MATRICES AND MATRIX OPERATIONS

Similar documents
Chapter 3. Determinants and Eigenvalues

Graduate Mathematical Economics Lecture 1

Undergraduate Mathematical Economics Lecture 1

ECON 186 Class Notes: Linear Algebra

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

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

Linear Systems and Matrices

Matrix operations Linear Algebra with Computer Science Application

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

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

Linear Algebra: Lecture notes from Kolman and Hill 9th edition.

Lecture Notes in Linear Algebra

Fundamentals of Engineering Analysis (650163)

Introduction to Determinants

Chapter 2 Notes, Linear Algebra 5e Lay

3 Matrix Algebra. 3.1 Operations on matrices

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS. + + x 1 x 2. x n 8 (4) 3 4 2

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

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

Graphics (INFOGR), , Block IV, lecture 8 Deb Panja. Today: Matrices. Welcome

Math Camp Notes: Linear Algebra I

Things we can already do with matrices. Unit II - Matrix arithmetic. Defining the matrix product. Things that fail in matrix arithmetic

Matrix Operations: Determinant

Matrices. In this chapter: matrices, determinants. inverse matrix

Chapter 2. Square matrices

Elementary maths for GMT

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.

ENGR-1100 Introduction to Engineering Analysis. Lecture 21

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

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

Foundations of Cryptography

Math 240 Calculus III

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

Linear Algebra and Matrix Inversion

Matrices and Determinants

CLASS 12 ALGEBRA OF MATRICES

M. Matrices and Linear Algebra

Properties of the Determinant Function

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

Chapter 4 - MATRIX ALGEBRA. ... a 2j... a 2n. a i1 a i2... a ij... a in

Section 12.4 Algebra of Matrices

Matrices and Linear Algebra

2.1 Matrices. 3 5 Solve for the variables in the following matrix equation.

1111: Linear Algebra I

Linear Algebra (part 1) : Matrices and Systems of Linear Equations (by Evan Dummit, 2016, v. 2.02)

Math Bootcamp An p-dimensional vector is p numbers put together. Written as. x 1 x =. x p

Linear Algebra Primer

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

Determinants. Samy Tindel. Purdue University. Differential equations and linear algebra - MA 262

c c c c c c c c c c a 3x3 matrix C= has a determinant determined by

Determinants by Cofactor Expansion (III)

Linear Equations in Linear Algebra

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

Phys 201. Matrices and Determinants

We could express the left side as a sum of vectors and obtain the Vector Form of a Linear System: a 12 a x n. a m2

ENGR-1100 Introduction to Engineering Analysis. Lecture 21. Lecture outline

MATH Mathematics for Agriculture II

1300 Linear Algebra and Vector Geometry

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

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

Matrix Algebra 2.1 MATRIX OPERATIONS Pearson Education, Inc.

Matrices and Determinants for Undergraduates. By Dr. Anju Gupta. Ms. Reena Yadav

Prepared by: M. S. KumarSwamy, TGT(Maths) Page

Review from Bootcamp: Linear Algebra

Linear Algebra V = T = ( 4 3 ).

Evaluating Determinants by Row Reduction

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

Equality: Two matrices A and B are equal, i.e., A = B if A and B have the same order and the entries of A and B are the same.

ICS 6N Computational Linear Algebra Matrix Algebra

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

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

Introduction to Matrix Algebra

1 Matrices and Systems of Linear Equations

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

Lemma 8: Suppose the N by N matrix A has the following block upper triangular form:

Introduction to Matrices

Fall Inverse of a matrix. Institute: UC San Diego. Authors: Alexander Knop

Elementary Row Operations on Matrices

Lesson 3. Inverse of Matrices by Determinants and Gauss-Jordan Method

a 11 a 12 a 11 a 12 a 13 a 21 a 22 a 23 . a 31 a 32 a 33 a 12 a 21 a 23 a 31 a = = = = 12

MATH2210 Notebook 2 Spring 2018

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

Matrices. 1 a a2 1 b b 2 1 c c π

* is a row matrix * An mxn matrix is a square matrix if and only if m=n * A= is a diagonal matrix if = 0 i

Linear Algebra Primer

Ma 227 Review for Systems of DEs

1 Determinants. 1.1 Determinant

Applied Matrix Algebra Lecture Notes Section 2.2. Gerald Höhn Department of Mathematics, Kansas State University

TOPIC III LINEAR ALGEBRA

Glossary of Linear Algebra Terms. Prepared by Vince Zaccone For Campus Learning Assistance Services at UCSB

n n matrices The system of m linear equations in n variables x 1, x 2,..., x n can be written as a matrix equation by Ax = b, or in full

