2.1: MATRIX OPERATIONS

Similar documents
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

1 Introduction to Matrices

MAT 200, Midterm Exam Solution. a. (5 points) Compute the determinant of the matrix A =

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS

MATH 304 Linear Algebra Lecture 9: Subspaces of vector spaces (continued). Span. Spanning set.

Solutions to Math 51 First Exam January 29, 2015

Name: Section Registered In:

Lecture 2 Matrix Operations

MATH 304 Linear Algebra Lecture 18: Rank and nullity of a matrix.

MAT188H1S Lec0101 Burbulla

MATH APPLIED MATRIX THEORY

18.06 Problem Set 4 Solution Due Wednesday, 11 March 2009 at 4 pm in Total: 175 points.

T ( a i x i ) = a i T (x i ).

LINEAR ALGEBRA. September 23, 2010

A =

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

Lecture Notes 2: Matrices as Systems of Linear Equations

MAT 242 Test 2 SOLUTIONS, FORM T

( ) which must be a vector

8 Square matrices continued: Determinants

Math 312 Homework 1 Solutions

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

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

Solutions to Homework Section 3.7 February 18th, 2005

Reduced echelon form: Add the following conditions to conditions 1, 2, and 3 above:

Recall that two vectors in are perpendicular or orthogonal provided that their dot

Solving Linear Systems, Continued and The Inverse of a Matrix

Similar matrices and Jordan form

University of Lille I PC first year list of exercises n 7. Review

Orthogonal Diagonalization of Symmetric Matrices

Linear Algebra Review. Vectors

Linearly Independent Sets and Linearly Dependent Sets

Linear Algebra Notes

Review Jeopardy. Blue vs. Orange. Review Jeopardy

160 CHAPTER 4. VECTOR SPACES

Similarity and Diagonalization. Similar Matrices

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

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

Examination paper for TMA4115 Matematikk 3

Question 2: How do you solve a matrix equation using the matrix inverse?

Section 5.3. Section 5.3. u m ] l jj. = l jj u j + + l mj u m. v j = [ u 1 u j. l mj

Systems of Linear Equations

x = + x 2 + x

MATH2210 Notebook 1 Fall Semester 2016/ MATH2210 Notebook Solving Systems of Linear Equations... 3

Matrix Algebra. Some Basic Matrix Laws. Before reading the text or the following notes glance at the following list of basic matrix algebra laws.

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.

Orthogonal Projections

Chapter 7. Matrices. Definition. An m n matrix is an array of numbers set out in m rows and n columns. Examples. (

Vector Spaces 4.4 Spanning and Independence

Using row reduction to calculate the inverse and the determinant of a square matrix

Brief Introduction to Vectors and Matrices

Subspaces of R n LECTURE Subspaces

NOTES ON LINEAR TRANSFORMATIONS

Notes on Determinant

α = u v. In other words, Orthogonal Projection

These axioms must hold for all vectors ū, v, and w in V and all scalars c and d.

1 Sets and Set Notation.

Math 215 HW #6 Solutions

9 MATRICES AND TRANSFORMATIONS

[1] Diagonal factorization

Introduction to Matrices for Engineers

CS3220 Lecture Notes: QR factorization and orthogonal transformations

Notes on Symmetric Matrices

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

4.5 Linear Dependence and Linear Independence

is in plane V. However, it may be more convenient to introduce a plane coordinate system in V.

Linear Algebra Notes for Marsden and Tromba Vector Calculus

Notes on Orthogonal and Symmetric Matrices MENU, Winter 2013

Linear Algebraic Equations, SVD, and the Pseudo-Inverse

Abstract: We describe the beautiful LU factorization of a square matrix (or how to write Gaussian elimination in terms of matrix multiplication).

Eigenvalues and Eigenvectors

Methods for Finding Bases

Matrices and Linear Algebra

SYSTEMS OF EQUATIONS AND MATRICES WITH THE TI-89. by Joseph Collison

Data Mining: Algorithms and Applications Matrix Math Review

1 VECTOR SPACES AND SUBSPACES

Vector Notation: AB represents the vector from point A to point B on a graph. The vector can be computed by B A.

