COMPUTING AND DATA ANALYSIS WITH EXCEL. Matrix manipulation and systems of linear equations

Size: px
Start display at page:

Download "COMPUTING AND DATA ANALYSIS WITH EXCEL. Matrix manipulation and systems of linear equations"

Transcription

1 COMPUTING AND DATA ANALYSIS WITH EXCEL Matrix manipulation and systems of linear equations

2 Outline 1 Matrices Addition Subtraction Excel functions that return more than one cell Solving systems of linear equations Matrix inversion method Gaussian elimination method Jacobi method Gauss-Seidel method

3 Matrix Commands in Excel Introduction: Excel can perform some useful basic matrix operations namely: Addition & subtraction; Scalar multiplication & division; Some common operations and Excel functions employed: Operation Transpose Matrix multiplication Matrix inverse Determinant of matrix Excel Function TRANSPOSE; MMULT; MINVERSE; MDETERM;

4 Addition, Subtraction and Scalar Multiplication, Etc. To add two named 3 x 2 matrices, A and B: i) Highlight a blank 3 x 2 results area in the spreadsheet. (If the results area is too small, you will get the wrong answer.) ii) Type =A+B in the formula bar and press the CTRL, SHIFT and ENTER keys simultaneously. (Note: A+B above refers to the named range or defined names of matrices A and B) You must use the CTRL, SHIFT,ENTER keys simultaneously if you want to perform a matrix computation. (If you don t do this, you will get an error message or the wrong answer.) If you click on any cell in the result, the formula {=A+B} will be displayed. In Excel, the { } brackets indicate a matrix (array) command.

5 Matrix Transpose Suppose A is a 3 x 2 matrix. The transpose of A, A, will be 2 x 3. Select a 2 x 3 results area, type =TRANSPOSE(A) in the formula bar and press CTRL, SHIFT, ENTER. Matrix transpose using Excel

6 Matrix transpose Step: i) Determine the resultant matrix and select the appropriate number of cells. That is, how many rows by columns are required for the resultant matrix?

7 Matrix Transpose Step: ii) Type in the transpose formula and select the range of cells or named range iii) Remember to simultaneously press Ctrl + shift + enter

8 Matrix Multiplication Suppose A and B are named 3 x 2 and 2 x 3 matrices. Then AB is 3 x 3 and BA is 2 x 2. This illustrates the fact that in general, AB is not equal to BA, even if the matrices are conformable. Select a blank 3 x 3 area for the result AB. Type =MMULT(A,B) in the formula bar and press CTRL, SHIFT, ENTER to generate AB.

9 Matrix multiplication, MMULT The product of two matrices A & B can be calculated using the MMULT function. The result is a matrix with a number of rows equal to the number of rows in matrix A and a number of columns in matrix B. The syntax is: MMULT(array1, array2) where array 1 and 2 are respectively matrix A & B Multiply matrix A (3x2) by matrix B (2x2) Step: i) Define a named range for each of matrix A and B (ref to MOS studyguide) ii) Determine the resultant matrix and select the required Excel cells.

10 Matrix multiplication, MMULT (cont d.) Step: ii) Enter the formula the resultant matrix and select the required excel cells. ii) Remember to simultaneously press Ctrl + Shift + Enter

11 Matrix Determinant, MDETERM The function MDETERM calculates the determinant of a square matrix. This operation results in a single value or scalar. No cell within the range can be either empty or contain text, Excel will return an error message in such cases. Syntax, =MDETERM(array) Determinant of a matrix B is written as: det( B) B

12 Matrix Determinant, MDETERM (conti.) The determinant of matrix B is -7

13 Matrix Inverse, MINVERSE The inverse of a matrix is defined as one which when multiplied by the original matrix will return the identity matrix. The matrix inverse function in Excel has the following syntax, =MINVERSE(array) This returns the elements of the inverse matrix corresponding to the matrix given by the input (array). Example: Find the inverse of the following matrix B B B 3 1 Next, let s do this in excel B (1*5) (4*3) Hence, B or

14 Matrix Inverse, MINVERSE (cont d.) As in the previous operations, select the appropriate number of cells before typing in the matrix inverse formula MINVERSE and as usual simultaneously press Ctrl + Shift + Enter Question: Can we invert matrix C? Why?

15 Excel (Cont d) Natural progression from this course is the use of VBA to automate or speed up the manipulation process. see Benninga, S. (2000), Financial Modelling, MIT Press.

16 Summary If A is singular, then det(a) = 0. Exercise: Check that det(ab) = det(ba) = det(a).det(b), where A and B are square matrices. Highlight the cells of the matrix and right-click your mouse. From the flyer, look for DEFINE NAME. Enter a name for the matrix. You can now use the name of the matrix formula instead of the matrix values. Remember Excel is not case sensitive. In other words, names like futures, Futures and FUTURES are the same in excel world. Choose a location for the matrix (or vector) and enter the elements of the matrix. Highlight the cells of the matrix and choose INSERT NAME DEFINE. Enter a name for the matrix. You can now use the name of the matrix in formulae.

