a square matrix in which x ij = x ji for all i and j. Matrix A is symmetric; matrix B is not symmetric A = 1 6 2, B = A diagonal matrix is a symmetric matrix where all the off diagonal elements are 0. Matrix A is diagonal.

2 Psychology 7291: Multivariate Statistics (Carey) 8/27/98 Matrix Algebra A = An identity matrix is a diagonal matrix with 1s and only 1s on the diagonal. The identity matrix is almost always denoted as I I = Matrix Addition and Subtraction: To add two matrices, they both must have the same number of rows and they both must have the same number of columns. The elements of the two matrices are simply added together, element by element, to produce the results. That is, for R = A+ B, then r ij = a ij + b ij for all i and j. Thus, = Matrix subtraction works in the same way, except that elements are subtracted instead of added. Matrix Multiplication: There are several rules for matrix multiplication. The first concerns the multiplication between a matrix and a scalar. Here, each element in the product matrix is simply the scalar multiplied by the element in the matrix. That is, for R = ab, then r ij = ab ij for all i and j. Thus, = Matrix multiplication involving a scalar is commutative. That is, ab = Ba. The next rule involves the multiplication of a row vector by a column vector. To perform this, the row vector must have as many columns as the column vector has rows. For example, 2 ( 1 7 5) 4 1 is legal. However

3 Psychology 7291: Multivariate Statistics (Carey) 8/27/98 Matrix Algebra ( 1 7 5) 1 6 is not legal because the row vector has three columns while the column vector has four rows. The product of a row vector multiplied by a column vector will be a scalar. This scalar is simply the sum of the first row vector element multiplied by the first column vector element plus the second row vector element multiplied by the second column vector element plus the product of the third elements, etc. In algebra, if r = ab, then n r = a i b i i =1 Thus, 8 ( 2 6 3) 1 = = 34 4 All other types of matrix multiplication involve the multiplication of a row vector and a column vector. Specifically, in the expression R = AB, r ij = a i b j where a i is the ith row vector in matrix A and b j is the jth column vector in matrix B. Thus, if A = ,and B = then 1 r 11 = a 1 b 1 = ( 2 8 1) 9 = = 80 6 and 7 r 12 = a 1 b 2 = ( 2 8 1) 2 = ( 2) = 1 3 and 1 r 21 = a 2 b 1 = ( 3 6 4) 9 = = 81 6 and

4 Psychology 7291: Multivariate Statistics (Carey) 8/27/98 Matrix Algebra r 22 = a 2 b 2 = ( 3 6 4) 2 = ( 2) = 21 3 Hence, = For matrix multiplication to be legal, the first matrix must have as many columns as the second matrix has rows. This, of course, is the requirement for multiplying a row vector by a column vector. The resulting matrix will have as many rows as the first matrix and as many columns as the second matrix. Because A has 2 rows and 3 columns while B has 3 rows and 2 columns, the matrix multiplication may legally proceed and the resulting matrix will have 2 rows and 2 columns. Because of these requirements, matrix multiplication is usually not commutative. That is, usually AB BA. And even if AB is a legal operation, there is no guarantee that BA will also be legal. For these reasons, the terms premultiply and postmultiply are often encountered in matrix algebra while they are seldom encountered in scalar algebra. One special case to be aware of is when a column vector is postmultiplied by a row vector. That is, what is 3 4 ( 8 2 )? 7 In this case, one simply follows the rules given above for the multiplication of two matrices. Note that the first matrix has one column and the second matrix has one row, so the matrix multiplication is legal. The resulting matrix will have as many rows as the first matrix (3) and as many columns as the second matrix (2). Hence, the result is ( 8 2 ) = Similarly, multiplication of a matrix times a vector (or a vector times a matrix) will also conform to the multiplication of two matrices. For example, is an illegal operation because the number of columns in the first matrix (2) does not match the number of rows in the second matrix (3). However,

5 Psychology 7291: Multivariate Statistics (Carey) 8/27/98 Matrix Algebra = = and 8 5 ( 2 7 3) 6 1 = ( ) = ( 85 29) 9 4 The last special case of matrix multiplication involves the identity matrix, I. The identity matrix operates as the number 1 does in scalar algebra. That is, any vector or matrix multiplied by an identity matrix is simply the original vector or matrix. Hence, ai = a, IX = X, etc. Note, however, that a scalar multiplied by an identify matrix becomes a diagonal matrix with the scalars on the diagonal. That is, = not 4. This should be verified by reviewing the rules for multiplying a scalar and a matrix given above. Matrix Transpose: The transpose of a matrix is denoted by a prime ( A ) or a superscript t or T ( A t or A T ). The first row of a matrix becomes the first column of the transpose matrix, the second row of the matrix becomes the second column of the transpose, etc. Thus, A = , and A t = The transpose of a row vector will be a column vector, and the transpose of a column vector will be a row vector. The transpose of a symmetric matrix is simply the original matrix. Matrix Inverse: In scalar algebra, the inverse of a number is that number which, when multiplied by the original number, gives a product of 1. Hence, the inverse of x is simple 1/x. or, in slightly different notation, x 1. In matrix algebra, the inverse of a matrix is that matrix which, when multiplied by the original matrix, gives an identity matrix. The inverse of a matrix is denoted by the superscript -1. Hence, AA 1 = A 1 A = I A matrix must be square to have an inverse, but not all square matrices have an inverse. In some cases, the inverse does not exist. For covariance and correlation matrices, an inverse will always exist, provided that there are more subjects than there are variables and that every variable has a variance greater than 0.

