Matrix multiplications that do row operations

Size: px
Start display at page:

Download "Matrix multiplications that do row operations"

Transcription

1 May 6, 204 Matrix multiplications that do row operations page Matrix multiplications that do row operations Introduction We have yet to justify our method for finding inverse matrices using row operations: that if row ops. there s a sequence of row operations [ A I] [ I B], then B = A. The use of the side-by-side matrix is really just a way to carry out an identical set of row operations on two different matrices, so what we really need to explain is: If there s a sequence of row operations that turns A into I and that turns I into B, why does B = A? An illuminating justification comes from learning a connection between row operations and matrix multiplication. The basic idea is that every row operation can be done by multiplying by an appropriate matrix. Finding a matrix for each row operation When we were solving linear systems using matrices, we used three kinds of row operations: (a) adding a multiple of one row to another row, (b) multiplying a row by a number, and (c) swapping two rows. For any such row operation, it s possible to find a matrix multiplication that does that row operation. Example: Find the matrix multiplication that does R + 3R2 R to any 2-by-3 matrix. Answer: 0 3 a b c a 3d b 3e c 3 f. d e f d e f. For each of the row operations listed below, find the 2-by-2 matrix that does that row operation. Check your answer by multiplying your chosen matrix with the general 2-by-3 a b c matrix. d e f a. 3R2 R2 b. 2 R R c. R2 4R R2

2 May 6, 204 Matrix multiplications that do row operations page 2 d. R R2 R e. Swap R R2. f. R2 R2 2. Write the 3-by-3 matrix that would do each of these row operations on a 3-by-4 matrix. [You do not have to write out the multiplications that confirm your answers, unless you are unsure and want to check whether you are right.] a. 2R3 R3 e. Swap R R3 b. Swap R2 R3 f. R3 5R R3 c. R2 + 4R R2 g. R R d. 4 R2 R2 h. R 2 R3 R

3 May 6, 204 Matrix multiplications that do row operations page 3 Summary: the elementary matrices An elementary matrix is a matrix that (when multiplied on the left) performs one of the basic row operations. All of the matrices found in problems and 2 are examples. Here is a summary of what the elementary matrices for each type of row operation will look like. type of row operation what the matrix must look like examples (3-by-3 s from problem 2) add a multiple of a row to almost the identity matrix, but with 0 0 another row a single 0 changed to another number R 3 5R R R 2 2 R 3 R multiply a row by a almost the identity matrix, but with 0 0 number a single changed to the number 2R 3 R swap two rows almost the identity matrix, but with 0 0 two pairs of s and 0 s reversed Swap R 2 R Why the inverse-finding procedure works Given: there exists a sequence of row operations that turns A into I and that turns I into B. To prove: that B = A. Proof: Call the elementary matrices that do the sequence of row operations: E, E2,, En. Based on what s given, we know that En E2 E A = I and En E2 E I = B Multiply the first equation on the right by A, and just simplify the second equation, to get En E2 E = A and En E2 E = B The left sides of these equations are identical, so transitivity gives that B = A. Reverse row operations and inverses of elementary matrices For every row operation, there s a row operation that reverses it. Whenever row operations reverse each other, the matrices that do the operations are inverses of each other. Example: R3 5R R3 can be reversed by doing R3 + 5R R Matrices 0 0 and 0 0 are inverses of each other Take En E2 E = A from the proof above. Multiply on the left by En then successively by each of the other elementary matrix inverses. This leads to I = A E E2 En. Finally a multiplication by A gives A = E E2 En. This means that we ve found a factorization of A as a product of elementary matrices.

4 May 6, 204 Matrix multiplications that do row operations page 4 Homework problems 3. Below are several row operations for matrices with 4 rows. For each: Write the 4-by-4 elementary matrix E that does this row operation. Find the row operation that reverses the given row operation, and write its elementary matrix. Check your answer by finding E on your calculator, which should be the matrix you wrote for the reverse row operation. a. 3R4 R4 b. R2 + 5R4 R2 c. R3 2R R3 d. Swap R2 R4 e. 2 5 R2 R2 f. R R4 R

5 May 6, 204 Matrix multiplications that do row operations page a. The inverse of matrix is the matrix 2 5. This inverse can be found using 2 3 row operations: row ops Following our standard procedure for choosing the row operations, there should be four row operation steps. Find these four row operations, and identify them below. Also record the matrix after each step. Identify your st row operation: Identify your 2nd row operation: Identify your 3rd row operation: Identify your 4th row operation: b. Write down the elementary matrix corresponding to each of the four row operations. E = E2 = E3 = E4 = c. Compute the matrix multiplication E4 E3 E2 E (do it by hand: first multiply E4 E3, then multiply by E2, then multiply by E. (If you do everything correctly, the final answer should be A.) d. Write the inverses of the elementary matrices from above. (The easiest way to find them may be to first identify the row operation that reverses each row operation.) E = E2 = E3 = E4 = e. Compute the matrix multiplication E E2 E3 E4 to verify that it equals A. If so, you have just found a factorization of A.