17 Summary Exercise: Choose A and B so that AB exists. Check that (AB)' = B 'A using MMULT (matrix multiplication). What do you think (ABC)' is equal to? Suppose A and B are named 3 x 2 and 2 x 3 matrices. Then AB is 3 x 3 and BA is 2 x 2. This illustrates the fact that, in general, AB is not equal to BA, even if the matrices are conformable. Select a blank 3 x 3 area for the result AB. Type =MMULT(A,B) in the formula bar and press CTRL, SHIFT, ENTER to generate AB. Suppose B is a square 2 x2 matrix. Select a 2 x 2 area for the inverse of B. Type =MINVERSE(B) in the formula bar and press CRTL, SHIFT, ENTER. If B is singular (non-invertible), you will get an error message. Suppose A and B have the same dimension and are both invertible. Show that (AB) -1 = B -1 A -1. What do you think (ABC) -1 is equal to?

18 Solving Systems of Linear Equations (in MS Excel) Simple simultaneous equations in2 variables Solve the simultaneous equations in MS Excel: 3x + 2y =8 2x + 5y =-2 You need 3 steps only: i) Create a matrix of the coefficients and constant ii) Determine the inverse matrix of the coefficients using MINVERSE iii) Multiply the inverse matrix with the matrix of constants, use MMULT

19 Solving Systems of Linear Equations (in MS Excel) Short exercise: Use MS Excel to solve the following simultaneous equations 1) x y z 4 2x 3y z 7 x 2y 3z 6 2) 3a b 3c 8 5a 3b 6c 4 6a 4b c 20 3) 2x y z 3 y z 2 4x y z 12

20 Calculating Variance-Covariance Matrix (From Excess returns) Lab Exercise Steps: i) Download the annual stock price for six companies listed on FTSE 100 ii) From the guide in lecture 2, calculate the average annual return for each company iii) Next calculate the excess return as (each return value less the average annual return for the asset iv) Now, transpose the matrix of excess asset returns v) Calculate the variance-covariance matrix from Mmult(transposed matrix, original excess return matrix) divided by the number of observations per asset

Linear Algebra, Vectors and Matrices

Linear Algebra, Vectors and Matrices Linear Algebra, Vectors and Matrices Prof. Manuela Pedio 20550 Quantitative Methods for Finance August 2018 Outline of the Course Lectures 1 and 2 (3 hours, in class): Linear and non-linear functions on

More information

Figure 1. Distillation Train. Table 1. Stream compositions.

Figure 1. Distillation Train. Table 1. Stream compositions. CM 3450 Drills 2 9/8/2010 1. A stream containing compounds,, and are fed to a series of distillation columns as shown in Figure 1 with corresponding stream compositions given in Table 1. Figure 1. Distillation

More information

[ Here 21 is the dot product of (3, 1, 2, 5) with (2, 3, 1, 2), and 31 is the dot product of

[ Here 21 is the dot product of (3, 1, 2, 5) with (2, 3, 1, 2), and 31 is the dot product of . Matrices A matrix is any rectangular array of numbers. For example 3 5 6 4 8 3 3 is 3 4 matrix, i.e. a rectangular array of numbers with three rows four columns. We usually use capital letters for matrices,

More information

MAC Learning Objectives. Learning Objectives (Cont.) Module 10 System of Equations and Inequalities II

MAC Learning Objectives. Learning Objectives (Cont.) Module 10 System of Equations and Inequalities II MAC 1140 Module 10 System of Equations and Inequalities II Learning Objectives Upon completing this module, you should be able to 1. represent systems of linear equations with matrices. 2. transform a

More information

Stochastic Modelling

Stochastic Modelling Stochastic Modelling Simulating Random Walks and Markov Chains This lab sheets is available for downloading from www.staff.city.ac.uk/r.j.gerrard/courses/dam/, as is the spreadsheet mentioned in section

More information

Multiplying matrices by diagonal matrices is faster than usual matrix multiplication.

Multiplying matrices by diagonal matrices is faster than usual matrix multiplication. 7-6 Multiplying matrices by diagonal matrices is faster than usual matrix multiplication. The following equations generalize to matrices of any size. Multiplying a matrix from the left by a diagonal matrix

More information

Chapter 2 Notes, Linear Algebra 5e Lay

Chapter 2 Notes, Linear Algebra 5e Lay Contents.1 Operations with Matrices..................................1.1 Addition and Subtraction.............................1. Multiplication by a scalar............................ 3.1.3 Multiplication

More information

7.4. The Inverse of a Matrix. Introduction. Prerequisites. Learning Outcomes

7.4. The Inverse of a Matrix. Introduction. Prerequisites. Learning Outcomes The Inverse of a Matrix 7.4 Introduction In number arithmetic every number a 0has a reciprocal b written as a or such that a ba = ab =. Similarly a square matrix A may have an inverse B = A where AB =

More information

7.6 The Inverse of a Square Matrix

7.6 The Inverse of a Square Matrix 7.6 The Inverse of a Square Matrix Copyright Cengage Learning. All rights reserved. What You Should Learn Verify that two matrices are inverses of each other. Use Gauss-Jordan elimination to find inverses

More information

9. Using Excel matrices functions to calculate partial autocorrelations

9. Using Excel matrices functions to calculate partial autocorrelations 9 Using Excel matrices functions to calculate partial autocorrelations In order to use a more elegant way to calculate the partial autocorrelations we need to borrow some equations from stochastic modelling,