6 Psychology 7291: Multivariate Statistics (Carey) 8/27/98 Matrix Algebra - 6 It is important to know what an inverse is in multivariate statistics, but it is not necessary to know how to compute an inverse. Determinant of a Matrix: The determinant of a matrix is a scalar and is denoted as A or det(a). The determinant has very important mathematical properties, but it is very difficult to provide a substantive definition. For covariance and correlation matrices, the determinant is a number that is sometimes used to express the generalized variance of the matrix. That is, covariance matrices with small determinants denote variables that are redundant or highly correlated. Matrices with large determinants denote variables that are independent of one another. The determinant has several very important properties for some multivariate stats (e.g., change in R 2 in multiple regression can be expressed as a ratio of determinants.) Only idiots calculate the determinant of a large matrix by hand. We will try to avoid them. Trace of a Matrix: The trace of a matrix is sometimes, although not always, denoted as tr(a). The trace is used only for square matrices and equals the sum of the diagonal elements of the matrix. For example, tr = = Orthogonal Matrices: Only square matrices may be orthogonal matrices, although not all square matrices are orthogonal matrices. An orthogonal matrix satisfied the equation AA t = I Thus, the inverse of an orthogonal matrix is simply the transpose of that matrix. Orthogonal matrices are very important in factor analysis. Matrices of eigenvectors (discussed below) are orthogonal matrices. Eigenvalues and Eigenvectors The eigenvalues and eigenvectors of a matrix play an important part in multivariate analysis. This discussion applies to correlation matrices and covariance matrices that (1) have more subjects than variables, (2) have variances > 0.0, and (3) are calculated from data having no missing values, and (4) no variable is a perfect linear combination of the other variables. Any such covariance matrix C can be mathematically decomposed into a product:

7 Psychology 7291: Multivariate Statistics (Carey) 8/27/98 Matrix Algebra - 7 C = ADA -1 where A is a square matrix of eigenvectors and D is a diagonal matrix with the eigenvalues on the diagonal. If there are n variables, both A and D will be n by n matrices. Eigenvalues are also called characteristic roots or latent roots. Eigenvectors are sometimes refereed to as characteristic vectors or latent vectors. Each eigenvalue has its associated eigenvector. That is, the first eigenvalue in D (d 11 ) is associated with the first column vector in A, the second diagonal element in D (i.e., the second eigenvalue or d 22 ) is associated with the second column in A, and so on. Actually, the order of the eigenvalues is arbitrary from a mathematical viewpoint. However, if the diagonals of D become switched around, then the corresponding columns in A must also be switched appropriately. It is customary to order the eigenvalues so that the largest one is in the upper left (d 11 ) and then they proceed in descending order until the smallest one is in d nn, or the extreme lower right. The eigenvectors in A are then ordered accordingly so that column 1 in A is associated with the largest eigenvalue and column n is associated with the lowest eigenvalue. Some important points about eigenvectors and eigenvalues are: 1) The eigenvectors are scaled so that A is an orthogonal matrix. Thus, A T = A -1, and AA T = I. Thus, each eigenvector is said to be orthogonal to all the other eigenvectors. 2) The eigenvalues will all be greater than 0.0, providing that the four conditions outlined above for C are true. 3) For a covariance matrix, the sum of the diagonal elements of the covariance matrix equals the sum of the eigenvalues, or in math terms, tr(c) = tr(d). For a correlation matrix, all the eigenvalues sum to n, the number of variables. Furthermore, in case you have a burning passion to know about it, the determinant of C equals the product of the eigenvalues of C. 4) It is a royal pain to compute eigenvalues and eigenvectors, so don't let me catch you doing it. 5) VERY IMPORTANT : The decomposition of a matrix into its eigenvalues and eigenvectors is a mathematical/geometric decomposition. The decomposition literally rearranges the dimensions in an n dimensional space (n being the number of variables) in such a way that the axis (e.g., North-South, East-West) are all perpendicular. This rearrangement may but is not guaranteed to uncover an important psychological construct or even to have a psychologically meaningful interpretation. 6) ALSO VERY IMPORTANT : An eigenvalue tells us the proportion of total variability in a matrix associated with its corresponding eigenvector. Consequently, the eigenvector that corresponds to the highest eigenvalue tells us the dimension (axis) that generates the maximum amount of individual variability in the variables. The next eigenvector is a dimension perpendicular to the first that accounts for the second largest amount of variability, and so on.

