Eigenvector Review. Notes. Notes. Notes. Problems pulled from archived UBC final exams
|
|
- Rudolph Ellis
- 6 years ago
- Views:
Transcription
1 Eigenvector Review Problems pulled from archived UBC final exams Note: many eigenvector problems involve differential equations (our next topic) If you re not aware, some solutions are on the wiki, Math_Exam_Resources/Courses/MATH5 Find an eigenvector corresponding to eigenvalue for the matrix below: [ 5 ] 4 Find an eigenvector of eigenvalue for the matrix [ ] 4 The matrix A = has eigenvalue. Find all eigenvectors corresponding to this eigenvalue. The matrix A = 5 5 has eigenvalue. 5 8 Find all eigenvectors corresponding to this eigenvalue. Standard question type: given eigenvalue, find associated eigenvector. Nonstandard wording: find ALL eigenvectors (instead of one representative of the bunch) Strategy: use the definition of an eigenvector (or the shortcut: set up the homogeneous system of equations with matrix of coefficients A λi.)
2 Ax = λx Find x for given A and λ = x x 5 5 y = y 5 8 z z x + y z x 5x y 5z = y make the right side constants 5x + y 8z z x + y z 5x + y 5z = 5x + y 5z 5 5 row-reduce row-reduce Parametrized solutions: let y = r and z = s x y = r + s z These vectors are the eigenvectors associated with eigenvalue. Let A be a matrix with real entries. Suppose that [ ] i is an eigenvector of A with eigenvalue + i. What is another eigenvalue of A and its associated eigenvector? Consider Find y and z so that eigenvalue. [ ] y A = z [ ] is an eigenvector of A with
3 [ ] [ y Consider A =. Find y and z so that is an eigenvector of A z ] with eigenvalue. Question type: solving for parameters. Strategy: (one there are others) Use the definition of an eigenvalue-eigenvector pair to find y and z. If [ ] is an eigenvector associated with eigenvalue : [ y z ] [ ] = [ y = ] [ + y + z = ] = [ ] z = = 4 Let A be a matrix which [ ] represents a reflection across a line in R. Suppose that is an eigenvector with eigenvalue [ ] α and is an eigenvector with eigenvalue β. What are the values of α and β? Let A be a [ ] matrix which represents a reflection across [ a] line in R. α Suppose that is an eigenvector with eigenvalue and is an eigenvector with eigenvalue β. What are the values of α and β? Strategy: interpret geometrically. Sometimes a picture helps. If a vector corresponds to eigenvalue, that means it s not changed by multiplication with the matrix. For a reflection, that means it s on the like of reflection. So, our line of reflection is in the direction [, ]. Another line whose reflection is parallel to itself is a line perpendicular to this. Its reflection is its negative, so β =. Also, [α, ] [, ] =, so α = 9.
4 The matrix below represents rotation in D about a line through the origin. / / / / / / / / Find a vector in the direction of the line of rotation. The matrix / / / / / / / / represents a rotation in D relative to some axis. marks Find all the eigenvalues of A. Find the direction vector of the axis of rotation. Hint: this vector remains unchanged after the rotation. Find the angle of rotation around the axis in the previous part. Hint: rotate a vector that is perpendicular to the rotation axis. Consider the probability transition matrix / / / / and initial probability x = / / / 4/ What is lim n P n x? Hint: this can be done without extensive calculations. Our theorem about the equilibrium probability being an eigenvector associated to eigenvalue DOES NOT APPLY because our matrix has zeroes in it. Let s think about the system using a sketch P = / / / / x = / / / 4/ lim x n = n [ ] / 4/ / State State / / State State 4 4/
5 Let... P = be the transition matrix of a random walk. It is known that P has three distinct eigenvalues. Two eigenvalues are λ = and λ =. with corresponding eigenvectors v =, and v = marks Find the third eigenvalue λ and its associated eigenvector v. If the initial state is x = find x = P x. Hint: x is a linear combination of v and v. What is lim n x n? P = [ ] λ = (theorem); find associated eigenvector in the usual way The last question is about equilibrium probability; don t need second question to answer Second question: use result from first question Suppose in the year, 5 million people live in cities and 5 million in the suburbs. Every year, % of city residents move to teh suburbs and % of the residents of the suburbs move to cities. mark Write down the probability transition matrix P for this problem, using the ordering () city and () suburbs. What fraction of residents will be living in cities in? Find the eigenvalues of P and a basis of eigenvectors. Assuming the overall population does not change (i.e. remains at million), how many people will be living in the suburbs far in the future?