More information

Graduate Mathematical Economics Lecture 1

Graduate Mathematical Economics Lecture 1 Graduate Mathematical Economics Lecture 1 Yu Ren WISE, Xiamen University September 23, 2012 Outline 1 2 Course Outline ematical techniques used in graduate level economics courses Mathematics for Economists

More information

Math 313 (Linear Algebra) Exam 2 - Practice Exam

Math 313 (Linear Algebra) Exam 2 - Practice Exam Name: Student ID: Section: Instructor: Math 313 (Linear Algebra) Exam 2 - Practice Exam Instructions: For questions which require a written answer, show all your work. Full credit will be given only if

More information

MAC Module 3 Determinants. Learning Objectives. Upon completing this module, you should be able to:

MAC Module 3 Determinants. Learning Objectives. Upon completing this module, you should be able to: MAC 2 Module Determinants Learning Objectives Upon completing this module, you should be able to:. Determine the minor, cofactor, and adjoint of a matrix. 2. Evaluate the determinant of a matrix by cofactor

More information

Lesson U2.1 Study Guide

Lesson U2.1 Study Guide Lesson U2.1 Study Guide Sunday, June 3, 2018 2:05 PM Matrix operations, The Inverse of a Matrix and Matrix Factorization Reading: Lay, Sections 2.1, 2.2, 2.3 and 2.5 (about 24 pages). MyMathLab: Lesson

More information

Materials engineering Collage \\ Ceramic & construction materials department Numerical Analysis \\Third stage by \\ Dalya Hekmat

Materials engineering Collage \\ Ceramic & construction materials department Numerical Analysis \\Third stage by \\ Dalya Hekmat Materials engineering Collage \\ Ceramic & construction materials department Numerical Analysis \\Third stage by \\ Dalya Hekmat Linear Algebra Lecture 2 1.3.7 Matrix Matrix multiplication using Falk s

More information

Unit 3: Matrices. Juan Luis Melero and Eduardo Eyras. September 2018

Unit 3: Matrices. Juan Luis Melero and Eduardo Eyras. September 2018 Unit 3: Matrices Juan Luis Melero and Eduardo Eyras September 2018 1 Contents 1 Matrices and operations 4 1.1 Definition of a matrix....................... 4 1.2 Addition and subtraction of matrices..............

More information

Chapter 6 Page 1 of 10. Lecture Guide. Math College Algebra Chapter 6. to accompany. College Algebra by Julie Miller

Chapter 6 Page 1 of 10. Lecture Guide. Math College Algebra Chapter 6. to accompany. College Algebra by Julie Miller Chapter 6 Page 1 of 10 Lecture Guide Math 105 - College Algebra Chapter 6 to accompany College Algebra by Julie Miller Corresponding Lecture Videos can be found at Prepared by Stephen Toner & Nichole DuBal

More information

Math Lecture 26 : The Properties of Determinants

Math Lecture 26 : The Properties of Determinants Math 2270 - Lecture 26 : The Properties of Determinants Dylan Zwick Fall 202 The lecture covers section 5. from the textbook. The determinant of a square matrix is a number that tells you quite a bit about

More information

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

MATH Topics in Applied Mathematics Lecture 12: Evaluation of determinants. Cross product. MATH 311-504 Topics in Applied Mathematics Lecture 12: Evaluation of determinants. Cross product. Determinant is a scalar assigned to each square matrix. Notation. The determinant of a matrix A = (a ij

More information

Undergraduate Mathematical Economics Lecture 1

Undergraduate Mathematical Economics Lecture 1 Undergraduate Mathematical Economics Lecture 1 Yu Ren WISE, Xiamen University September 15, 2014 Outline 1 Courses Description and Requirement 2 Course Outline ematical techniques used in economics courses

More information

Inverting Matrices. 1 Properties of Transpose. 2 Matrix Algebra. P. Danziger 3.2, 3.3

Inverting Matrices. 1 Properties of Transpose. 2 Matrix Algebra. P. Danziger 3.2, 3.3 3., 3.3 Inverting Matrices P. Danziger 1 Properties of Transpose Transpose has higher precedence than multiplication and addition, so AB T A ( B T and A + B T A + ( B T As opposed to the bracketed expressions

More information

Matrices A matrix is a rectangular array of numbers. For example, the following rectangular arrays of numbers are matrices: 2 1 2

Matrices A matrix is a rectangular array of numbers. For example, the following rectangular arrays of numbers are matrices: 2 1 2 Matrices A matrix is a rectangular array of numbers For example, the following rectangular arrays of numbers are matrices: 7 A = B = C = 3 6 5 8 0 6 D = [ 3 5 7 9 E = 8 7653 0 Matrices vary in size An

More information

MATH 2050 Assignment 8 Fall [10] 1. Find the determinant by reducing to triangular form for the following matrices.