8 Psychology 7291: Multivariate Statistics (Carey) 8/27/98 Matrix Algebra - 8 Mathematical Digression: eigenvalues and eigenvectors An important mathematical formulation is the characteristic equation of a square matrix. If C is an n by n covariance matrix, the characteristic equation is C - λi = 0 (1.0) where λ is a scalar. Solving this equation for λ reveals that the equation is a nth degree polynomial of λ. That is, there are as many λs as there are variables in the covariance matrix. The n λs that are the roots of this polynomial are the eigenvalues of C. Because C is symmetric, all the λs will be real numbers (i.e., not complex or imaginary numbers), although some of the λs may be equal to or less than 0. The λs can be solved for in any order, but it is customary to order them from largest to smallest. To examine what is meant here, let C denote a two by two correlation matrix that has the form: 1 ρ ρ 1 Then the quantity C - λi may be written as C λi= 1 ρ λ 0 1 λ = ρ 1 0 λ ρ The determinant is ρ 1 λ C λi = ( 1 λ) 2 ρ 2 So the equation that requires solution is (1 λ) 2 ρ 2 = 0. This is a quadratic in λ. If we had three variables, it would be a cubic; and if there were four variables, it would be a quartic, etc. Solving for the quadratic gives λ = 1 ±ρ The largest root depends on the sign of ρ. For ρ > 0, then λ 1 = 1 + ρ and λ 2 = 1 - ρ. For each λ, one can define a nonzero vector a, such that ( C λi)a =0. The 0 to the right of the equal sign denotes a vector filled with 0s. Any number in a that satisfies this equation is called a latent vector or eigenvector of matrix C. Each eigenvector is associated with its own λ. Note that the solution to a is not unique because if a is multiplied by any scalar, the above equation still holds. Thus, there are an infinite set of values for a, although each solution will be a scalar multiple of any other solution. It is customary to normalize the values of a by imposing the constraint that a'a = 1. A latent vector subject to this constraint is called a normalized latent vector. Taking the two by two correlation matrix with ρ > 0, then

9 Psychology 7291: Multivariate Statistics (Carey) 8/27/98 Matrix Algebra - 9 (C λi)a = 1 λ ρ a 1 (1 λ)a 1 +ρa = 2 = 0. ρ 1 λ a 2 (1 λ)a 2 +ρa 1 0 or, by carrying out the multiplication, we find ( 1 λ)a 1 +ρa 2 =0 ( 1 λ)a 2 +ρa 1 =0 Now take the largest eigenvalue, l = 1 + r, and substitute. This gives ρ( a 2 a 1 ) = 0 ρ( a 1 a 2 ) = 0 Thus, all we know is that a 1 = a 2. If we let a 1 = 10, then a 2 = 10; and if we let a 1 = -.023, then a 2 = This is what was meant above when it was said that there were an infinite number of solutions where any single solution is a scalar multiple of any other solution. By requiring that a'a = 1, we can settle on values of a 1 and a 2. That is, if a a 2 2 = 1 and a 1 = a 2 = a, say, then 2a 2 = 1 and a =.5. So the first eigenvector will be a 2 by 1 column vector with both elements equaling.5. For the second eigenvector, we substitute 1 - ρ for λ. This gives, ρ( a 1 + a 2 ) = 0 Consequently, a 1 = -a 2. One of the a's must equal.5 and the other must equal -.5. It is immaterial which is positive and which is negative, but it is a frequent convention to make the first one (a 1 ) positive and the second negative. Note that the normalized eigenvectors of a two by two correlation matrix will always take on these values. The actual value of the correlation coefficient is irrelevant as long as it exceeds 0. Can you figure out the eigenvectors for a two by two matrix where ρ < 0?

### 1 Introduction. 2 Matrices: Definition. Matrix Algebra. Hervé Abdi Lynne J. Williams

In Neil Salkind (Ed.), Encyclopedia of Research Design. Thousand Oaks, CA: Sage. 00 Matrix Algebra Hervé Abdi Lynne J. Williams Introduction Sylvester developed the modern concept of matrices in the 9th

### The basic unit in matrix algebra is a matrix, generally expressed as: a 11 a 12. a 13 A = a 21 a 22 a 23

(copyright by Scott M Lynch, February 2003) Brief Matrix Algebra Review (Soc 504) Matrix algebra is a form of mathematics that allows compact notation for, and mathematical manipulation of, high-dimensional

### 1 Introduction to Matrices

1 Introduction to Matrices In this section, important definitions and results from matrix algebra that are useful in regression analysis are introduced. While all statements below regarding the columns

### 4. MATRICES Matrices

4. MATRICES 170 4. Matrices 4.1. Definitions. Definition 4.1.1. A matrix is a rectangular array of numbers. A matrix with m rows and n columns is said to have dimension m n and may be represented as follows:

### Helpsheet. Giblin Eunson Library MATRIX ALGEBRA. library.unimelb.edu.au/libraries/bee. Use this sheet to help you:

Helpsheet Giblin Eunson Library ATRIX ALGEBRA Use this sheet to help you: Understand the basic concepts and definitions of matrix algebra Express a set of linear equations in matrix notation Evaluate determinants

### 13 MATH FACTS 101. 2 a = 1. 7. The elements of a vector have a graphical interpretation, which is particularly easy to see in two or three dimensions.