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

Linear Equations and Matrix

7.6 The Inverse of a Square Matrix

Introduction to Matrices and Linear Systems Ch. 3

SCHOOL OF BUSINESS, ECONOMICS AND MANAGEMENT BUSINESS MATHEMATICS / MATHEMATICAL ANALYSIS

Chapter 4. Determinants

Linear Algebra. Linear Equations and Matrices. Copyright 2005, W.R. Winfrey

Matrix Arithmetic. j=1

MTH 102A - Linear Algebra II Semester

Transcription:

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) and columns (n), we call that matrix the squared (nxn) matrix. column 4 0 ] 7 9 4x 4 7 6 ] x 7 4 ] 4 9 x a a a n a a a n a m a m a mn ]mxn row squared x matrix SPECIAL MATRICES ) Zero Matrix matrix that has all elements equal to 0; The notation for this matrix is O. ) Identity Matrix matrix that has all s on the diagonal; The notation for this matrix is I, but in some books, it can be E. O = 0], 0 0 0 0 0 0 0 ], 0 0 0], etc. I = ], 0 0 0 0 ], 0 0], etc. 0 0 0 0 0 ADDITION AND SUBTRACTION We can add or subtract only matrices that are same sizes. A = 4 7 6 ] x B = 4 6 ] x 4 0 C = ] 7 9 Matrices A and B are the same sizes, because they both have rows and columns, matrix C has the different size. So we can only do addition or subtraction with matrices A and B. For example, we can do A B. A B = 4 7 6 ] 4 6 ] = 4 7 4 6 6 ] = 0 4 4 4 ] SCALAR MULTIPLES If A is the matrix and c is the scalar (any number) then ca (this is the same as c x A) is the matrix that we get when we multiply each entry of the matrix A with the scalar c. 7 4 x7 x x4 6 a) A = ] = x x x] = 6 9 ] 4 9 x4 x x9 4 7 7 4 7/ b) /A = / ] = / /] 4 9 4 9/ 4x

MULTIPLYING MATRICES We can multiply only matrices where the first matrix has the number of columns same as the number of rows of the second matrix. And new matrix AB will have same number of rows as the first matrix, and same number of columns as the second matrix. The next drawing will help you to understand. A B = AB m x r r x n = m x n Note: A x B = AB and B x A = BA, but when we are multiplying matrices AB isn t the same as BA. Can we multiply next matrices?!!! AB BA 4 0 ) A = 4 6 0 ] B = ] A B = AB 7 9 x 4 x =? Matrix A has size x ( rows and columns), and matrix B has size 4x (4 rows and columns). The number of columns of the matrix A is, and the number of the rows of the matrix B is 4, as these numbers are not the same we CAN T multiply these two matrices. ) A = 4 4 4 6 0 ] B = 0 ] 7 A B = AB x x 4 = x 4 Matrix A has size x ( rows and columns), and matrix B has size x4 ( rows and 4 columns). The number of columns of the matrix A is, and the number of the rows of the matrix B is, as these numbers are the same we CAN multiply these two matrices. Now, when we know that we can multiply A and B, we need to see what is going to be the size of the product matrix AB. Number of the rows of the matrix AB is equal to the number of the rows of the matrix A, it is. Number of the column of the matrix AB is equal to the number of the columns of the matrix B, it is 4. Thus, the size of the matrix AB is x 4.

Now, we know that we can multiply these matrices, but how do we multiply matrices? We multiply each row of the first matrix with each column of the second matrix and put values in the specific order. AB = 4 4 4 6 0 ] st row x st col st r x nd c st r x rd c st r x 4th c 0 ] = nd row x st col nd r x nd c nd r x rd c nd r x 4th c ] 7 How do we multiply row with column? The best way to explain this is with an example. We are going to multiply the st row of the matrix A with the st column of the matrix B. 4 The first row of A is 4], the first column of B is 0]. We are multiplying first number of the row, with the first number of the column, second number of the row with the second number of the column and third number of the row with the third number of the column. When we have these three numbers we are going to add them, and their sum will be the number we are going to put in the first row and in the first column of the matrix AB. st row x st column: x4 + x0 + 4x = 4 + 0 + =. Now we have AB = ]. 7 st row x nd column: x + x(-) + 4x7 = - + = 7 => AB = ] 7 0 st row x rd column: x4 + x + 4x = 4 + 6 + 0 = 0 => AB = ] st row x 4th column: x + x + 4x = + + = => AB = ] nd row x st column: x4 + 6x0 + 0x = + 0 + 0 = => AB = ] nd row x nd column: x + 6x(-) + 0x7 = -6 + 0 = -4 = > AB = 4 ] nd row x rd column: x4 + 6x + 0x = + + 0 = 6 => AB = 4 6 ] nd row x st column: x + 6x + 0x = 6 + 6 + 0 = => AB = 4 6 ] Rules for multiplying matrices: (AB)C = A(BC) AB BA k(ab) = (ka)b = A(kB), k is scalar (number) OA = AO = O, O is zero matrix A(B ± C) = AB ± AC and (B ± C)A = BA ± CA IA = AI = A, I is Identity matrix