MATH 2050 Assignment 8 Fall [10] 1. Find the determinant by reducing to triangular form for the following matrices. MATH 2050 Assignment 8 Fall 2016 [10] 1. Find the determinant by reducing to triangular form for the following matrices. 0 1 2 (a) A = 2 1 4. ANS: We perform the Gaussian Elimination on A by the following

More information

Presentation by: H. Sarper. Chapter 2 - Learning Objectives

Presentation by: H. Sarper. Chapter 2 - Learning Objectives Chapter Basic Linear lgebra to accompany Introduction to Mathematical Programming Operations Research, Volume, th edition, by Wayne L. Winston and Munirpallam Venkataramanan Presentation by: H. Sarper

More information

The Theory of the Simplex Method. Chapter 5: Hillier and Lieberman Chapter 5: Decision Tools for Agribusiness Dr. Hurley s AGB 328 Course

The Theory of the Simplex Method. Chapter 5: Hillier and Lieberman Chapter 5: Decision Tools for Agribusiness Dr. Hurley s AGB 328 Course The Theory of the Simplex Method Chapter 5: Hillier and Lieberman Chapter 5: Decision Tools for Agribusiness Dr. Hurley s AGB 328 Course Terms to Know Constraint Boundary Equation, Hyperplane, Constraint

More information

Simultaneous Linear Equations

Simultaneous Linear Equations Simultaneous Linear Equations PHYSICAL PROBLEMS Truss Problem Pressure vessel problem a a b c b Polynomial Regression We are to fit the data to the polynomial regression model Simultaneous Linear Equations

More information

Matrix Algebra Determinant, Inverse matrix. Matrices. A. Fabretti. Mathematics 2 A.Y. 2015/2016. A. Fabretti Matrices

Matrix Algebra Determinant, Inverse matrix. Matrices. A. Fabretti. Mathematics 2 A.Y. 2015/2016. A. Fabretti Matrices Matrices A. Fabretti Mathematics 2 A.Y. 2015/2016 Table of contents Matrix Algebra Determinant Inverse Matrix Introduction A matrix is a rectangular array of numbers. The size of a matrix is indicated

More information

Math 3191 Applied Linear Algebra

Math 3191 Applied Linear Algebra Math 191 Applied Linear Algebra Lecture 8: Inverse of a Matrix Stephen Billups University of Colorado at Denver Math 191Applied Linear Algebra p.1/0 Announcements We will not make it to section. tonight,

More information

Review of Vectors and Matrices

Review of Vectors and Matrices A P P E N D I X D Review of Vectors and Matrices D. VECTORS D.. Definition of a Vector Let p, p, Á, p n be any n real numbers and P an ordered set of these real numbers that is, P = p, p, Á, p n Then P

More information

Introduction to Computational Finance and Financial Econometrics Matrix Algebra Review

Introduction to Computational Finance and Financial Econometrics Matrix Algebra Review You can t see this text! Introduction to Computational Finance and Financial Econometrics Matrix Algebra Review Eric Zivot Spring 2015 Eric Zivot (Copyright 2015) Matrix Algebra Review 1 / 54 Outline 1

More information

This MUST hold matrix multiplication satisfies the distributive property.

This MUST hold matrix multiplication satisfies the distributive property. The columns of AB are combinations of the columns of A. The reason is that each column of AB equals A times the corresponding column of B. But that is a linear combination of the columns of A with coefficients

More information

CITS2401 Computer Analysis & Visualisation

CITS2401 Computer Analysis & Visualisation FACULTY OF ENGINEERING, COMPUTING AND MATHEMATICS CITS2401 Computer Analysis & Visualisation SCHOOL OF COMPUTER SCIENCE AND SOFTWARE ENGINEERING Topic 7 Matrix Algebra Material from MATLAB for Engineers,

More information

Finite Mathematics Chapter 2. where a, b, c, d, h, and k are real numbers and neither a and b nor c and d are both zero.

Finite Mathematics Chapter 2. where a, b, c, d, h, and k are real numbers and neither a and b nor c and d are both zero. Finite Mathematics Chapter 2 Section 2.1 Systems of Linear Equations: An Introduction Systems of Equations Recall that a system of two linear equations in two variables may be written in the general form

More information

Chapter 1: Systems of linear equations and matrices. Section 1.1: Introduction to systems of linear equations

Chapter 1: Systems of linear equations and matrices. Section 1.1: Introduction to systems of linear equations Chapter 1: Systems of linear equations and matrices Section 1.1: Introduction to systems of linear equations Definition: A linear equation in n variables can be expressed in the form a 1 x 1 + a 2 x 2

More information

Introduction to Matrices and Linear Systems Ch. 3

Introduction to Matrices and Linear Systems Ch. 3 Introduction to Matrices and Linear Systems Ch. 3 Doreen De Leon Department of Mathematics, California State University, Fresno June, 5 Basic Matrix Concepts and Operations Section 3.4. Basic Matrix Concepts

More information

Linear Algebra. Matrices Operations. Consider, for example, a system of equations such as x + 2y z + 4w = 0, 3x 4y + 2z 6w = 0, x 3y 2z + w = 0.

Linear Algebra. Matrices Operations. Consider, for example, a system of equations such as x + 2y z + 4w = 0, 3x 4y + 2z 6w = 0, x 3y 2z + w = 0. Matrices Operations Linear Algebra Consider, for example, a system of equations such as x + 2y z + 4w = 0, 3x 4y + 2z 6w = 0, x 3y 2z + w = 0 The rectangular array 1 2 1 4 3 4 2 6 1 3 2 1 in which the

More information

Lecture 10: Determinants and Cramer s Rule

Lecture 10: Determinants and Cramer s Rule Lecture 0: Determinants and Cramer s Rule The determinant and its applications. Definition The determinant of a square matrix A, denoted by det(a) or A, is a real number, which is defined as follows. -by-

More information

To Create a Simple Formula using the Point and Click Method:

To Create a Simple Formula using the Point and Click Method: To Create a Simple Formula that Adds Two Numbers: Click the cell where the formula will be defined (C5, for example). Type the equal sign (=) to let Excel know a formula is being defined. Type the first

More information

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

7.5 Operations with Matrices. Copyright Cengage Learning. All rights reserved. 7.5 Operations with Matrices Copyright Cengage Learning. All rights reserved. What You Should Learn Decide whether two matrices are equal. Add and subtract matrices and multiply matrices by scalars. Multiply

More information

MATRICES The numbers or letters in any given matrix are called its entries or elements

MATRICES The numbers or letters in any given matrix are called its entries or elements MATRICES A matrix is defined as a rectangular array of numbers. Examples are: 1 2 4 a b 1 4 5 A : B : C 0 1 3 c b 1 6 2 2 5 8 The numbers or letters in any given matrix are called its entries or elements

More information

Elementary Linear Algebra

Elementary Linear Algebra Elementary Linear Algebra Linear algebra is the study of; linear sets of equations and their transformation properties. Linear algebra allows the analysis of; rotations in space, least squares fitting,

More information

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

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 Question 1 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 Quang T. Bach Math 18 October 18, 2017 1 / 17 Question 2 1 2 Let A = 3 4 1 2 3

More information

Lecture 3: Matrix and Matrix Operations

Lecture 3: Matrix and Matrix Operations Lecture 3: Matrix and Matrix Operations Representation, row vector, column vector, element of a matrix. Examples of matrix representations Tables and spreadsheets Scalar-Matrix operation: Scaling a matrix

More information

CITS2401 Computer Analysis & Visualisation

CITS2401 Computer Analysis & Visualisation FACULTY OF ENGINEERING, COMPUTING AND MATHEMATICS CITS2401 Computer Analysis & Visualisation SCHOOL OF COMPUTER SCIENCE AND SOFTWARE ENGINEERING Topic 7 Matrix Algebra Material from MATLAB for Engineers,

More information

Matrix notation. A nm : n m : size of the matrix. m : no of columns, n: no of rows. Row matrix n=1 [b 1, b 2, b 3,. b m ] Column matrix m=1

Matrix notation. A nm : n m : size of the matrix. m : no of columns, n: no of rows. Row matrix n=1 [b 1, b 2, b 3,. b m ] Column matrix m=1 Matrix notation A nm : n m : size of the matrix m : no of columns, n: no of rows Row matrix n=1 [b 1, b 2, b 3,. b m ] Column matrix m=1 n = m square matrix Symmetric matrix Upper triangular matrix: matrix

More information

Matrices and Linear Algebra

Matrices and Linear Algebra Contents Quantitative methods for Economics and Business University of Ferrara Academic year 2017-2018 Contents 1 Basics 2 3 4 5 Contents 1 Basics 2 3 4 5 Contents 1 Basics 2 3 4 5 Contents 1 Basics 2

More information

Chapter 2: Matrices and Linear Systems

Chapter 2: Matrices and Linear Systems Chapter 2: Matrices and Linear Systems Paul Pearson Outline Matrices Linear systems Row operations Inverses Determinants Matrices Definition An m n matrix A = (a ij ) is a rectangular array of real numbers

More information

Formula for the inverse matrix. Cramer s rule. Review: 3 3 determinants can be computed expanding by any row or column

Formula for the inverse matrix. Cramer s rule. Review: 3 3 determinants can be computed expanding by any row or column Math 20F Linear Algebra Lecture 18 1 Determinants, n n Review: The 3 3 case Slide 1 Determinants n n (Expansions by rows and columns Relation with Gauss elimination matrices: Properties) Formula for the

More information

MA 138 Calculus 2 with Life Science Applications Matrices (Section 9.2)

MA 138 Calculus 2 with Life Science Applications Matrices (Section 9.2) MA 38 Calculus 2 with Life Science Applications Matrices (Section 92) Alberto Corso albertocorso@ukyedu Department of Mathematics University of Kentucky Friday, March 3, 207 Identity Matrix and Inverse

More information

LS.1 Review of Linear Algebra

LS.1 Review of Linear Algebra LS. LINEAR SYSTEMS LS.1 Review of Linear Algebra In these notes, we will investigate a way of handling a linear system of ODE s directly, instead of using elimination to reduce it to a single higher-order

More information

Matrices. Chapter Keywords and phrases. 3.2 Introduction

Matrices. Chapter Keywords and phrases. 3.2 Introduction Chapter 3 Matrices 3.1 Keywords and phrases Special matrices: (row vector, column vector, zero, square, diagonal, scalar, identity, lower triangular, upper triangular, symmetric, row echelon form, reduced

More information

Chapter 4. Determinants

Chapter 4. Determinants 4.2 The Determinant of a Square Matrix 1 Chapter 4. Determinants 4.2 The Determinant of a Square Matrix Note. In this section we define the determinant of an n n matrix. We will do so recursively by defining

More information

Numerical Methods Lecture 2 Simultaneous Equations

Numerical Methods Lecture 2 Simultaneous Equations Numerical Methods Lecture 2 Simultaneous Equations Topics: matrix operations solving systems of equations pages 58-62 are a repeat of matrix notes. New material begins on page 63. Matrix operations: Mathcad

More information

Mathematics 13: Lecture 10

Mathematics 13: Lecture 10 Mathematics 13: Lecture 10 Matrices Dan Sloughter Furman University January 25, 2008 Dan Sloughter (Furman University) Mathematics 13: Lecture 10 January 25, 2008 1 / 19 Matrices Recall: A matrix is a

More information

M. Matrices and Linear Algebra

M. Matrices and Linear Algebra M. Matrices and Linear Algebra. Matrix algebra. In section D we calculated the determinants of square arrays of numbers. Such arrays are important in mathematics and its applications; they are called matrices.