3 MATH FACTS 0 3 MATH FACTS 3. Vectors 3.. Definition We use the overhead arrow to denote a column vector, i.e., a linear segment with a direction. For example, in three-space, we write a vector in terms

### Mathematics Notes for Class 12 chapter 3. Matrices

1 P a g e Mathematics Notes for Class 12 chapter 3. Matrices A matrix is a rectangular arrangement of numbers (real or complex) which may be represented as matrix is enclosed by [ ] or ( ) or Compact form

### UNIT 2 MATRICES - I 2.0 INTRODUCTION. Structure

UNIT 2 MATRICES - I Matrices - I Structure 2.0 Introduction 2.1 Objectives 2.2 Matrices 2.3 Operation on Matrices 2.4 Invertible Matrices 2.5 Systems of Linear Equations 2.6 Answers to Check Your Progress

### MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS Systems of Equations and Matrices Representation of a linear system The general system of m equations in n unknowns can be written a x + a 2 x 2 + + a n x n b a

### Eigenvalues, Eigenvectors, Matrix Factoring, and Principal Components

Eigenvalues, Eigenvectors, Matrix Factoring, and Principal Components The eigenvalues and eigenvectors of a square matrix play a key role in some important operations in statistics. In particular, they

### Similarity and Diagonalization. Similar Matrices

MATH022 Linear Algebra Brief lecture notes 48 Similarity and Diagonalization Similar Matrices Let A and B be n n matrices. We say that A is similar to B if there is an invertible n n matrix P such that

### ( % . This matrix consists of \$ 4 5 " 5' the coefficients of the variables as they appear in the original system. The augmented 3 " 2 2 # 2 " 3 4&

Matrices define matrix We will use matrices to help us solve systems of equations. A matrix is a rectangular array of numbers enclosed in parentheses or brackets. In linear algebra, matrices are important

### Linear Algebra Notes for Marsden and Tromba Vector Calculus

Linear Algebra Notes for Marsden and Tromba Vector Calculus n-dimensional Euclidean Space and Matrices Definition of n space As was learned in Math b, a point in Euclidean three space can be thought of

### MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS. + + x 2. x n. a 11 a 12 a 1n b 1 a 21 a 22 a 2n b 2 a 31 a 32 a 3n b 3. a m1 a m2 a mn b m

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS 1. SYSTEMS OF EQUATIONS AND MATRICES 1.1. Representation of a linear system. The general system of m equations in n unknowns can be written a 11 x 1 + a 12 x 2 +

### Introduction to Matrix Algebra I

Appendix A Introduction to Matrix Algebra I Today we will begin the course with a discussion of matrix algebra. Why are we studying this? We will use matrix algebra to derive the linear regression model

### 15.062 Data Mining: Algorithms and Applications Matrix Math Review

.6 Data Mining: Algorithms and Applications Matrix Math Review The purpose of this document is to give a brief review of selected linear algebra concepts that will be useful for the course and to develop

### MATH 304 Linear Algebra Lecture 4: Matrix multiplication. Diagonal matrices. Inverse matrix.

MATH 304 Linear Algebra Lecture 4: Matrix multiplication. Diagonal matrices. Inverse matrix. Matrices Definition. An m-by-n matrix is a rectangular array of numbers that has m rows and n columns: a 11

### Diagonal, Symmetric and Triangular Matrices

Contents 1 Diagonal, Symmetric Triangular Matrices 2 Diagonal Matrices 2.1 Products, Powers Inverses of Diagonal Matrices 2.1.1 Theorem (Powers of Matrices) 2.2 Multiplying Matrices on the Left Right by

### Linear Algebra Review. Vectors

Linear Algebra Review By Tim K. Marks UCSD Borrows heavily from: Jana Kosecka kosecka@cs.gmu.edu http://cs.gmu.edu/~kosecka/cs682.html Virginia de Sa Cogsci 8F Linear Algebra review UCSD Vectors The length

### = [a ij ] 2 3. Square matrix A square matrix is one that has equal number of rows and columns, that is n = m. Some examples of square matrices are

This document deals with the fundamentals of matrix algebra and is adapted from B.C. Kuo, Linear Networks and Systems, McGraw Hill, 1967. It is presented here for educational purposes. 1 Introduction In

### Orthogonal Diagonalization of Symmetric Matrices

MATH10212 Linear Algebra Brief lecture notes 57 Gram Schmidt Process enables us to find an orthogonal basis of a subspace. Let u 1,..., u k be a basis of a subspace V of R n. We begin the process of finding

### A Introduction to Matrix Algebra and Principal Components Analysis

A Introduction to Matrix Algebra and Principal Components Analysis Multivariate Methods in Education ERSH 8350 Lecture #2 August 24, 2011 ERSH 8350: Lecture 2 Today s Class An introduction to matrix algebra

### Matrix Algebra, Class Notes (part 1) by Hrishikesh D. Vinod Copyright 1998 by Prof. H. D. Vinod, Fordham University, New York. All rights reserved.

Matrix Algebra, Class Notes (part 1) by Hrishikesh D. Vinod Copyright 1998 by Prof. H. D. Vinod, Fordham University, New York. All rights reserved. 1 Sum, Product and Transpose of Matrices. If a ij with

### MATH 304 Linear Algebra Lecture 8: Inverse matrix (continued). Elementary matrices. Transpose of a matrix.

MATH 304 Linear Algebra Lecture 8: Inverse matrix (continued). Elementary matrices. Transpose of a matrix. Inverse matrix Definition. Let A be an n n matrix. The inverse of A is an n n matrix, denoted

### 1.5 Elementary Matrices and a Method for Finding the Inverse

.5 Elementary Matrices and a Method for Finding the Inverse Definition A n n matrix is called an elementary matrix if it can be obtained from I n by performing a single elementary row operation Reminder:

### Diagonalisation. Chapter 3. Introduction. Eigenvalues and eigenvectors. Reading. Definitions

Chapter 3 Diagonalisation Eigenvalues and eigenvectors, diagonalisation of a matrix, orthogonal diagonalisation fo symmetric matrices Reading As in the previous chapter, there is no specific essential

### The Solution of Linear Simultaneous Equations

Appendix A The Solution of Linear Simultaneous Equations Circuit analysis frequently involves the solution of linear simultaneous equations. Our purpose here is to review the use of determinants to solve

### Chapter 6. Orthogonality

6.3 Orthogonal Matrices 1 Chapter 6. Orthogonality 6.3 Orthogonal Matrices Definition 6.4. An n n matrix A is orthogonal if A T A = I. Note. We will see that the columns of an orthogonal matrix must be

### Notes on Determinant

ENGG2012B Advanced Engineering Mathematics Notes on Determinant Lecturer: Kenneth Shum Lecture 9-18/02/2013 The determinant of a system of linear equations determines whether the solution is unique, without

### The Characteristic Polynomial

Physics 116A Winter 2011 The Characteristic Polynomial 1 Coefficients of the characteristic polynomial Consider the eigenvalue problem for an n n matrix A, A v = λ v, v 0 (1) The solution to this problem

### Matrix Algebra and Applications

Matrix Algebra and Applications Dudley Cooke Trinity College Dublin Dudley Cooke (Trinity College Dublin) Matrix Algebra and Applications 1 / 49 EC2040 Topic 2 - Matrices and Matrix Algebra Reading 1 Chapters

### Matrices in Statics and Mechanics

Matrices in Statics and Mechanics Casey Pearson 3/19/2012 Abstract The goal of this project is to show how linear algebra can be used to solve complex, multi-variable statics problems as well as illustrate

### Basics Inversion and related concepts Random vectors Matrix calculus. Matrix algebra. Patrick Breheny. January 20

Matrix algebra January 20 Introduction Basics The mathematics of multiple regression revolves around ordering and keeping track of large arrays of numbers and solving systems of equations The mathematical

### Math 115A HW4 Solutions University of California, Los Angeles. 5 2i 6 + 4i. (5 2i)7i (6 + 4i)( 3 + i) = 35i + 14 ( 22 6i) = 36 + 41i.

Math 5A HW4 Solutions September 5, 202 University of California, Los Angeles Problem 4..3b Calculate the determinant, 5 2i 6 + 4i 3 + i 7i Solution: The textbook s instructions give us, (5 2i)7i (6 + 4i)(

### Vector and Matrix Norms

Chapter 1 Vector and Matrix Norms 11 Vector Spaces Let F be a field (such as the real numbers, R, or complex numbers, C) with elements called scalars A Vector Space, V, over the field F is a non-empty

### A Brief Primer on Matrix Algebra

A Brief Primer on Matrix Algebra A matrix is a rectangular array of numbers whose individual entries are called elements. Each horizontal array of elements is called a row, while each vertical array is

### 9 Matrices, determinants, inverse matrix, Cramer s Rule

AAC - Business Mathematics I Lecture #9, December 15, 2007 Katarína Kálovcová 9 Matrices, determinants, inverse matrix, Cramer s Rule Basic properties of matrices: Example: Addition properties: Associative:

### NOTES on LINEAR ALGEBRA 1

School of Economics, Management and Statistics University of Bologna Academic Year 205/6 NOTES on LINEAR ALGEBRA for the students of Stats and Maths This is a modified version of the notes by Prof Laura

### MATH 240 Fall, Chapter 1: Linear Equations and Matrices

MATH 240 Fall, 2007 Chapter Summaries for Kolman / Hill, Elementary Linear Algebra, 9th Ed. written by Prof. J. Beachy Sections 1.1 1.5, 2.1 2.3, 4.2 4.9, 3.1 3.5, 5.3 5.5, 6.1 6.3, 6.5, 7.1 7.3 DEFINITIONS

### Summary of week 8 (Lectures 22, 23 and 24)

WEEK 8 Summary of week 8 (Lectures 22, 23 and 24) This week we completed our discussion of Chapter 5 of [VST] Recall that if V and W are inner product spaces then a linear map T : V W is called an isometry

### DETERMINANTS. b 2. x 2

DETERMINANTS 1 Systems of two equations in two unknowns A system of two equations in two unknowns has the form a 11 x 1 + a 12 x 2 = b 1 a 21 x 1 + a 22 x 2 = b 2 This can be written more concisely in

### Practice Math 110 Final. Instructions: Work all of problems 1 through 5, and work any 5 of problems 10 through 16.

Practice Math 110 Final Instructions: Work all of problems 1 through 5, and work any 5 of problems 10 through 16. 1. Let A = 3 1 1 3 3 2. 6 6 5 a. Use Gauss elimination to reduce A to an upper triangular

### Inverses. Stephen Boyd. EE103 Stanford University. October 27, 2015

Inverses Stephen Boyd EE103 Stanford University October 27, 2015 Outline Left and right inverses Inverse Solving linear equations Examples Pseudo-inverse Left and right inverses 2 Left inverses a number

### DATA ANALYSIS II. Matrix Algorithms

DATA ANALYSIS II Matrix Algorithms Similarity Matrix Given a dataset D = {x i }, i=1,..,n consisting of n points in R d, let A denote the n n symmetric similarity matrix between the points, given as where