6 The percentage of people with the disease, March Madness, is recorded every week. Note that is it possible to recover from March Madness one week and catch it again the following week. Records indicate that the disease can be modelled by a random walk and that if 5% of the population is infected with March Madness one week, then % of the population will be infected the next week. Records also indicate that if % of the population is infected one week, then 9% of the population will be infected the next week. It is known that % of the population has March Madness this week. marks What is the probability transition matrix for this system? What percentage of the population will have March Madness two weeks from now? What percentage of the population had March Madness last week? Approximately what will be the percentage of people with March Madness many weeks from now? Consider the matrix A =. 4 5 mark Find an eigenvector of A corresponding to the eigenvalue λ = Find all other eigenvalues of A. Find a basis of eigenvectors of A. Let A be a matrix in the form [ ] 4 a mark Find a such that A is not invertible For the value of a above, find the eigenvalues of A. Find the eigenvectors associated with the eigenvalues found above. Do the eigenvectors found above form a basis of R? Justify briefly. Let A be the matrix 4 marks Find an eigenvector of A corresponding to eigenvalue λ =. Find all the other eigenvalues of A. How many linearly independent eigenvectors does A have? Justify briefly.
7 4 Let A be the matrix marks Find an eigenvector of A corresponding to eigenvalue λ =. Find all the other eigenvalues of A. λ = and λ = 5 How many linearly independent eigenvectors does A have? Justify briefly. Option : find eigenvectors for the remaining two eigenvalues, show they are linearly independent. 4 are all eigenvectors of A, and they are linearly independent. Since there can t be any MORE than three linearly independent vectors in R, we see A has three linearly independent eigenvectors. 4 Let A be the matrix marks Find an eigenvector of A corresponding to eigenvalue λ =. Find all the other eigenvalues of A. λ = and λ = 5 How many linearly independent eigenvectors does A have? Justify briefly. Option : show that the three eigenvectors are linearly independent WITHOUT actually finding them.it s a fact that distinct eigenvalues have linearly independent eigenvectors, but I don t think it s proved in your book. We can prove it on our own in this simple case. ANY collection of linearly independent vectors in R has size AT MOST three. So, there are at most three linearly independent eigenvectors. For every eigenvalue of A, there exists a (nonzero) eigenvector. Say these eigenvectors are x, x, and x. If these eigenvectors are linearly independent, then we have exactly three linearly independent eigenvectors. If they are not linearly independent, we have at most two. We want to show that eigenvectors x, x, x are linearly independent. Suppose they aren t (we ll show this leads to trouble). Then we can write one as a linear combination of the others, say x = c x + c x. Since x is an eigenvector, it s not allowed to be a zero vector, so it s not the case that c and c are both zero. So, let s say c. Now we ll use the definition of eigenvector twice. Ax = λ x = λ (c x + c x ) Ax = A(c x + c x ) = Ac x + Ac x = λ c x + λ c x So, λ (c x + c x ) = λ c x + λ c x = (λ λ )c x + (λ λ )c x Since λ λ and c : x = (λ λ )c (λ λ )c x But if x and x are parallel, they correspond to the same eigenvalue, which contradicts the way we chose them.
TMA Calculus 3. Lecture 21, April 3. Toke Meier Carlsen Norwegian University of Science and Technology Spring 2013
TMA4115 - Calculus 3 Lecture 21, April 3 Toke Meier Carlsen Norwegian University of Science and Technology Spring 2013 www.ntnu.no TMA4115 - Calculus 3, Lecture 21 Review of last week s lecture Last week
More informationProperties of Linear Transformations from R n to R m
Properties of Linear Transformations from R n to R m MATH 322, Linear Algebra I J. Robert Buchanan Department of Mathematics Spring 2015 Topic Overview Relationship between the properties of a matrix transformation
More informationChapter 5. Eigenvalues and Eigenvectors
Chapter 5 Eigenvalues and Eigenvectors Section 5. Eigenvectors and Eigenvalues Motivation: Difference equations A Biology Question How to predict a population of rabbits with given dynamics:. half of the
More informationMAT 1302B Mathematical Methods II
MAT 1302B Mathematical Methods II Alistair Savage Mathematics and Statistics University of Ottawa Winter 2015 Lecture 19 Alistair Savage (uottawa) MAT 1302B Mathematical Methods II Winter 2015 Lecture
More information[Disclaimer: This is not a complete list of everything you need to know, just some of the topics that gave people difficulty.]