6 May 6, 204 Matrix multiplications that do row operations page Let A = 0. Repeating the methods of problem 4, calculate A, and find a factorization of A as a product of elementary matrices. 6. Using the inverse matrix found in problem 5, solve the linear system x + 5z = 2 y z = 0 3x + 4y + 2z = 8 Hint: Write the system in the form AX = C where X = x. Then AX = C X = A C. y z

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

Elementary Matrices. MATH 322, Linear Algebra I. J. Robert Buchanan. Spring Department of Mathematics Elementary Matrices MATH 322, Linear Algebra I J. Robert Buchanan Department of Mathematics Spring 2015 Outline Today s discussion will focus on: elementary matrices and their properties, using elementary

More information

Lecture 22: Section 4.7

Lecture 22: Section 4.7 Lecture 22: Section 47 Shuanglin Shao December 2, 213 Row Space, Column Space, and Null Space Definition For an m n, a 11 a 12 a 1n a 21 a 22 a 2n A = a m1 a m2 a mn, the vectors r 1 = [ a 11 a 12 a 1n

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

1. Solve each linear system using Gaussian elimination or Gauss-Jordan reduction. The augmented matrix of this linear system is

1. Solve each linear system using Gaussian elimination or Gauss-Jordan reduction. The augmented matrix of this linear system is Solutions to Homework Additional Problems. Solve each linear system using Gaussian elimination or Gauss-Jordan reduction. (a) x + y = 8 3x + 4y = 7 x + y = 3 The augmented matrix of this linear system

More information

Homework 1.1 and 1.2 WITH SOLUTIONS

Homework 1.1 and 1.2 WITH SOLUTIONS Math 220 Linear Algebra (Spring 2018) Homework 1.1 and 1.2 WITH SOLUTIONS Due Thursday January 25 These will be graded in detail and will count as two (TA graded) homeworks. Be sure to start each of these

More information

4.5 Integration of Rational Functions by Partial Fractions

4.5 Integration of Rational Functions by Partial Fractions 4.5 Integration of Rational Functions by Partial Fractions From algebra, we learned how to find common denominators so we can do something like this, 2 x + 1 + 3 x 3 = 2(x 3) (x + 1)(x 3) + 3(x + 1) (x

More information

Updated: January 16, 2016 Calculus II 7.4. Math 230. Calculus II. Brian Veitch Fall 2015 Northern Illinois University

Updated: January 16, 2016 Calculus II 7.4. Math 230. Calculus II. Brian Veitch Fall 2015 Northern Illinois University Math 30 Calculus II Brian Veitch Fall 015 Northern Illinois University Integration of Rational Functions by Partial Fractions From algebra, we learned how to find common denominators so we can do something

More information

1111: Linear Algebra I

1111: Linear Algebra I 1111: Linear Algebra I Dr. Vladimir Dotsenko (Vlad) Michaelmas Term 2015 Dr. Vladimir Dotsenko (Vlad) 1111: Linear Algebra I Michaelmas Term 2015 1 / 8 Elementary matrices Let us define elementary matrices.

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

Honors Advanced Mathematics Determinants page 1

Honors Advanced Mathematics Determinants page 1 Determinants page 1 Determinants For every square matrix A, there is a number called the determinant of the matrix, denoted as det(a) or A. Sometimes the bars are written just around the numbers of the

More information

CH 54 PREPARING FOR THE QUADRATIC FORMULA

CH 54 PREPARING FOR THE QUADRATIC FORMULA 1 CH 54 PREPARING FOR THE QUADRATIC FORMULA Introduction W e re pretty good by now at solving equations like (3x 4) + 8 10(x + 1), and we ve had a whole boatload of word problems which can be solved by

More information

MATH10212 Linear Algebra B Homework Week 3. Be prepared to answer the following oral questions if asked in the supervision class

MATH10212 Linear Algebra B Homework Week 3. Be prepared to answer the following oral questions if asked in the supervision class MATH10212 Linear Algebra B Homework Week Students are strongly advised to acquire a copy of the Textbook: D. C. Lay Linear Algebra its Applications. Pearson, 2006. ISBN 0-521-2871-4. Normally, homework

More information

Name: MATH 3195 :: Fall 2011 :: Exam 2. No document, no calculator, 1h00. Explanations and justifications are expected for full credit.

Name: MATH 3195 :: Fall 2011 :: Exam 2. No document, no calculator, 1h00. Explanations and justifications are expected for full credit. Name: MATH 3195 :: Fall 2011 :: Exam 2 No document, no calculator, 1h00. Explanations and justifications are expected for full credit. 1. ( 4 pts) Say which matrix is in row echelon form and which is not.