### MATH 423 Linear Algebra II Lecture 38: Generalized eigenvectors. Jordan canonical form (continued).

MATH 423 Linear Algebra II Lecture 38: Generalized eigenvectors Jordan canonical form (continued) Jordan canonical form A Jordan block is a square matrix of the form λ 1 0 0 0 0 λ 1 0 0 0 0 λ 0 0 J = 0

### Lecture 2 Matrix Operations

Lecture 2 Matrix Operations transpose, sum & difference, scalar multiplication matrix multiplication, matrix-vector product matrix inverse 2 1 Matrix transpose transpose of m n matrix A, denoted A T or

### Matrix Algebra LECTURE 1. Simultaneous Equations Consider a system of m linear equations in n unknowns: y 1 = a 11 x 1 + a 12 x 2 + +a 1n x n,

LECTURE 1 Matrix Algebra Simultaneous Equations Consider a system of m linear equations in n unknowns: y 1 a 11 x 1 + a 12 x 2 + +a 1n x n, (1) y 2 a 21 x 1 + a 22 x 2 + +a 2n x n, y m a m1 x 1 +a m2 x

### (a) The transpose of a lower triangular matrix is upper triangular, and the transpose of an upper triangular matrix is lower triangular.

Theorem.7.: (Properties of Triangular Matrices) (a) The transpose of a lower triangular matrix is upper triangular, and the transpose of an upper triangular matrix is lower triangular. (b) The product

### GRA6035 Mathematics. Eivind Eriksen and Trond S. Gustavsen. Department of Economics

GRA635 Mathematics Eivind Eriksen and Trond S. Gustavsen Department of Economics c Eivind Eriksen, Trond S. Gustavsen. Edition. Edition Students enrolled in the course GRA635 Mathematics for the academic

Facts About Eigenvalues By Dr David Butler Definitions Suppose A is an n n matrix An eigenvalue of A is a number λ such that Av = λv for some nonzero vector v An eigenvector of A is a nonzero vector v

### Sergei Silvestrov, Christopher Engström, Karl Lundengård, Johan Richter, Jonas Österberg. November 13, 2014

Sergei Silvestrov,, Karl Lundengård, Johan Richter, Jonas Österberg November 13, 2014 Analysis Todays lecture: Course overview. Repetition of matrices elementary operations. Repetition of solvability of

### INTRODUCTORY LINEAR ALGEBRA WITH APPLICATIONS B. KOLMAN, D. R. HILL

SOLUTIONS OF THEORETICAL EXERCISES selected from INTRODUCTORY LINEAR ALGEBRA WITH APPLICATIONS B. KOLMAN, D. R. HILL Eighth Edition, Prentice Hall, 2005. Dr. Grigore CĂLUGĂREANU Department of Mathematics

### row row row 4

13 Matrices The following notes came from Foundation mathematics (MATH 123) Although matrices are not part of what would normally be considered foundation mathematics, they are one of the first topics

### Matrices, Determinants and Linear Systems

September 21, 2014 Matrices A matrix A m n is an array of numbers in rows and columns a 11 a 12 a 1n r 1 a 21 a 22 a 2n r 2....... a m1 a m2 a mn r m c 1 c 2 c n We say that the dimension of A is m n (we

### Advanced Techniques for Mobile Robotics Compact Course on Linear Algebra. Wolfram Burgard, Cyrill Stachniss, Kai Arras, Maren Bennewitz

Advanced Techniques for Mobile Robotics Compact Course on Linear Algebra Wolfram Burgard, Cyrill Stachniss, Kai Arras, Maren Bennewitz Vectors Arrays of numbers Vectors represent a point in a n dimensional

### Linear Algebra: Determinants, Inverses, Rank

D Linear Algebra: Determinants, Inverses, Rank D 1 Appendix D: LINEAR ALGEBRA: DETERMINANTS, INVERSES, RANK TABLE OF CONTENTS Page D.1. Introduction D 3 D.2. Determinants D 3 D.2.1. Some Properties of

### LINEAR ALGEBRA. September 23, 2010

LINEAR ALGEBRA September 3, 00 Contents 0. LU-decomposition.................................... 0. Inverses and Transposes................................. 0.3 Column Spaces and NullSpaces.............................

### Lecture No. # 02 Prologue-Part 2

Advanced Matrix Theory and Linear Algebra for Engineers Prof. R.Vittal Rao Center for Electronics Design and Technology Indian Institute of Science, Bangalore Lecture No. # 02 Prologue-Part 2 In the last

### Notes for STA 437/1005 Methods for Multivariate Data

Notes for STA 437/1005 Methods for Multivariate Data Radford M. Neal, 26 November 2010 Random Vectors Notation: Let X be a random vector with p elements, so that X = [X 1,..., X p ], where denotes transpose.