More information

POLI270 - Linear Algebra

POLI270 - Linear Algebra POLI7 - Linear Algebra Septemer 8th Basics a x + a x +... + a n x n b () is the linear form where a, b are parameters and x n are variables. For a given equation such as x +x you only need a variable and

More information

1 Matrices and Systems of Linear Equations. a 1n a 2n

1 Matrices and Systems of Linear Equations. a 1n a 2n March 31, 2013 16-1 16. Systems of Linear Equations 1 Matrices and Systems of Linear Equations An m n matrix is an array A = (a ij ) of the form a 11 a 21 a m1 a 1n a 2n... a mn where each a ij is a real

More information

x + 2y + 3z = 8 x + 3y = 7 x + 2z = 3

x + 2y + 3z = 8 x + 3y = 7 x + 2z = 3 Chapter 2: Solving Linear Equations 23 Elimination Using Matrices As we saw in the presentation, we can use elimination to make a system of linear equations into an upper triangular system that is easy

More information

ENGI 9420 Lecture Notes 2 - Matrix Algebra Page Matrix operations can render the solution of a linear system much more efficient.

ENGI 9420 Lecture Notes 2 - Matrix Algebra Page Matrix operations can render the solution of a linear system much more efficient. ENGI 940 Lecture Notes - Matrix Algebra Page.0. Matrix Algebra A linear system of m equations in n unknowns, a x + a x + + a x b (where the a ij and i n n a x + a x + + a x b n n a x + a x + + a x b m

More information

Definition of Equality of Matrices. Example 1: Equality of Matrices. Consider the four matrices

Definition of Equality of Matrices. Example 1: Equality of Matrices. Consider the four matrices IT 131: Mathematics for Science Lecture Notes 3 Source: Larson, Edwards, Falvo (2009): Elementary Linear Algebra, Sixth Edition. Matrices 2.1 Operations with Matrices This section and the next introduce

More information

LINEAR SYSTEMS, MATRICES, AND VECTORS

LINEAR SYSTEMS, MATRICES, AND VECTORS ELEMENTARY LINEAR ALGEBRA WORKBOOK CREATED BY SHANNON MARTIN MYERS LINEAR SYSTEMS, MATRICES, AND VECTORS Now that I ve been teaching Linear Algebra for a few years, I thought it would be great to integrate

More information

OHSx XM511 Linear Algebra: Solutions to Online True/False Exercises

OHSx XM511 Linear Algebra: Solutions to Online True/False Exercises This document gives the solutions to all of the online exercises for OHSx XM511. The section ( ) numbers refer to the textbook. TYPE I are True/False. Answers are in square brackets [. Lecture 02 ( 1.1)

More information

Math 250B Midterm I Information Fall 2018

Math 250B Midterm I Information Fall 2018 Math 250B Midterm I Information Fall 2018 WHEN: Wednesday, September 26, in class (no notes, books, calculators I will supply a table of integrals) EXTRA OFFICE HOURS: Sunday, September 23 from 8:00 PM

More information

Linear Equations and Matrix

Linear Equations and Matrix 1/60 Chia-Ping Chen Professor Department of Computer Science and Engineering National Sun Yat-sen University Linear Algebra Gaussian Elimination 2/60 Alpha Go Linear algebra begins with a system of linear

More information

Phys 201. Matrices and Determinants

Phys 201. Matrices and Determinants Phys 201 Matrices and Determinants 1 1.1 Matrices 1.2 Operations of matrices 1.3 Types of matrices 1.4 Properties of matrices 1.5 Determinants 1.6 Inverse of a 3 3 matrix 2 1.1 Matrices A 2 3 7 =! " 1

More information

88 CHAPTER 3. SYMMETRIES

88 CHAPTER 3. SYMMETRIES 88 CHAPTER 3 SYMMETRIES 31 Linear Algebra Start with a field F (this will be the field of scalars) Definition: A vector space over F is a set V with a vector addition and scalar multiplication ( scalars

More information

MATH 213 Linear Algebra and ODEs Spring 2015 Study Sheet for Midterm Exam. Topics

MATH 213 Linear Algebra and ODEs Spring 2015 Study Sheet for Midterm Exam. Topics MATH 213 Linear Algebra and ODEs Spring 2015 Study Sheet for Midterm Exam This study sheet will not be allowed during the test Books and notes will not be allowed during the test Calculators and cell phones

More information

Introduction to Matrices

Introduction to Matrices POLS 704 Introduction to Matrices Introduction to Matrices. The Cast of Characters A matrix is a rectangular array (i.e., a table) of numbers. For example, 2 3 X 4 5 6 (4 3) 7 8 9 0 0 0 Thismatrix,with4rowsand3columns,isoforder

More information

Matrices Gaussian elimination Determinants. Graphics 2009/2010, period 1. Lecture 4: matrices