Math 43 Review Notes [Disclaimer: This is not a complete list of everything you need to know, just some of the topics that gave people difficulty Dot Product If v (v, v, v 3 and w (w, w, w 3, then the
More informationLecture 15, 16: Diagonalization
Lecture 15, 16: Diagonalization Motivation: Eigenvalues and Eigenvectors are easy to compute for diagonal matrices. Hence, we would like (if possible) to convert matrix A into a diagonal matrix. Suppose
More informationMATH 310, REVIEW SHEET 2
MATH 310, REVIEW SHEET 2 These notes are a very short summary of the key topics in the book (and follow the book pretty closely). You should be familiar with everything on here, but it s not comprehensive,
More informationCity Suburbs. : population distribution after m years
Section 5.3 Diagonalization of Matrices Definition Example: stochastic matrix To City Suburbs From City Suburbs.85.03 = A.15.97 City.15.85 Suburbs.97.03 probability matrix of a sample person s residence
More informationPractice problems for Exam 3 A =
Practice problems for Exam 3. Let A = 2 (a) Determine whether A is diagonalizable. If so, find a matrix S such that S AS is diagonal. If not, explain why not. (b) What are the eigenvalues of A? Is A diagonalizable?
More informationM340L Final Exam Solutions May 13, 1995
M340L Final Exam Solutions May 13, 1995 Name: Problem 1: Find all solutions (if any) to the system of equations. Express your answer in vector parametric form. The matrix in other words, x 1 + 2x 3 + 3x
More informationQ1 Q2 Q3 Q4 Tot Letr Xtra
Mathematics 54.1 Final Exam, 12 May 2011 180 minutes, 90 points NAME: ID: GSI: INSTRUCTIONS: You must justify your answers, except when told otherwise. All the work for a question should be on the respective
More informationRemark By definition, an eigenvector must be a nonzero vector, but eigenvalue could be zero.
Sec 6 Eigenvalues and Eigenvectors Definition An eigenvector of an n n matrix A is a nonzero vector x such that A x λ x for some scalar λ A scalar λ is called an eigenvalue of A if there is a nontrivial
More informationLinear Independence x
Linear Independence A consistent system of linear equations with matrix equation Ax = b, where A is an m n matrix, has a solution set whose graph in R n is a linear object, that is, has one of only n +
More informationEcon Slides from Lecture 7
Econ 205 Sobel Econ 205 - Slides from Lecture 7 Joel Sobel August 31, 2010 Linear Algebra: Main Theory A linear combination of a collection of vectors {x 1,..., x k } is a vector of the form k λ ix i for
More information235 Final exam review questions
5 Final exam review questions Paul Hacking December 4, 0 () Let A be an n n matrix and T : R n R n, T (x) = Ax the linear transformation with matrix A. What does it mean to say that a vector v R n is an
More informationMath 308 Practice Final Exam Page and vector y =
Math 308 Practice Final Exam Page Problem : Solving a linear equation 2 0 2 5 Given matrix A = 3 7 0 0 and vector y = 8. 4 0 0 9 (a) Solve Ax = y (if the equation is consistent) and write the general solution
More informationChapters 5 & 6: Theory Review: Solutions Math 308 F Spring 2015
Chapters 5 & 6: Theory Review: Solutions Math 308 F Spring 205. If A is a 3 3 triangular matrix, explain why det(a) is equal to the product of entries on the diagonal. If A is a lower triangular or diagonal
More informationDiagonalization of Matrix
of Matrix King Saud University August 29, 2018 of Matrix Table of contents 1 2 of Matrix Definition If A M n (R) and λ R. We say that λ is an eigenvalue of the matrix A if there is X R n \ {0} such that
More informationEigenvalues and Eigenvectors
Eigenvalues and Eigenvectors week -2 Fall 26 Eigenvalues and eigenvectors The most simple linear transformation from R n to R n may be the transformation of the form: T (x,,, x n ) (λ x, λ 2,, λ n x n
More informationTest 3, Linear Algebra