### MATH36001 Background Material 2015

MATH3600 Background Material 205 Matrix Algebra Matrices and Vectors An ordered array of mn elements a ij (i =,, m; j =,, n) written in the form a a 2 a n A = a 2 a 22 a 2n a m a m2 a mn is said to be

### Chapter 17. Orthogonal Matrices and Symmetries of Space

Chapter 17. Orthogonal Matrices and Symmetries of Space Take a random matrix, say 1 3 A = 4 5 6, 7 8 9 and compare the lengths of e 1 and Ae 1. The vector e 1 has length 1, while Ae 1 = (1, 4, 7) has length

### MATH 551 - APPLIED MATRIX THEORY

MATH 55 - APPLIED MATRIX THEORY FINAL TEST: SAMPLE with SOLUTIONS (25 points NAME: PROBLEM (3 points A web of 5 pages is described by a directed graph whose matrix is given by A Do the following ( points

### by the matrix A results in a vector which is a reflection of the given

Eigenvalues & Eigenvectors Example Suppose Then So, geometrically, multiplying a vector in by the matrix A results in a vector which is a reflection of the given vector about the y-axis We observe that

### Brief Introduction to Vectors and Matrices

CHAPTER 1 Brief Introduction to Vectors and Matrices In this chapter, we will discuss some needed concepts found in introductory course in linear algebra. We will introduce matrix, vector, vector-valued

### Section 6.1 - Inner Products and Norms

Section 6.1 - Inner Products and Norms Definition. Let V be a vector space over F {R, C}. An inner product on V is a function that assigns, to every ordered pair of vectors x and y in V, a scalar in F,

### 1 Spherical Kinematics

ME 115(a): Notes on Rotations 1 Spherical Kinematics Motions of a 3-dimensional rigid body where one point of the body remains fixed are termed spherical motions. A spherical displacement is a rigid body

### SF2940: Probability theory Lecture 8: Multivariate Normal Distribution

SF2940: Probability theory Lecture 8: Multivariate Normal Distribution Timo Koski 24.09.2015 Timo Koski Matematisk statistik 24.09.2015 1 / 1 Learning outcomes Random vectors, mean vector, covariance matrix,

### Chapter 8. Matrices II: inverses. 8.1 What is an inverse?

Chapter 8 Matrices II: inverses We have learnt how to add subtract and multiply matrices but we have not defined division. The reason is that in general it cannot always be defined. In this chapter, we

### Review Jeopardy. Blue vs. Orange. Review Jeopardy

Review Jeopardy Blue vs. Orange Review Jeopardy Jeopardy Round Lectures 0-3 Jeopardy Round \$200 How could I measure how far apart (i.e. how different) two observations, y 1 and y 2, are from each other?

### Multivariate Analysis of Variance (MANOVA): I. Theory

Gregory Carey, 1998 MANOVA: I - 1 Multivariate Analysis of Variance (MANOVA): I. Theory Introduction The purpose of a t test is to assess the likelihood that the means for two groups are sampled from the

### Solution to Homework 2

Solution to Homework 2 Olena Bormashenko September 23, 2011 Section 1.4: 1(a)(b)(i)(k), 4, 5, 14; Section 1.5: 1(a)(b)(c)(d)(e)(n), 2(a)(c), 13, 16, 17, 18, 27 Section 1.4 1. Compute the following, if

### Using the Singular Value Decomposition

Using the Singular Value Decomposition Emmett J. Ientilucci Chester F. Carlson Center for Imaging Science Rochester Institute of Technology emmett@cis.rit.edu May 9, 003 Abstract This report introduces

### [1] Diagonal factorization

8.03 LA.6: Diagonalization and Orthogonal Matrices [ Diagonal factorization [2 Solving systems of first order differential equations [3 Symmetric and Orthonormal Matrices [ Diagonal factorization Recall:

### The Inverse of a Matrix

The Inverse of a Matrix 7.4 Introduction In number arithmetic every number a ( 0) has a reciprocal b written as a or such that a ba = ab =. Some, but not all, square matrices have inverses. If a square

### Au = = = 3u. Aw = = = 2w. so the action of A on u and w is very easy to picture: it simply amounts to a stretching by 3 and 2, respectively.

Chapter 7 Eigenvalues and Eigenvectors In this last chapter of our exploration of Linear Algebra we will revisit eigenvalues and eigenvectors of matrices, concepts that were already introduced in Geometry

### Matrix Inverse and Determinants

DM554 Linear and Integer Programming Lecture 5 and Marco Chiarandini Department of Mathematics & Computer Science University of Southern Denmark Outline 1 2 3 4 and Cramer s rule 2 Outline 1 2 3 4 and

### a 11 x 1 + a 12 x 2 + + a 1n x n = b 1 a 21 x 1 + a 22 x 2 + + a 2n x n = b 2.

Chapter 1 LINEAR EQUATIONS 1.1 Introduction to linear equations A linear equation in n unknowns x 1, x,, x n is an equation of the form a 1 x 1 + a x + + a n x n = b, where a 1, a,..., a n, b are given

### Topic 1: Matrices and Systems of Linear Equations.

Topic 1: Matrices and Systems of Linear Equations Let us start with a review of some linear algebra concepts we have already learned, such as matrices, determinants, etc Also, we shall review the method

### Homework: 2.1 (page 56): 7, 9, 13, 15, 17, 25, 27, 35, 37, 41, 46, 49, 67

Chapter Matrices Operations with Matrices Homework: (page 56):, 9, 3, 5,, 5,, 35, 3, 4, 46, 49, 6 Main points in this section: We define a few concept regarding matrices This would include addition of

### Vocabulary Words and Definitions for Algebra

Name: Period: Vocabulary Words and s for Algebra Absolute Value Additive Inverse Algebraic Expression Ascending Order Associative Property Axis of Symmetry Base Binomial Coefficient Combine Like Terms