TRANSPOSE OF THE MATRIX It is a new matrix that we get when rows and columns of the matrix A change places, transpose of A is denoted by A T. If matrix A has size m x n, then matrix A T will have size n x m, because we have changed row and columns. a) A = 7 4 4 6 ] AT =? b) B = ] B T =? 9 To find A T rows and columns of matrix A need to change places. We will put the first row ] as a first column of A T, and the second row 4 6] as second column of the A T. Do the same for B T. 7 a) A T = 4] b) B T = ] 6 4 9 Note: If matrix is squared and A = A T we say that it is SYMMETRIC MATRIX. A = 4 7 ] if we change places for rows and columns we will get A T = 4 7 ] 7 6 7 6 From here we can see A = A T, thus this matrix is symmetric. Rules for transpose (if the sizes of matrices are such that stated operations can be performed): (A T ) T = A (ka) T = ka T, k is scalar (number) (A ± B) T = A T ± B T (AB) T = B T A T MINORS OF MATRIX In this handout we will only cover minors for x matrices, but similarly it can be calculated for any squared matrix For doing this we need to know determinate of the matrix x. A = a b c d ] det(a) = a b = ad bc c d If A is a squared matrix, then the minor, denoted by Mij, of element aij is the determinate of submatrix that remains after the ith row and jth column are deleted from A. a a a A = a a a a a a ]x a a a det(a) = a a a a a a

If we want M, since that is the minor that correspond to element a, we are going to cover row and column, everything that is left we will write in the same order in our minor. a a a a a a M = a a a a a a a Now we are going to do the same thing for every minor. M = a a a a M = a a a a M = a a a a M = a a a a M = a a a a M = a a a a M = a a a a M = a a a a COFACTORS - the number Cij = (-) i+j x Mij is the cofactor of element aij ADJOINT OF THE MATRIX (ADJUGATE) C C C +M M +M M = C C C ] = M +M M ] = C C C +M M +M + a a a a a a a a + a a a a a a a a + a a a a a a a a + a a a a a a a a + a a a a ] adj(a) = M T Note that signs (+ or -) are easy to remember where to put the right one. Start with + and then -, +, -, +, etc. INVERSE OF MATRIX -If A is the square matrix and B is the same size of A. If matrix B can be find such that AB = BA = I, then A is said to be invertible (nonsingular, if det(a) 0), and B is called an inverse of A. If there is no such matrix B, then A is not invertible (singular, if det(a) = 0). Notation for the inverse of matrix A is A -. If B is inverse of A, then B = A -. AB = BA = I or AA - = A - A = I Inverse of matrix A (for all squared matrices) can be found using this formula: A - = Inverse of x matrix A = a b c d ] is A- = adj(a) = det(a) d ad bc det(a) adj(a). b ], if and only if det(a) 0. c a

a) A = 6 ], A- = 0 b) A = 0 ] 0 Subject: Linear Algebra x d ad bc, A - =? b c a ] = 6x x 6 ] = 7 6 ] = M = = x 0x = 6 M = 0 = M = 0 0 0 = - M = 0 0 = 0 M = = M = 0 0 = - M = 0 0 = 0 M = = M = 0 0 = +6 ( ) +( ) 6 6 0 0 M = 0 + ( ) ] = 0 ] adj(a) = M T = ] +0 + 0 Help Determinant of only x matrix can be find using Sarrus rule. We write first two columns of the determinate to the right of the determinant (in that order). Then we are adding the products of the diagonals, going from the top to bottom (dashed lines), and subtract products of the diagonals going from the bottom to the top (solid lines). 0 det(a)= 0 0 = xx + xx+ 0x0x0 -xx0-0xx- x0x = 6 +0 +0 0-0 - 0 = 0 0 6 0 0 Finally, A - = adj(a) = ] = det(a) Rules for inverse (if A is invertible): 6 (A - ) T = (A T ) - (A - ) - = A 0 0 ] = 7 7 7 6 7 ]. ] (AB) - = B - A - (ka) - = k - A - = k A-, k is nonzero scalar (AA An) - = An - A - A - References: The following work were referred to during the creation of this handout: Elementary Linear Algebra, Application Version, th Ed., Howard Anthon, Chriss Roress; and http://www.matematiranje.in.rs