Test 3, Linear Algebra Dr. Adam Graham-Squire, Fall 2017 Name: I pledge that I have neither given nor received any unauthorized assistance on this exam. (signature) DIRECTIONS 1. Don t panic. 2. Show all
More informationLINEAR ALGEBRA BOOT CAMP WEEK 2: LINEAR OPERATORS
LINEAR ALGEBRA BOOT CAMP WEEK 2: LINEAR OPERATORS Unless otherwise stated, all vector spaces in this worksheet are finite dimensional and the scalar field F has characteristic zero. The following are facts
More informationLINEAR ALGEBRA 1, 2012-I PARTIAL EXAM 3 SOLUTIONS TO PRACTICE PROBLEMS
LINEAR ALGEBRA, -I PARTIAL EXAM SOLUTIONS TO PRACTICE PROBLEMS Problem (a) For each of the two matrices below, (i) determine whether it is diagonalizable, (ii) determine whether it is orthogonally diagonalizable,
More informationMath 520 Exam 2 Topic Outline Sections 1 3 (Xiao/Dumas/Liaw) Spring 2008
Math 520 Exam 2 Topic Outline Sections 1 3 (Xiao/Dumas/Liaw) Spring 2008 Exam 2 will be held on Tuesday, April 8, 7-8pm in 117 MacMillan What will be covered The exam will cover material from the lectures
More informationWeek #4: Midterm 1 Review
Week #4: Midterm Review April 5, NAMES: TARDIS : http://math.ucsb.edu/ kgracekennedy/spring 4A.html Week : Introduction to Systems of Linear Equations Problem.. What row operations are allowed and why?...
More informationWarm-up. True or false? Baby proof. 2. The system of normal equations for A x = y has solutions iff A x = y has solutions
Warm-up True or false? 1. proj u proj v u = u 2. The system of normal equations for A x = y has solutions iff A x = y has solutions 3. The normal equations are always consistent Baby proof 1. Let A be
More informationMath 205, Summer I, Week 4b:
Math 205, Summer I, 2016 Week 4b: Chapter 5, Sections 6, 7 and 8 (5.5 is NOT on the syllabus) 5.6 Eigenvalues and Eigenvectors 5.7 Eigenspaces, nondefective matrices 5.8 Diagonalization [*** See next slide
More informationRemark 1 By definition, an eigenvector must be a nonzero vector, but eigenvalue could be zero.
Sec 5 Eigenvectors and Eigenvalues In this chapter, vector means column vector Definition An eigenvector of an n n matrix A is a nonzero vector x such that A x λ x for some scalar λ A scalar λ is called
More informationMATH 1553 PRACTICE FINAL EXAMINATION
MATH 553 PRACTICE FINAL EXAMINATION Name Section 2 3 4 5 6 7 8 9 0 Total Please read all instructions carefully before beginning. The final exam is cumulative, covering all sections and topics on the master
More informationRecall : Eigenvalues and Eigenvectors
Recall : Eigenvalues and Eigenvectors Let A be an n n matrix. If a nonzero vector x in R n satisfies Ax λx for a scalar λ, then : The scalar λ is called an eigenvalue of A. The vector x is called an eigenvector
More informationMATH 31 - ADDITIONAL PRACTICE PROBLEMS FOR FINAL
MATH 3 - ADDITIONAL PRACTICE PROBLEMS FOR FINAL MAIN TOPICS FOR THE FINAL EXAM:. Vectors. Dot product. Cross product. Geometric applications. 2. Row reduction. Null space, column space, row space, left
More informationMA 242 LINEAR ALGEBRA C1, Solutions to First Midterm Exam
MA 242 LINEAR ALGEBRA C Solutions to First Midterm Exam Prof Nikola Popovic October 2 9:am - :am Problem ( points) Determine h and k such that the solution set of x + = k 4x + h = 8 (a) is empty (b) contains
More informationStudy Guide for Linear Algebra Exam 2
Study Guide for Linear Algebra Exam 2 Term Vector Space Definition A Vector Space is a nonempty set V of objects, on which are defined two operations, called addition and multiplication by scalars (real
More informationProblem # Max points possible Actual score Total 120
FINAL EXAMINATION - MATH 2121, FALL 2017. Name: ID#: Email: Lecture & Tutorial: Problem # Max points possible Actual score 1 15 2 15 3 10 4 15 5 15 6 15 7 10 8 10 9 15 Total 120 You have 180 minutes to
More informationand let s calculate the image of some vectors under the transformation T.