More information

4.3 Row operations. As we have seen in Section 4.1 we can simplify a system of equations by either:

4.3 Row operations. As we have seen in Section 4.1 we can simplify a system of equations by either: 4.3 Row operations As we have seen in Section 4.1 we can simplify a system of equations by either: 1. Swapping the order of the equations around. For example: can become 3x 1 + 7x 2 = 9 x 1 2x 1 = 2 x

More information

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

ENGR-1100 Introduction to Engineering Analysis. Lecture 21. Lecture outline ENGR-1100 Introduction to Engineering Analysis Lecture 21 Lecture outline Procedure (algorithm) for finding the inverse of invertible matrix. Investigate the system of linear equation and invertibility

More information

Lecture 9: Elementary Matrices

Lecture 9: Elementary Matrices Lecture 9: Elementary Matrices Review of Row Reduced Echelon Form Consider the matrix A and the vector b defined as follows: 1 2 1 A b 3 8 5 A common technique to solve linear equations of the form Ax

More information

Chapter 3: Theory Review: Solutions Math 308 F Spring 2015

Chapter 3: Theory Review: Solutions Math 308 F Spring 2015 Chapter : Theory Review: Solutions Math 08 F Spring 05. What two properties must a function T : R m R n satisfy to be a linear transformation? (a) For all vectors u and v in R m, T (u + v) T (u) + T (v)

More information

ENGR-1100 Introduction to Engineering Analysis. Lecture 21

ENGR-1100 Introduction to Engineering Analysis. Lecture 21 ENGR-1100 Introduction to Engineering Analysis Lecture 21 Lecture outline Procedure (algorithm) for finding the inverse of invertible matrix. Investigate the system of linear equation and invertibility

More information

5x 2 = 10. x 1 + 7(2) = 4. x 1 3x 2 = 4. 3x 1 + 9x 2 = 8

5x 2 = 10. x 1 + 7(2) = 4. x 1 3x 2 = 4. 3x 1 + 9x 2 = 8 1 To solve the system x 1 + x 2 = 4 2x 1 9x 2 = 2 we find an (easier to solve) equivalent system as follows: Replace equation 2 with (2 times equation 1 + equation 2): x 1 + x 2 = 4 Solve equation 2 for

More information

Math 344 Lecture # Linear Systems

Math 344 Lecture # Linear Systems Math 344 Lecture #12 2.7 Linear Systems Through a choice of bases S and T for finite dimensional vector spaces V (with dimension n) and W (with dimension m), a linear equation L(v) = w becomes the linear

More information

SOLVING Ax = b: GAUSS-JORDAN ELIMINATION [LARSON 1.2]

SOLVING Ax = b: GAUSS-JORDAN ELIMINATION [LARSON 1.2] SOLVING Ax = b: GAUSS-JORDAN ELIMINATION [LARSON.2 EQUIVALENT LINEAR SYSTEMS: Two m n linear systems are equivalent both systems have the exact same solution sets. When solving a linear system Ax = b,

More information

Exercise Sketch these lines and find their intersection.

Exercise Sketch these lines and find their intersection. These are brief notes for the lecture on Friday August 21, 2009: they are not complete, but they are a guide to what I want to say today. They are not guaranteed to be correct. 1. Solving systems of linear

More information

1111: Linear Algebra I

1111: Linear Algebra I 1111: Linear Algebra I Dr. Vladimir Dotsenko (Vlad) Lecture 7 Dr. Vladimir Dotsenko (Vlad) 1111: Linear Algebra I Lecture 7 1 / 8 Properties of the matrix product Let us show that the matrix product we

More information

Midterm 1 Review. Written by Victoria Kala SH 6432u Office Hours: R 12:30 1:30 pm Last updated 10/10/2015

Midterm 1 Review. Written by Victoria Kala SH 6432u Office Hours: R 12:30 1:30 pm Last updated 10/10/2015 Midterm 1 Review Written by Victoria Kala vtkala@math.ucsb.edu SH 6432u Office Hours: R 12:30 1:30 pm Last updated 10/10/2015 Summary This Midterm Review contains notes on sections 1.1 1.5 and 1.7 in your

More information

Lecture 6 & 7. Shuanglin Shao. September 16th and 18th, 2013

Lecture 6 & 7. Shuanglin Shao. September 16th and 18th, 2013 Lecture 6 & 7 Shuanglin Shao September 16th and 18th, 2013 1 Elementary matrices 2 Equivalence Theorem 3 A method of inverting matrices Def An n n matrice is called an elementary matrix if it can be obtained

More information

Math 3C Lecture 20. John Douglas Moore

Math 3C Lecture 20. John Douglas Moore Math 3C Lecture 20 John Douglas Moore May 18, 2009 TENTATIVE FORMULA I Midterm I: 20% Midterm II: 20% Homework: 10% Quizzes: 10% Final: 40% TENTATIVE FORMULA II Higher of two midterms: 30% Homework: 10%

More information

2. (10 pts) How many vectors are in the null space of the matrix A = 0 1 1? (i). Zero. (iv). Three. (ii). One. (v).