Matrices Gaussian elimination Determinants. Graphics 2009/2010, period 1. Lecture 4: matrices Graphics 2009/2010, period 1 Lecture 4 Matrices m n matrices Matrices Definitions Diagonal, Identity, and zero matrices Addition Multiplication Transpose and inverse The system of m linear equations in

More information

Kevin James. MTHSC 3110 Section 2.2 Inverses of Matrices

Kevin James. MTHSC 3110 Section 2.2 Inverses of Matrices MTHSC 3110 Section 2.2 Inverses of Matrices Definition Suppose that T : R n R m is linear. We will say that T is invertible if for every b R m there is exactly one x R n so that T ( x) = b. Note If T is

More information

Linear Algebra review Powers of a diagonalizable matrix Spectral decomposition

Linear Algebra review Powers of a diagonalizable matrix Spectral decomposition Linear Algebra review Powers of a diagonalizable matrix Spectral decomposition Prof. Tesler Math 283 Fall 2016 Also see the separate version of this with Matlab and R commands. Prof. Tesler Diagonalizing

More information

. a m1 a mn. a 1 a 2 a = a n

. a m1 a mn. a 1 a 2 a = a n Biostat 140655, 2008: Matrix Algebra Review 1 Definition: An m n matrix, A m n, is a rectangular array of real numbers with m rows and n columns Element in the i th row and the j th column is denoted by

More information

a11 a A = : a 21 a 22

a11 a A = : a 21 a 22 Matrices The study of linear systems is facilitated by introducing matrices. Matrix theory provides a convenient language and notation to express many of the ideas concisely, and complicated formulas are

More information

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

MAC Module 2 Systems of Linear Equations and Matrices II. Learning Objectives. Upon completing this module, you should be able to : MAC 0 Module Systems of Linear Equations and Matrices II Learning Objectives Upon completing this module, you should be able to :. Find the inverse of a square matrix.. Determine whether a matrix is invertible..

More information

Definition. A matrix is a rectangular array of numbers enclosed by brackets (plural: matrices).

Definition. A matrix is a rectangular array of numbers enclosed by brackets (plural: matrices). Matrices (general theory). Definition. A matrix is a rectangular array of numbers enclosed by brackets (plural: matrices). Examples. 1 2 1 1 0 2 A= 0 0 7 B= 0 1 3 4 5 0 Terminology and Notations. Each

More information

Properties of the Determinant Function

Properties of the Determinant Function Properties of the Determinant Function MATH 322, Linear Algebra I J. Robert Buchanan Department of Mathematics Spring 2015 Overview Today s discussion will illuminate some of the properties of the determinant:

More information

LECTURE 12: SOLUTIONS TO SIMULTANEOUS LINEAR EQUATIONS. Prof. N. Harnew University of Oxford MT 2012

LECTURE 12: SOLUTIONS TO SIMULTANEOUS LINEAR EQUATIONS. Prof. N. Harnew University of Oxford MT 2012 LECTURE 12: SOLUTIONS TO SIMULTANEOUS LINEAR EQUATIONS Prof. N. Harnew University of Oxford MT 2012 1 Outline: 12. SOLUTIONS TO SIMULTANEOUS LINEAR EQUATIONS 12.1 Methods used to solve for unique solution

More information

A primer on matrices

A primer on matrices A primer on matrices Stephen Boyd August 4, 2007 These notes describe the notation of matrices, the mechanics of matrix manipulation, and how to use matrices to formulate and solve sets of simultaneous

More information

Online Exercises for Linear Algebra XM511

Online Exercises for Linear Algebra XM511 This document lists the online exercises for XM511. The section ( ) numbers refer to the textbook. TYPE I are True/False. Lecture 02 ( 1.1) Online Exercises for Linear Algebra XM511 1) The matrix [3 2

More information

Econ 424 Review of Matrix Algebra

Econ 424 Review of Matrix Algebra Econ 424 Review of Matrix Algebra Eric Zivot January 22, 205 Matrices and Vectors Matrix 2 A = 2 22 2 ( )...... 2 = of rows, = of columns Square matrix : = Vector x ( ) = 2. Remarks R is a matrix oriented

More information

+ MATRIX VARIABLES AND TWO DIMENSIONAL ARRAYS

+ MATRIX VARIABLES AND TWO DIMENSIONAL ARRAYS + MATRIX VARIABLES AND TWO DIMENSIONAL ARRAYS Matrices are organized rows and columns of numbers that mathematical operations can be performed on. MATLAB is organized around the rules of matrix operations.

More information

(Mathematical Operations with Arrays) Applied Linear Algebra in Geoscience Using MATLAB

(Mathematical Operations with Arrays) Applied Linear Algebra in Geoscience Using MATLAB Applied Linear Algebra in Geoscience Using MATLAB (Mathematical Operations with Arrays) Contents Getting Started Matrices Creating Arrays Linear equations Mathematical Operations with Arrays Using Script

More information

Introduction. Vectors and Matrices. Vectors [1] Vectors [2]

Introduction. Vectors and Matrices. Vectors [1] Vectors [2] Introduction Vectors and Matrices Dr. TGI Fernando 1 2 Data is frequently arranged in arrays, that is, sets whose elements are indexed by one or more subscripts. Vector - one dimensional array Matrix -

More information

Linear Systems and Matrices

Linear Systems and Matrices Department of Mathematics The Chinese University of Hong Kong 1 System of m linear equations in n unknowns (linear system) 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.......