Chapter 5 Eigenvalues and Eigenvectors 5. Eigenvalues and Eigenvectors Let T : R n R n be a linear transformation. Then T can be represented by a matrix (the standard matrix), and we can write T ( v) =
More informationYORK UNIVERSITY. Faculty of Science Department of Mathematics and Statistics MATH M Test #1. July 11, 2013 Solutions
YORK UNIVERSITY Faculty of Science Department of Mathematics and Statistics MATH 222 3. M Test # July, 23 Solutions. For each statement indicate whether it is always TRUE or sometimes FALSE. Note: For
More informationExamples True or false: 3. Let A be a 3 3 matrix. Then there is a pattern in A with precisely 4 inversions.
The exam will cover Sections 6.-6.2 and 7.-7.4: True/False 30% Definitions 0% Computational 60% Skip Minors and Laplace Expansion in Section 6.2 and p. 304 (trajectories and phase portraits) in Section
More informationMATH 315 Linear Algebra Homework #1 Assigned: August 20, 2018
Homework #1 Assigned: August 20, 2018 Review the following subjects involving systems of equations and matrices from Calculus II. Linear systems of equations Converting systems to matrix form Pivot entry
More information5.) For each of the given sets of vectors, determine whether or not the set spans R 3. Give reasons for your answers.
Linear Algebra - Test File - Spring Test # For problems - consider the following system of equations. x + y - z = x + y + 4z = x + y + 6z =.) Solve the system without using your calculator..) Find the
More informationPROBLEM SET. Problems on Eigenvalues and Diagonalization. Math 3351, Fall Oct. 20, 2010 ANSWERS
PROBLEM SET Problems on Eigenvalues and Diagonalization Math 335, Fall 2 Oct. 2, 2 ANSWERS i Problem. In each part, find the characteristic polynomial of the matrix and the eigenvalues of the matrix by
More information4. Linear transformations as a vector space 17
4 Linear transformations as a vector space 17 d) 1 2 0 0 1 2 0 0 1 0 0 0 1 2 3 4 32 Let a linear transformation in R 2 be the reflection in the line = x 2 Find its matrix 33 For each linear transformation
More informationLinear Algebra Practice Problems
Math 7, Professor Ramras Linear Algebra Practice Problems () Consider the following system of linear equations in the variables x, y, and z, in which the constants a and b are real numbers. x y + z = a
More informationElementary Linear Algebra Review for Exam 3 Exam is Friday, December 11th from 1:15-3:15
Elementary Linear Algebra Review for Exam 3 Exam is Friday, December th from :5-3:5 The exam will cover sections: 6., 6.2, 7. 7.4, and the class notes on dynamical systems. You absolutely must be able
More informationMidterm 2 Solutions, MATH 54, Linear Algebra and Differential Equations, Fall 2014
Name (Last, First): Student ID: Circle your section: 2 Shin 8am 7 Evans 22 Lim pm 35 Etcheverry 22 Cho 8am 75 Evans 23 Tanzer 2pm 35 Evans 23 Shin 9am 5 Latimer 24 Moody 2pm 8 Evans 24 Cho 9am 254 Sutardja
More informationMath 322. Spring 2015 Review Problems for Midterm 2
Linear Algebra: Topic: Linear Independence of vectors. Question. Math 3. Spring Review Problems for Midterm Explain why if A is not square, then either the row vectors or the column vectors of A are linearly
More information18.06 Quiz 2 April 7, 2010 Professor Strang
18.06 Quiz 2 April 7, 2010 Professor Strang Your PRINTED name is: 1. Your recitation number or instructor is 2. 3. 1. (33 points) (a) Find the matrix P that projects every vector b in R 3 onto the line
More informationSolutions to Review Problems for Chapter 6 ( ), 7.1
Solutions to Review Problems for Chapter (-, 7 The Final Exam is on Thursday, June,, : AM : AM at NESBITT Final Exam Breakdown Sections % -,7-9,- - % -9,-,7,-,-7 - % -, 7 - % Let u u and v Let x x x x,
More informationAnnouncements Monday, October 29