2. (10 pts) How many vectors are in the null space of the matrix A = 0 1 1? (i). Zero. (iv). Three. (ii). One. (v). Exam 3 MAS 3105 Applied Linear Algebra, Spring 2018 (Clearly!) Print Name: Apr 10, 2018 Read all of what follows carefully before starting! 1. This test has 7 problems and is worth 110 points. Please be

More information

REPLACE ONE ROW BY ADDING THE SCALAR MULTIPLE OF ANOTHER ROW

REPLACE ONE ROW BY ADDING THE SCALAR MULTIPLE OF ANOTHER ROW 20 CHAPTER 1 Systems of Linear Equations REPLACE ONE ROW BY ADDING THE SCALAR MULTIPLE OF ANOTHER ROW The last type of operation is slightly more complicated. Suppose that we want to write down the elementary

More information

Math 308 Midterm Answers and Comments July 18, Part A. Short answer questions

Math 308 Midterm Answers and Comments July 18, Part A. Short answer questions Math 308 Midterm Answers and Comments July 18, 2011 Part A. Short answer questions (1) Compute the determinant of the matrix a 3 3 1 1 2. 1 a 3 The determinant is 2a 2 12. Comments: Everyone seemed to

More information

Chapter 1. Vectors, Matrices, and Linear Spaces

Chapter 1. Vectors, Matrices, and Linear Spaces 1.4 Solving Systems of Linear Equations 1 Chapter 1. Vectors, Matrices, and Linear Spaces 1.4. Solving Systems of Linear Equations Note. We give an algorithm for solving a system of linear equations (called

More information

Row Space, Column Space, and Nullspace

Row Space, Column Space, and Nullspace Row Space, Column Space, and Nullspace MATH 322, Linear Algebra I J. Robert Buchanan Department of Mathematics Spring 2015 Introduction Every matrix has associated with it three vector spaces: row space

More information

MTH 362: Advanced Engineering Mathematics

MTH 362: Advanced Engineering Mathematics MTH 362: Advanced Engineering Mathematics Lecture 5 Jonathan A. Chávez Casillas 1 1 University of Rhode Island Department of Mathematics September 26, 2017 1 Linear Independence and Dependence of Vectors

More information

Inverses and Elementary Matrices

Inverses and Elementary Matrices Inverses and Elementary Matrices 1-12-2013 Matrix inversion gives a method for solving some systems of equations Suppose 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 a n1 x

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

Solution: By inspection, the standard matrix of T is: A = Where, Ae 1 = 3. , and Ae 3 = 4. , Ae 2 =

Solution: By inspection, the standard matrix of T is: A = Where, Ae 1 = 3. , and Ae 3 = 4. , Ae 2 = This is a typical assignment, but you may not be familiar with the material. You should also be aware that many schools only give two exams, but also collect homework which is usually worth a small part

More information

Topic 15 Notes Jeremy Orloff

Topic 15 Notes Jeremy Orloff Topic 5 Notes Jeremy Orloff 5 Transpose, Inverse, Determinant 5. Goals. Know the definition and be able to compute the inverse of any square matrix using row operations. 2. Know the properties of inverses.

More information

Row Reduced Echelon Form

Row Reduced Echelon Form Math 40 Row Reduced Echelon Form Solving systems of linear equations lies at the heart of linear algebra. In high school we learn to solve systems in or variables using elimination and substitution of

More information

Review Solutions for Exam 1

Review Solutions for Exam 1 Definitions Basic Theorems. Finish the definition: Review Solutions for Exam (a) A linear combination of vectors {v,..., v n } is: any vector of the form c v + c v + + c n v n (b) A set of vectors {v,...,

More information

Solving Systems of Linear Equations Using Matrices

Solving Systems of Linear Equations Using Matrices Solving Systems of Linear Equations Using Matrices What is a Matrix? A matrix is a compact grid or array of numbers. It can be created from a system of equations and used to solve the system of equations.

More information

A 2. =... = c c N. 's arise from the three types of elementary row operations. If rref A = I its determinant is 1, and A = c 1

A 2. =... = c c N. 's arise from the three types of elementary row operations. If rref A = I its determinant is 1, and A = c 1 Theorem: Let A n n Then A 1 exists if and only if det A 0 proof: We already know that A 1 exists if and only if the reduced row echelon form of A is the identity matrix Now, consider reducing A to its

More information

Matrix Inverses. November 19, 2014

Matrix Inverses. November 19, 2014 Matrix Inverses November 9, 204 22 The Inverse of a Matrix Now that we have discussed how to multiply two matrices, we can finally have a proper discussion of what we mean by the expression A for a matrix

More information

Math 1021, Linear Algebra 1. Section: A at 10am, B at 2:30pm

Math 1021, Linear Algebra 1. Section: A at 10am, B at 2:30pm Math 1021, Linear Algebra 1. Section: A at 10am, B at 2:30pm All course information is available on Moodle. Text: Nicholson, Linear algebra with applications, 7th edition. We shall cover Chapters 1,2,3,4,5:

More information

a. Do you think the function is linear or non-linear? Explain using what you know about powers of variables.

a. Do you think the function is linear or non-linear? Explain using what you know about powers of variables. 8.5.8 Lesson Date: Graphs of Non-Linear Functions Student Objectives I can examine the average rate of change for non-linear functions and learn that they do not have a constant rate of change. I can determine

More information

Matrix operations Linear Algebra with Computer Science Application

Matrix operations Linear Algebra with Computer Science Application Linear Algebra with Computer Science Application February 14, 2018 1 Matrix operations 11 Matrix operations If A is an m n matrix that is, a matrix with m rows and n columns then the scalar entry in the

More information

Review for Exam Find all a for which the following linear system has no solutions, one solution, and infinitely many solutions.