More information

Lecture 44. Better and successive approximations x2, x3,, xn to the root are obtained from

Lecture 44. Better and successive approximations x2, x3,, xn to the root are obtained from Lecture 44 Solution of Non-Linear Equations Regula-Falsi Method Method of iteration Newton - Raphson Method Muller s Method Graeffe s Root Squaring Method Newton -Raphson Method An approximation to the

More information

3 Fields, Elementary Matrices and Calculating Inverses

3 Fields, Elementary Matrices and Calculating Inverses 3 Fields, Elementary Matrices and Calculating Inverses 3. Fields So far we have worked with matrices whose entries are real numbers (and systems of equations whose coefficients and solutions are real numbers).

More information

M.A.P. Matrix Algebra Procedures. by Mary Donovan, Adrienne Copeland, & Patrick Curry

M.A.P. Matrix Algebra Procedures. by Mary Donovan, Adrienne Copeland, & Patrick Curry M.A.P. Matrix Algebra Procedures by Mary Donovan, Adrienne Copeland, & Patrick Curry This document provides an easy to follow background and review of basic matrix definitions and algebra. Because population

More information

4-1 Matrices and Data

4-1 Matrices and Data 4-1 Matrices and Data Warm Up Lesson Presentation Lesson Quiz 2 The table shows the top scores for girls in barrel racing at the 2004 National High School Rodeo finals. The data can be presented in a table

More information

BCMB/CHEM 8190 Lab Exercise Using Maple for NMR Data Processing and Pulse Sequence Design March 2012

BCMB/CHEM 8190 Lab Exercise Using Maple for NMR Data Processing and Pulse Sequence Design March 2012 BCMB/CHEM 8190 Lab Exercise Using Maple for NMR Data Processing and Pulse Sequence Design March 2012 Introduction Maple is a powerful collection of routines to aid in the solution of mathematical problems

More information

Elementary maths for GMT

Elementary maths for GMT Elementary maths for GMT Linear Algebra Part 2: Matrices, Elimination and Determinant m n matrices The system of m linear equations in n variables x 1, x 2,, x n a 11 x 1 + a 12 x 2 + + a 1n x n = b 1

More information

OR MSc Maths Revision Course

OR MSc Maths Revision Course OR MSc Maths Revision Course Tom Byrne School of Mathematics University of Edinburgh t.m.byrne@sms.ed.ac.uk 15 September 2017 General Information Today JCMB Lecture Theatre A, 09:30-12:30 Mathematics revision

More information

A Review of Matrix Analysis

A Review of Matrix Analysis Matrix Notation Part Matrix Operations Matrices are simply rectangular arrays of quantities Each quantity in the array is called an element of the matrix and an element can be either a numerical value

More information

Section 5.5: Matrices and Matrix Operations

Section 5.5: Matrices and Matrix Operations Section 5.5 Matrices and Matrix Operations 359 Section 5.5: Matrices and Matrix Operations Two club soccer teams, the Wildcats and the Mud Cats, are hoping to obtain new equipment for an upcoming season.

More information

MTH 464: Computational Linear Algebra

MTH 464: Computational Linear Algebra MTH 464: Computational Linear Algebra Lecture Outlines Exam 2 Material Prof. M. Beauregard Department of Mathematics & Statistics Stephen F. Austin State University March 2, 2018 Linear Algebra (MTH 464)

More information

8-15. Stop by or call (630)

8-15. Stop by or call (630) To review the basics Matrices, what they represent, and how to find sum, scalar product, product, inverse, and determinant of matrices, watch the following set of YouTube videos. They are followed by several

More information

MAT 1332: CALCULUS FOR LIFE SCIENCES. Contents. 1. Review: Linear Algebra II Vectors and matrices Definition. 1.2.

MAT 1332: CALCULUS FOR LIFE SCIENCES. Contents. 1. Review: Linear Algebra II Vectors and matrices Definition. 1.2. MAT 1332: CALCULUS FOR LIFE SCIENCES JING LI Contents 1 Review: Linear Algebra II Vectors and matrices 1 11 Definition 1 12 Operations 1 2 Linear Algebra III Inverses and Determinants 1 21 Inverse Matrices

More information

9 Appendix. Determinants and Cramer s formula

9 Appendix. Determinants and Cramer s formula LINEAR ALGEBRA: THEORY Version: August 12, 2000 133 9 Appendix Determinants and Cramer s formula Here we the definition of the determinant in the general case and summarize some features Then we show how

More information

MATRICES AND MATRIX OPERATIONS

MATRICES AND MATRIX OPERATIONS SIZE OF THE MATRIX is defined by number of rows and columns in the matrix. For the matrix that have m rows and n columns we say the size of the matrix is m x n. If matrix have the same number of rows (n)

More information

Matrix Operations: Determinant

Matrix Operations: Determinant Matrix Operations: Determinant Determinants Determinants are only applicable for square matrices. Determinant of the square matrix A is denoted as: det(a) or A Recall that the absolute value of the determinant

More information

1 procedure for determining the inverse matrix

1 procedure for determining the inverse matrix table of contents 1 procedure for determining the inverse matrix The inverse matrix of a matrix A can be determined only if the determinant of the matrix A is different from zero. The following procedures

More information