Chapter 19. General Matrices. An n m matrix is an array. a 11 a 12 a 1m a 21 a 22 a 2m A = a n1 a n2 a nm. The matrix A has n row vectors

Lecture 5: Singular Value Decomposition SVD (1)

6. Cholesky factorization

LINEAR ALGEBRA W W L CHEN

DETERMINANTS TERRY A. LORING

Solving Systems of Linear Equations

Lecture 4: Partitioned Matrices and Determinants

Matrix Representations of Linear Transformations and Changes of Coordinates

Mathematics Course 111: Algebra I Part IV: Vector Spaces

Vector and Matrix Norms

Typical Linear Equation Set and Corresponding Matrices

Here are some examples of combining elements and the operations used:

Lectures notes on orthogonal matrices (with exercises) Linear Algebra II - Spring 2004 by D. Klain

Boolean Algebra Part 1

Direct Methods for Solving Linear Systems. Matrix Factorization

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.

Row Echelon Form and Reduced Row Echelon Form

26. Determinants I. 1. Prehistory

Applied Linear Algebra I Review page 1

Eigenvalues, Eigenvectors, Matrix Factoring, and Principal Components

Matrix Algebra in R A Minimal Introduction

Section Continued

MATHEMATICS FOR ENGINEERS BASIC MATRIX THEORY TUTORIAL 2

Transcription:

.: MATRIX OPERATIONS What are diagonal entries and the main diagonal of a matrix? What is a diagonal matrix? When are matrices equal? Scalar Multiplication 45

Matrix Addition Theorem (pg 0) Let A, B, and C be matrices of the same size, and let r & s be scalars..) A + B B+ A.) (A + B) + C A+ (B+ C).) A + 0 A 4.) r(a + B) ra+ rb 5.) (r + s)a ra+ sa 6.) r(sa) (rs)a Example 6 4 Let A and 0 8 B 5. Compute each of the following: 4 - A B - A A + B 46

MATRIX MULTIPLICATION How do I multiply matrices? Row-Column Rule For Computing AB Theorem (pg 4) Let A be an mxn matrix, and let B & C have sizes for which the indicated sums and products are defined. Let r be any scalar..) A(BC) (AB)C.) A(B + C) AB+ AC.) (B + C)A BA+ CA 4.) r(ab) (ra)b A(rB) 5.) I ma A AIn Associative law of multiplication Left distributive law Right distributive law Identity for matrix multiplication 47

Example 0 5 Let A, B, and C. 4 5 4 Compute each of the following: AC CA BC CB 48

.) In general, AB BA. WARNINGS!.) The cancellation laws do NOT hold for matrix multiplication. That is, if AB AC then it is NOT necessarily true thatb C..) If a product AB is the zero matrix, you CANNOT conclude in general that A 0 or B 0. What is the transpose of a matrix? 49

Theorem (pg 6) Let A and B denote matrices whose sizes are appropriate for the following sums and products and let r be any scalar. T T.) ( A ) A T A + B A + B T T.) ( ) T T.) ( ra ) ra T T 4.) ( AB ) B A T Example 5 Let A and x. Compute each of the following: 4 6 T ( Ax ) x T A T T xx x T x Is A T x T defined? Why or why not. 50

When is a matrix A invertible?.: THE INVERSE OF A MATRIX Singular Matrix vs. Nonsingular Matrix Theorem 4 (CAUTION: THIS ONLY WORKS FOR x MATRICES!) Example Find the inverse (if it exists) of each of the following matrices. 8 4 6 6 4 8 5

Theorem 5 Theorem 6 Theorem 7 Algorithm for Finding A (pg 5).) Set up the augmented matrix [A I]..) Row reduce the matrix into reduced echelon form..) If A is row equivalent to I, then [A I] is row equivalent to [I have an inverse. A ]. Otherwise, A does not 5