Announcements Monday, October 29 WeBWorK on determinents due on Wednesday at :59pm. The quiz on Friday covers 5., 5.2, 5.3. My office is Skiles 244 and Rabinoffice hours are: Mondays, 2 pm; Wednesdays,
More informationMath 5BI: Problem Set 6 Gradient dynamical systems
Math 5BI: Problem Set 6 Gradient dynamical systems April 25, 2007 Recall that if f(x) = f(x 1, x 2,..., x n ) is a smooth function of n variables, the gradient of f is the vector field f(x) = ( f)(x 1,
More informationPENN STATE UNIVERSITY MATH 220: LINEAR ALGEBRA
PENN STATE UNIVERSITY MATH 220: LINEAR ALGEBRA Penn State Bluebook: 1. Systems of Linear Equations 2. Matrix Algebra 3. Eigenvalues and Eigenvectors 4. Linear Systems of Differential Equations The above
More informationMATH 223 FINAL EXAM APRIL, 2005
MATH 223 FINAL EXAM APRIL, 2005 Instructions: (a) There are 10 problems in this exam. Each problem is worth five points, divided equally among parts. (b) Full credit is given to complete work only. Simply
More informationEK102 Linear Algebra PRACTICE PROBLEMS for Final Exam Spring 2016
EK102 Linear Algebra PRACTICE PROBLEMS for Final Exam Spring 2016 Answer the questions in the spaces provided on the question sheets. You must show your work to get credit for your answers. There will
More informationMAT1302F Mathematical Methods II Lecture 19
MAT302F Mathematical Methods II Lecture 9 Aaron Christie 2 April 205 Eigenvectors, Eigenvalues, and Diagonalization Now that the basic theory of eigenvalues and eigenvectors is in place most importantly
More informationMath 215 HW #9 Solutions
Math 5 HW #9 Solutions. Problem 4.4.. If A is a 5 by 5 matrix with all a ij, then det A. Volumes or the big formula or pivots should give some upper bound on the determinant. Answer: Let v i be the ith
More informationMATH 3321 Sample Questions for Exam 3. 3y y, C = Perform the indicated operations, if possible: (a) AC (b) AB (c) B + AC (d) CBA
MATH 33 Sample Questions for Exam 3. Find x and y so that x 4 3 5x 3y + y = 5 5. x = 3/7, y = 49/7. Let A = 3 4, B = 3 5, C = 3 Perform the indicated operations, if possible: a AC b AB c B + AC d CBA AB
More informationMath 1553, Introduction to Linear Algebra
Learning goals articulate what students are expected to be able to do in a course that can be measured. This course has course-level learning goals that pertain to the entire course, and section-level
More informationAnnouncements Wednesday, November 01
Announcements Wednesday, November 01 WeBWorK 3.1, 3.2 are due today at 11:59pm. The quiz on Friday covers 3.1, 3.2. My office is Skiles 244. Rabinoffice hours are Monday, 1 3pm and Tuesday, 9 11am. Section
More informationMATH 1120 (LINEAR ALGEBRA 1), FINAL EXAM FALL 2011 SOLUTIONS TO PRACTICE VERSION
MATH (LINEAR ALGEBRA ) FINAL EXAM FALL SOLUTIONS TO PRACTICE VERSION Problem (a) For each matrix below (i) find a basis for its column space (ii) find a basis for its row space (iii) determine whether
More informationEXAM. Exam #3. Math 2360, Spring April 24, 2001 ANSWERS
EXAM Exam #3 Math 2360, Spring 200 April 24, 200 ANSWERS i 40 pts Problem In this problem, we will work in the vectorspace P 3 = { ax 2 + bx + c a, b, c R }, the space of polynomials of degree less than
More informationProblem 1: Solving a linear equation
Math 38 Practice Final Exam ANSWERS Page Problem : Solving a linear equation Given matrix A = 2 2 3 7 4 and vector y = 5 8 9. (a) Solve Ax = y (if the equation is consistent) and write the general solution
More informationCS 246 Review of Linear Algebra 01/17/19
1 Linear algebra In this section we will discuss vectors and matrices. We denote the (i, j)th entry of a matrix A as A ij, and the ith entry of a vector as v i. 1.1 Vectors and vector operations A vector
More informationMATH 1553-C MIDTERM EXAMINATION 3