### CHARACTERISTIC ROOTS AND VECTORS

CHARACTERISTIC ROOTS AND VECTORS 1 DEFINITION OF CHARACTERISTIC ROOTS AND VECTORS 11 Statement of the characteristic root problem Find values of a scalar λ for which there exist vectors x 0 satisfying

### WHICH LINEAR-FRACTIONAL TRANSFORMATIONS INDUCE ROTATIONS OF THE SPHERE?

WHICH LINEAR-FRACTIONAL TRANSFORMATIONS INDUCE ROTATIONS OF THE SPHERE? JOEL H. SHAPIRO Abstract. These notes supplement the discussion of linear fractional mappings presented in a beginning graduate course

### Linear Algebra: Matrices

B Linear Algebra: Matrices B 1 Appendix B: LINEAR ALGEBRA: MATRICES TABLE OF CONTENTS Page B.1. Matrices B 3 B.1.1. Concept................... B 3 B.1.2. Real and Complex Matrices............ B 3 B.1.3.

### 1. True/False: Circle the correct answer. No justifications are needed in this exercise. (1 point each)

Math 33 AH : Solution to the Final Exam Honors Linear Algebra and Applications 1. True/False: Circle the correct answer. No justifications are needed in this exercise. (1 point each) (1) If A is an invertible

### Mathematics of Cryptography

CHAPTER 2 Mathematics of Cryptography Part I: Modular Arithmetic, Congruence, and Matrices Objectives This chapter is intended to prepare the reader for the next few chapters in cryptography. The chapter

### Matrices provide a compact notation for expressing systems of equations or variables. For instance, a linear function might be written as: b k

MATRIX ALGEBRA FOR STATISTICS: PART 1 Matrices provide a compact notation for expressing systems of equations or variables. For instance, a linear function might be written as: y = x 1 b 1 + x 2 + x 3

### EC327: Advanced Econometrics, Spring 2007

EC327: Advanced Econometrics, Spring 2007 Wooldridge, Introductory Econometrics (3rd ed, 2006) Appendix D: Summary of matrix algebra Basic definitions A matrix is a rectangular array of numbers, with m

### Economics 102C: Advanced Topics in Econometrics 2 - Tooling Up: The Basics

Economics 102C: Advanced Topics in Econometrics 2 - Tooling Up: The Basics Michael Best Spring 2015 Outline The Evaluation Problem Matrix Algebra Matrix Calculus OLS With Matrices The Evaluation Problem:

### MATH10212 Linear Algebra. Systems of Linear Equations. Definition. An n-dimensional vector is a row or a column of n numbers (or letters): a 1.

MATH10212 Linear Algebra Textbook: D. Poole, Linear Algebra: A Modern Introduction. Thompson, 2006. ISBN 0-534-40596-7. Systems of Linear Equations Definition. An n-dimensional vector is a row or a column

### Lecture 5 Principal Minors and the Hessian

Lecture 5 Principal Minors and the Hessian Eivind Eriksen BI Norwegian School of Management Department of Economics October 01, 2010 Eivind Eriksen (BI Dept of Economics) Lecture 5 Principal Minors and

### Linear Dependence Tests

Linear Dependence Tests The book omits a few key tests for checking the linear dependence of vectors. These short notes discuss these tests, as well as the reasoning behind them. Our first test checks

### State of Stress at Point

State of Stress at Point Einstein Notation The basic idea of Einstein notation is that a covector and a vector can form a scalar: This is typically written as an explicit sum: According to this convention,

### SF2940: Probability theory Lecture 8: Multivariate Normal Distribution

SF2940: Probability theory Lecture 8: Multivariate Normal Distribution Timo Koski 24.09.2014 Timo Koski () Mathematisk statistik 24.09.2014 1 / 75 Learning outcomes Random vectors, mean vector, covariance

### 3.4. Solving Simultaneous Linear Equations. Introduction. Prerequisites. Learning Outcomes

Solving Simultaneous Linear Equations 3.4 Introduction Equations often arise in which there is more than one unknown quantity. When this is the case there will usually be more than one equation involved.

### December 4, 2013 MATH 171 BASIC LINEAR ALGEBRA B. KITCHENS

December 4, 2013 MATH 171 BASIC LINEAR ALGEBRA B KITCHENS The equation 1 Lines in two-dimensional space (1) 2x y = 3 describes a line in two-dimensional space The coefficients of x and y in the equation

### 3.4. Solving simultaneous linear equations. Introduction. Prerequisites. Learning Outcomes

Solving simultaneous linear equations 3.4 Introduction Equations often arise in which there is more than one unknown quantity. When this is the case there will usually be more than one equation involved.

### Notes on Symmetric Matrices

CPSC 536N: Randomized Algorithms 2011-12 Term 2 Notes on Symmetric Matrices Prof. Nick Harvey University of British Columbia 1 Symmetric Matrices We review some basic results concerning symmetric matrices.

### Recall the basic property of the transpose (for any A): v A t Aw = v w, v, w R n.

ORTHOGONAL MATRICES Informally, an orthogonal n n matrix is the n-dimensional analogue of the rotation matrices R θ in R 2. When does a linear transformation of R 3 (or R n ) deserve to be called a rotation?