Review for Exam Find all a for which the following linear system has no solutions, one solution, and infinitely many solutions. Review for Exam. Find all a for which the following linear system has no solutions, one solution, and infinitely many solutions. x + y z = 2 x + 2y + z = 3 x + y + (a 2 5)z = a 2 The augmented matrix for

More information

Chapter 4 Systems of Linear Equations; Matrices

Chapter 4 Systems of Linear Equations; Matrices Chapter 4 Systems of Linear Equations; Matrices Section 5 Inverse of a Square Matrix Learning Objectives for Section 4.5 Inverse of a Square Matrix The student will be able to identify identity matrices

More information

Topic 14 Notes Jeremy Orloff

Topic 14 Notes Jeremy Orloff Topic 4 Notes Jeremy Orloff 4 Row reduction and subspaces 4. Goals. Be able to put a matrix into row reduced echelon form (RREF) using elementary row operations.. Know the definitions of null and column

More information

Solving Ax = b w/ different b s: LU-Factorization

Solving Ax = b w/ different b s: LU-Factorization Solving Ax = b w/ different b s: LU-Factorization Linear Algebra Josh Engwer TTU 14 September 2015 Josh Engwer (TTU) Solving Ax = b w/ different b s: LU-Factorization 14 September 2015 1 / 21 Elementary

More information

MODEL ANSWERS TO THE THIRD HOMEWORK

MODEL ANSWERS TO THE THIRD HOMEWORK MODEL ANSWERS TO THE THIRD HOMEWORK 1 (i) We apply Gaussian elimination to A First note that the second row is a multiple of the first row So we need to swap the second and third rows 1 3 2 1 2 6 5 7 3

More information

MTH6108 Coding theory Coursework 7

MTH6108 Coding theory Coursework 7 1 Let C be the linear [6, 3]-code over F 5 with generator matrix 4 0 0 2 1 2 (a) By applying the matrix operations MO1 5, put this in standard form, ie find a standard-form generator matrix for a code

More information

Chapter 2. Matrix Arithmetic. Chapter 2

Chapter 2. Matrix Arithmetic. Chapter 2 Matrix Arithmetic Matrix Addition and Subtraction Addition and subtraction act element-wise on matrices. In order for the addition/subtraction (A B) to be possible, the two matrices A and B must have the

More information

Homework sheet 4: EIGENVALUES AND EIGENVECTORS. DIAGONALIZATION (with solutions) Year ? Why or why not? 6 9

Homework sheet 4: EIGENVALUES AND EIGENVECTORS. DIAGONALIZATION (with solutions) Year ? Why or why not? 6 9 Bachelor in Statistics and Business Universidad Carlos III de Madrid Mathematical Methods II María Barbero Liñán Homework sheet 4: EIGENVALUES AND EIGENVECTORS DIAGONALIZATION (with solutions) Year - Is

More information

CHAPTER 1 Systems of Linear Equations

CHAPTER 1 Systems of Linear Equations CHAPTER Systems of Linear Equations Section. Introduction to Systems of Linear Equations. Because the equation is in the form a x a y b, it is linear in the variables x and y. 0. Because the equation cannot

More information

Methods for Solving Linear Systems Part 2

Methods for Solving Linear Systems Part 2 Methods for Solving Linear Systems Part 2 We have studied the properties of matrices and found out that there are more ways that we can solve Linear Systems. In Section 7.3, we learned that we can use

More information

Solution Set 7, Fall '12

Solution Set 7, Fall '12 Solution Set 7, 18.06 Fall '12 1. Do Problem 26 from 5.1. (It might take a while but when you see it, it's easy) Solution. Let n 3, and let A be an n n matrix whose i, j entry is i + j. To show that det

More information

MATH 260 LINEAR ALGEBRA EXAM II Fall 2013 Instructions: The use of built-in functions of your calculator, such as det( ) or RREF, is prohibited.

MATH 260 LINEAR ALGEBRA EXAM II Fall 2013 Instructions: The use of built-in functions of your calculator, such as det( ) or RREF, is prohibited. MAH 60 LINEAR ALGEBRA EXAM II Fall 0 Instructions: he use of built-in functions of your calculator, such as det( ) or RREF, is prohibited ) For the matrix find: a) M and C b) M 4 and C 4 ) Evaluate the

More information

Designing Information Devices and Systems I Fall 2016 Babak Ayazifar, Vladimir Stojanovic Midterm 1. Exam location: 145 Dwinelle, last SID# 2

Designing Information Devices and Systems I Fall 2016 Babak Ayazifar, Vladimir Stojanovic Midterm 1. Exam location: 145 Dwinelle, last SID# 2 EECS 16A Designing Information Devices and Systems I Fall 2016 Babak Ayazifar, Vladimir Stojanovic Midterm 1 Exam location: 145 Dwinelle, last SID# 2 PRINT your student ID: PRINT AND SIGN your name:, (last)

More information

Lesson 3-2: Solving Linear Systems Algebraically

Lesson 3-2: Solving Linear Systems Algebraically Yesterday we took our first look at solving a linear system. We learned that a linear system is two or more linear equations taken at the same time. Their solution is the point that all the lines have

More information

This lecture is a review for the exam. The majority of the exam is on what we ve learned about rectangular matrices.