5 Example Let 8 0 5 A and 5 4 b. Find A and use it to solve b x A.

Example Let A, B, C, D, X, and Y be invertible nxn matrices. Solve the equation, A ( B CX) D Y + for X. Things to keep in mind: Matrix division does not exist. You cannot divide by a matrix. Also, keep the order of multiplication consistent. If you multiply by A, on the left of the left side of the equation you must multiply by A on the left of the right side of the equation as well. 54

.: CHARACTERIZATIONS OF INVERTIBLE MATRICES Theorem 8: The Invertible Matrix Theorem (pg 6) Let A be a square nxn matrix. Then the following statements are equivalent. That is, for a given A, the statements are either all true or all false..) A is an invertible matrix..) A is row equivalent to the nxn identity matrix, I n..) A has n pivot positions. 4.) The equation A x 0 has only the trivial solution. 5.) The columns of A form a linearly independent set. 6.) The linear transformation n n T : R R given by T ( x) Ax 7.) The equation A x b has exactly one solution for each b in is one-to-one. n R. 8.) The columns of A span 9.) The linear transformation n R. n n T : R R given by T ( x) Ax 0.) There is an nxn matrix C such that CA AC In. is onto..) T A is an invertible matrix. 55

What is an invertible transformation? Theorem 9 What if T : R n n R is one-to-one? Onto? 56

Example x x x Let T : R R by T x x+ x x be a linear transformation. Show that T is x 6x+ 4x invertible and find T..4 SUBSPACES OF n R 57

What is a vector space? What is a subspace? What is the subspace test? 58

Example Determine which of the following are subspaces of R. 59

Example Use the subspace test to determine if the following is a subspace of x+ y H y x x,y R x+ 4y R. 60

6 Example Use the subspace test to determine if the following is a subspace of R. + R a,b,c c b a c b W

What is ColA? What is NulA? Theorem 0 6

Example 4 Let A. 7 8 5 7 0 What is ColA? ColA is a subspace of What is NulA? k R, what is k in this example? NulA is a subspace of s R, what is s in this example? 6

64 What is a basis? What is the standard basis for n R? Theorem Example 5 Let 0 7 5 8 7 A. Find a basis for ColA and NulA.

65 Example 6 Determine which sets are bases for R or R. Justify each answer.,,,,, 0 0,, 5 7 8

66.5 DIMENSION & RANK What is the dimension of a subspace? Example Determine the dimension of the subspace H of R spanned by the vectors v, v, 7 0 v, 4 6 7 v 4.

What is the rank of a matrix? Theorem (The Rank Theorem) Example Suppose a x5 matrix A has pivot columns. Is ColA R? Is NulA R? Suppose a 4x7 matrix A has pivot columns. Is ColA R? What is the dimension of NulA? 67

Example Construct a 4x matrix with rank. Theorem (The Basis Theorem) 68

69 Example 4 Let 4 5 4 0 A. Is the set 0, 0, 9 0 9 6 S a basis for ColA?

Theorem 8: The Invertible Matrix Theorem (pg 90) Let A be a square nxn matrix. Then the following statements are equivalent. That is, for a given A, the statements are either all true or all false..) A is an invertible matrix..) A is row equivalent to the nxn identity matrix, I n..) A has n pivot positions. 4.) The equation A x 0 has only the trivial solution. 5.) The columns of A form a linearly independent set. 6.) The linear transformation n n T : R R given by T ( x) Ax 7.) The equation A x b has exactly one solution for each b in is one-to-one. n R. 8.) The columns of A span 9.) The linear transformation n R. n n T : R R given by T ( x) Ax 0.) There is an nxn matrix C such that CA AC In. is onto..) T A is an invertible matrix..) The columns of A form a basis for.) ColA n R 4.) dim ( ColA) n 5.) rank ( A) n 6.) NulA {0} 7.) dim ( NulA) 0 8.) det A 0 n R. 70