MATH 553-C MIDTERM EXAMINATION 3 Name GT Email @gatech.edu Please read all instructions carefully before beginning. Please leave your GT ID card on your desk until your TA scans your exam. Each problem
More informationMath 1553 Worksheet 5.3, 5.5
Math Worksheet, Answer yes / no / maybe In each case, A is a matrix whose entries are real a) If A is a matrix with characteristic polynomial λ(λ ), then the - eigenspace is -dimensional b) If A is an
More informationMath 205, Summer I, Week 4b: Continued. Chapter 5, Section 8
Math 205, Summer I, 2016 Week 4b: Continued Chapter 5, Section 8 2 5.8 Diagonalization [reprint, week04: Eigenvalues and Eigenvectors] + diagonaliization 1. 5.8 Eigenspaces, Diagonalization A vector v
More informationLecture 3 Eigenvalues and Eigenvectors
Lecture 3 Eigenvalues and Eigenvectors Eivind Eriksen BI Norwegian School of Management Department of Economics September 10, 2010 Eivind Eriksen (BI Dept of Economics) Lecture 3 Eigenvalues and Eigenvectors
More informationMath 20F Practice Final Solutions. Jor-el Briones
Math 2F Practice Final Solutions Jor-el Briones Jor-el Briones / Math 2F Practice Problems for Final Page 2 of 6 NOTE: For the solutions to these problems, I skip all the row reduction calculations. Please
More informationMath 308 Practice Test for Final Exam Winter 2015
Math 38 Practice Test for Final Exam Winter 25 No books are allowed during the exam. But you are allowed one sheet ( x 8) of handwritten notes (back and front). You may use a calculator. For TRUE/FALSE
More informationLecture 11: Eigenvalues and Eigenvectors
Lecture : Eigenvalues and Eigenvectors De nition.. Let A be a square matrix (or linear transformation). A number λ is called an eigenvalue of A if there exists a non-zero vector u such that A u λ u. ()
More informationLinear vector spaces and subspaces.
Math 2051 W2008 Margo Kondratieva Week 1 Linear vector spaces and subspaces. Section 1.1 The notion of a linear vector space. For the purpose of these notes we regard (m 1)-matrices as m-dimensional vectors,
More informationLinear Algebra: Sample Questions for Exam 2
Linear Algebra: Sample Questions for Exam 2 Instructions: This is not a comprehensive review: there are concepts you need to know that are not included. Be sure you study all the sections of the book and
More informationMath 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 informationLinear Algebra: Matrix Eigenvalue Problems
CHAPTER8 Linear Algebra: Matrix Eigenvalue Problems Chapter 8 p1 A matrix eigenvalue problem considers the vector equation (1) Ax = λx. 8.0 Linear Algebra: Matrix Eigenvalue Problems Here A is a given
More information1. In this problem, if the statement is always true, circle T; otherwise, circle F.
Math 1553, Extra Practice for Midterm 3 (sections 45-65) Solutions 1 In this problem, if the statement is always true, circle T; otherwise, circle F a) T F If A is a square matrix and the homogeneous equation
More information1. What is the determinant of the following matrix? a 1 a 2 4a 3 2a 2 b 1 b 2 4b 3 2b c 1. = 4, then det
What is the determinant of the following matrix? 3 4 3 4 3 4 4 3 A 0 B 8 C 55 D 0 E 60 If det a a a 3 b b b 3 c c c 3 = 4, then det a a 4a 3 a b b 4b 3 b c c c 3 c = A 8 B 6 C 4 D E 3 Let A be an n n matrix
More informationProblems for M 10/26:
Math, Lesieutre Problem set # November 4, 25 Problems for M /26: 5 Is λ 2 an eigenvalue of 2? 8 Why or why not? 2 A 2I The determinant is, which means that A 2I has 6 a nullspace, and so there is an eigenvector
More informationLinear Algebra Exam 1 Spring 2007
Linear Algebra Exam 1 Spring 2007 March 15, 2007 Name: SOLUTION KEY (Total 55 points, plus 5 more for Pledged Assignment.) Honor Code Statement: Directions: Complete all problems. Justify all answers/solutions.