This lecture is a review for the exam. The majority of the exam is on what we ve learned about rectangular matrices. Exam review This lecture is a review for the exam. The majority of the exam is on what we ve learned about rectangular matrices. Sample question Suppose u, v and w are non-zero vectors in R 7. They span

More information

A Quick Introduction to Row Reduction

A Quick Introduction to Row Reduction A Quick Introduction to Row Reduction Gaussian Elimination Suppose we are asked to solve the system of equations 4x + 5x 2 + 6x 3 = 7 6x + 7x 2 + 8x 3 = 9. That is, we want to find all values of x, x 2

More information

Geometry/Trig Name: Date: Lesson 1-11 Writing the Equation of a Perpendicular Bisector

Geometry/Trig Name: Date: Lesson 1-11 Writing the Equation of a Perpendicular Bisector Name: Date: Lesson 1-11 Writing the Equation of a Perpendicular Bisector Learning Goals: #14: How do I write the equation of a perpendicular bisector? Warm-up What is the equation of a line that passes

More information

Sections 6.1 and 6.2: Systems of Linear Equations

Sections 6.1 and 6.2: Systems of Linear Equations What is a linear equation? Sections 6.1 and 6.2: Systems of Linear Equations We are now going to discuss solving systems of two or more linear equations with two variables. Recall that solving an equation

More information

Section 1.5. Solution Sets of Linear Systems

Section 1.5. Solution Sets of Linear Systems Section 1.5 Solution Sets of Linear Systems Plan For Today Today we will learn to describe and draw the solution set of an arbitrary system of linear equations Ax = b, using spans. Ax = b Recall: the solution

More information

Lecture 1 Systems of Linear Equations and Matrices

Lecture 1 Systems of Linear Equations and Matrices Lecture 1 Systems of Linear Equations and Matrices Math 19620 Outline of Course Linear Equations and Matrices Linear Transformations, Inverses Bases, Linear Independence, Subspaces Abstract Vector Spaces

More information

Chapter 4. Solving Systems of Equations. Chapter 4

Chapter 4. Solving Systems of Equations. Chapter 4 Solving Systems of Equations 3 Scenarios for Solutions There are three general situations we may find ourselves in when attempting to solve systems of equations: 1 The system could have one unique solution.

More information

Math Spring 2011 Final Exam

Math Spring 2011 Final Exam Math 471 - Spring 211 Final Exam Instructions The following exam consists of three problems, each with multiple parts. There are 15 points available on the exam. The highest possible score is 125. Your

More information

18.06 Professor Johnson Quiz 1 October 3, 2007

18.06 Professor Johnson Quiz 1 October 3, 2007 18.6 Professor Johnson Quiz 1 October 3, 7 SOLUTIONS 1 3 pts.) A given circuit network directed graph) which has an m n incidence matrix A rows = edges, columns = nodes) and a conductance matrix C [diagonal

More information

Math 301 Test I. M. Randall Holmes. September 8, 2008

Math 301 Test I. M. Randall Holmes. September 8, 2008 Math 0 Test I M. Randall Holmes September 8, 008 This exam will begin at 9:40 am and end at 0:5 am. You may use your writing instrument, a calculator, and your test paper; books, notes and neighbors to

More information

Lecture 12: Solving Systems of Linear Equations by Gaussian Elimination

Lecture 12: Solving Systems of Linear Equations by Gaussian Elimination Lecture 12: Solving Systems of Linear Equations by Gaussian Elimination Winfried Just, Ohio University September 22, 2017 Review: The coefficient matrix Consider a system of m linear equations in n variables.

More information

Example: 2x y + 3z = 1 5y 6z = 0 x + 4z = 7. Definition: Elementary Row Operations. Example: Type I swap rows 1 and 3

Example: 2x y + 3z = 1 5y 6z = 0 x + 4z = 7. Definition: Elementary Row Operations. Example: Type I swap rows 1 and 3 Math 0 Row Reduced Echelon Form Techniques for solving systems of linear equations lie at the heart of linear algebra. In high school we learn to solve systems with or variables using elimination and substitution

More information

MATH 2331 Linear Algebra. Section 1.1 Systems of Linear Equations. Finding the solution to a set of two equations in two variables: Example 1: Solve:

MATH 2331 Linear Algebra. Section 1.1 Systems of Linear Equations. Finding the solution to a set of two equations in two variables: Example 1: Solve: MATH 2331 Linear Algebra Section 1.1 Systems of Linear Equations Finding the solution to a set of two equations in two variables: Example 1: Solve: x x = 3 1 2 2x + 4x = 12 1 2 Geometric meaning: Do these

More information

Math 320, spring 2011 before the first midterm

Math 320, spring 2011 before the first midterm Math 320, spring 2011 before the first midterm Typical Exam Problems 1 Consider the linear system of equations 2x 1 + 3x 2 2x 3 + x 4 = y 1 x 1 + 3x 2 2x 3 + 2x 4 = y 2 x 1 + 2x 3 x 4 = y 3 where x 1,,

More information

MATH 300, Second Exam REVIEW SOLUTIONS. NOTE: You may use a calculator for this exam- You only need something that will perform basic arithmetic.

MATH 300, Second Exam REVIEW SOLUTIONS. NOTE: You may use a calculator for this exam- You only need something that will perform basic arithmetic. MATH 300, Second Exam REVIEW SOLUTIONS NOTE: You may use a calculator for this exam- You only need something that will perform basic arithmetic. [ ] [ ] 2 2. Let u = and v =, Let S be the parallelegram

More information

EE5120 Linear Algebra: Tutorial 1, July-Dec Solve the following sets of linear equations using Gaussian elimination (a)

EE5120 Linear Algebra: Tutorial 1, July-Dec Solve the following sets of linear equations using Gaussian elimination (a) EE5120 Linear Algebra: Tutorial 1, July-Dec 2017-18 1. Solve the following sets of linear equations using Gaussian elimination (a) 2x 1 2x 2 3x 3 = 2 3x 1 3x 2 2x 3 + 5x 4 = 7 x 1 x 2 2x 3 x 4 = 3 (b)

More information

Evaluating Determinants by Row Reduction

Evaluating Determinants by Row Reduction Evaluating Determinants by Row Reduction MATH 322, Linear Algebra I J. Robert Buchanan Department of Mathematics Spring 2015 Objectives Reduce a matrix to row echelon form and evaluate its determinant.

More information

Matrices and Vectors

Matrices and Vectors Matrices and Vectors James K. Peterson Department of Biological Sciences and Department of Mathematical Sciences Clemson University November 11, 2013 Outline 1 Matrices and Vectors 2 Vector Details 3 Matrix

More information

22A-2 SUMMER 2014 LECTURE 5

22A-2 SUMMER 2014 LECTURE 5 A- SUMMER 0 LECTURE 5 NATHANIEL GALLUP Agenda Elimination to the identity matrix Inverse matrices LU factorization Elimination to the identity matrix Previously, we have used elimination to get a system

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

Exam 2 MAS 3105 Applied Linear Algebra, Spring 2018

Exam 2 MAS 3105 Applied Linear Algebra, Spring 2018 Exam 2 MAS 3105 Applied Linear Algebra, Spring 2018 (Clearly!) Print Name: Mar 8, 2018 Read all of what follows carefully before starting! 1. This test has 6 problems and is worth 100 points. Please be

More information

Linear Equations in Linear Algebra

Linear Equations in Linear Algebra 1 Linear Equations in Linear Algebra 1.1 SYSTEMS OF LINEAR EQUATIONS LINEAR EQUATION x 1,, x n A linear equation in the variables equation that can be written in the form a 1 x 1 + a 2 x 2 + + a n x n

More information

CH 73 THE QUADRATIC FORMULA, PART II

CH 73 THE QUADRATIC FORMULA, PART II 1 CH THE QUADRATIC FORMULA, PART II INTRODUCTION W ay back in Chapter 55 we used the Quadratic Formula to solve quadratic equations like 6x + 1x + 0 0, whose solutions are 5 and 8. In fact, all of the

More information

3.4 Elementary Matrices and Matrix Inverse

3.4 Elementary Matrices and Matrix Inverse Math 220: Summer 2015 3.4 Elementary Matrices and Matrix Inverse A n n elementary matrix is a matrix which is obtained from the n n identity matrix I n n by a single elementary row operation. Elementary

More information

30.3. LU Decomposition. Introduction. Prerequisites. Learning Outcomes

30.3. LU Decomposition. Introduction. Prerequisites. Learning Outcomes LU Decomposition 30.3 Introduction In this Section we consider another direct method for obtaining the solution of systems of equations in the form AX B. Prerequisites Before starting this Section you

More information

Linear Systems. Math A Bianca Santoro. September 23, 2016

Linear Systems. Math A Bianca Santoro. September 23, 2016 Linear Systems Math A4600 - Bianca Santoro September 3, 06 Goal: Understand how to solve Ax = b. Toy Model: Let s study the following system There are two nice ways of thinking about this system: x + y

More information

Name: Section Registered In:

Name: Section Registered In: Name: Section Registered In: Math 125 Exam 1 Version 1 February 21, 2006 60 points possible 1. (a) (3pts) Define what it means for a linear system to be inconsistent. Solution: A linear system is inconsistent

More information

Designing Information Devices and Systems I Discussion 4B

Designing Information Devices and Systems I Discussion 4B 0.5 0.4 0.4 0.4 Last Updated: 2018-09-19 23:58 1 EECS 16A Fall 2018 Designing Information Devices and Systems I Discussion 4B Reference Definitions: Orthogonality n n matrix A: We ve seen that the following

More information

Final exam (practice) UCLA: Math 31B, Spring 2017

Final exam (practice) UCLA: Math 31B, Spring 2017 Instructor: Noah White Date: Final exam (practice) UCLA: Math 3B, Spring 207 This exam has 8 questions, for a total of 80 points. Please print your working and answers neatly. Write your solutions in the

More information

MA 1B PRACTICAL - HOMEWORK SET 3 SOLUTIONS. Solution. (d) We have matrix form Ax = b and vector equation 4

MA 1B PRACTICAL - HOMEWORK SET 3 SOLUTIONS. Solution. (d) We have matrix form Ax = b and vector equation 4 MA B PRACTICAL - HOMEWORK SET SOLUTIONS (Reading) ( pts)[ch, Problem (d), (e)] Solution (d) We have matrix form Ax = b and vector equation 4 i= x iv i = b, where v i is the ith column of A, and 4 A = 8

More information

Lecture 2 Systems of Linear Equations and Matrices, Continued

Lecture 2 Systems of Linear Equations and Matrices, Continued Lecture 2 Systems of Linear Equations and Matrices, Continued Math 19620 Outline of Lecture Algorithm for putting a matrix in row reduced echelon form - i.e. Gauss-Jordan Elimination Number of Solutions

More information

Recitation 5: Elementary Matrices

Recitation 5: Elementary Matrices Math 1b TA: Padraic Bartlett Recitation 5: Elementary Matrices Week 5 Caltech 2011 1 Random Question Consider the following two-player game, called Angels and Devils: Our game is played on a n n chessboard,

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

Example: 2x y + 3z = 1 5y 6z = 0 x + 4z = 7. Definition: Elementary Row Operations. Example: Type I swap rows 1 and 3

Example: 2x y + 3z = 1 5y 6z = 0 x + 4z = 7. Definition: Elementary Row Operations. Example: Type I swap rows 1 and 3 Linear Algebra Row Reduced Echelon Form Techniques for solving systems of linear equations lie at the heart of linear algebra. In high school we learn to solve systems with or variables using elimination

More information

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

MATH 323 Linear Algebra Lecture 6: Matrix algebra (continued). Determinants. MATH 323 Linear Algebra Lecture 6: Matrix algebra (continued). Determinants. Elementary matrices Theorem 1 Any elementary row operation σ on matrices with n rows can be simulated as left multiplication

More information

Math 2331 Linear Algebra

Math 2331 Linear Algebra 2.2 The Inverse of a Matrix Math 2331 Linear Algebra 2.2 The Inverse of a Matrix Shang-Huan Chiu Department of Mathematics, University of Houston schiu@math.uh.edu math.uh.edu/ schiu/ Shang-Huan Chiu,

More information

Math 154 :: Elementary Algebra

Math 154 :: Elementary Algebra Math 4 :: Elementary Algebra Section. Additive Property of Equality Section. Multiplicative Property of Equality Section.3 Linear Equations in One-Variable Section.4 Linear Equations in One-Variable with

More information

MATH 260 LINEAR ALGEBRA EXAM III Fall 2014

MATH 260 LINEAR ALGEBRA EXAM III Fall 2014 MAH 60 LINEAR ALGEBRA EXAM III Fall 0 Instructions: the use of built-in functions of your calculator such as det( ) or RREF is permitted ) Consider the table and the vectors and matrices given below Fill

More information

Algebra 1B notes and problems March 12, 2009 Factoring page 1

Algebra 1B notes and problems March 12, 2009 Factoring page 1 March 12, 2009 Factoring page 1 Factoring Last class, you worked on a set of problems where you had to do multiplication table calculations in reverse. For example, given the answer x 2 + 4x + 2x + 8,

More information

Orthogonality. Orthonormal Bases, Orthogonal Matrices. Orthogonality

Orthogonality. Orthonormal Bases, Orthogonal Matrices. Orthogonality Orthonormal Bases, Orthogonal Matrices The Major Ideas from Last Lecture Vector Span Subspace Basis Vectors Coordinates in different bases Matrix Factorization (Basics) The Major Ideas from Last Lecture

More information

MTHSC 3110 Section 1.1

MTHSC 3110 Section 1.1 MTHSC 3110 Section 1.1 Kevin James A system of linear equations is a collection of equations in the same set of variables. For example, { x 1 + 3x 2 = 5 2x 1 x 2 = 4 Of course, since this is a pair of

More information

Math 33A Discussion Notes

Math 33A Discussion Notes Math 33A Discussion Notes Jean-Michel Maldague October 21, 2017 Week 3 Incomplete! Will update soon. - A function T : R k R n is called injective, or one-to-one, if each input gets a unique output. The

More information