More informationMath 2331 Linear Algebra
5. Eigenvectors & Eigenvalues Math 233 Linear Algebra 5. Eigenvectors & Eigenvalues Shang-Huan Chiu Department of Mathematics, University of Houston schiu@math.uh.edu math.uh.edu/ schiu/ Shang-Huan Chiu,
More informationJUST THE MATHS SLIDES NUMBER 9.6. MATRICES 6 (Eigenvalues and eigenvectors) A.J.Hobson
JUST THE MATHS SLIDES NUMBER 96 MATRICES 6 (Eigenvalues and eigenvectors) by AJHobson 96 The statement of the problem 962 The solution of the problem UNIT 96 - MATRICES 6 EIGENVALUES AND EIGENVECTORS 96
More informationSolution: In standard form (i.e. y + P (t)y = Q(t)) we have y t y = cos(t)
Math 380 Practice Final Solutions This is longer than the actual exam, which will be 8 to 0 questions (some might be multiple choice). You are allowed up to two sheets of notes (both sides) and a calculator,
More information80 min. 65 points in total. The raw score will be normalized according to the course policy to count into the final score.
This is a closed book, closed notes exam You need to justify every one of your answers unless you are asked not to do so Completely correct answers given without justification will receive little credit
More informationMATH 310, REVIEW SHEET
MATH 310, REVIEW SHEET These notes are a summary of the key topics in the book (and follow the book pretty closely). You should be familiar with everything on here, but it s not comprehensive, so please
More informationAnswers in blue. If you have questions or spot an error, let me know. 1. Find all matrices that commute with A =. 4 3
Answers in blue. If you have questions or spot an error, let me know. 3 4. Find all matrices that commute with A =. 4 3 a b If we set B = and set AB = BA, we see that 3a + 4b = 3a 4c, 4a + 3b = 3b 4d,
More informationFinal. for Math 308, Winter This exam contains 7 questions for a total of 100 points in 15 pages.
Final for Math 308, Winter 208 NAME (last - first): Do not open this exam until you are told to begin. You will have 0 minutes for the exam. This exam contains 7 questions for a total of 00 points in 5
More informationSection 5.5. Complex Eigenvalues (Part II)
Section 5.5 Complex Eigenvalues (Part II) Motivation: Complex Versus Two Real Eigenvalues Today s decomposition is very analogous to diagonalization. Theorem Let A be a 2 2 matrix with linearly independent
More informationReview 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 informationDesigning Information Devices and Systems I Discussion 4B
Last Updated: 29-2-2 9:56 EECS 6A Spring 29 Designing Information Devices and Systems I Discussion 4B Reference Definitions: Matrices and Linear (In)Dependence We ve seen that the following statements
More informationMTH 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 informationMath 152 Second Midterm March 20, 2012
Math 52 Second Midterm March 20, 202 Name: EXAM SOLUTIONS Instructor: Jose Gonzalez Section: 202 Student ID: Exam prepared by Jose Gonzalez. Do not open this exam until you are told to do so. 2. SPECIAL
More informationElementary Linear Algebra Review for Exam 2 Exam is Monday, November 16th.
Elementary Linear Algebra Review for Exam Exam is Monday, November 6th. The exam will cover sections:.4,..4, 5. 5., 7., the class notes on Markov Models. You must be able to do each of the following. Section.4
More informationHomework 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 information1. Linear systems of equations. Chapters 7-8: Linear Algebra. Solution(s) of a linear system of equations (continued)
1 A linear system of equations of the form Sections 75, 78 & 81 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 m1 x 1 + a m2 x 2 + + a mn x n = b m can be written in matrix
More informationMath 308 Final, Autumn 2017
Math 308 Final, Autumn 2017 Name: ID#: Signature: All work on this exam is my own. Instructions. You are allowed a calculator and notesheet (handwritten, two-sided). Hand in your notesheet with your exam.
More informationMath 369 Exam #2 Practice Problem Solutions
Math 369 Exam #2 Practice Problem Solutions 2 5. Is { 2, 3, 8 } a basis for R 3? Answer: No, it is not. To show that it is not a basis, it suffices to show that this is not a linearly independent set.
More informationDot Products. K. Behrend. April 3, Abstract A short review of some basic facts on the dot product. Projections. The spectral theorem.
Dot Products K. Behrend April 3, 008 Abstract A short review of some basic facts on the dot product. Projections. The spectral theorem. Contents The dot product 3. Length of a vector........................
More informationMATH 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 informationLinear Algebra Review. Fei-Fei Li
Linear Algebra Review Fei-Fei Li 1 / 37 Vectors Vectors and matrices are just collections of ordered numbers that represent something: movements in space, scaling factors, pixel brightnesses, etc. A vector
More informationAnnouncements Monday, November 06
Announcements Monday, November 06 This week s quiz: covers Sections 5 and 52 Midterm 3, on November 7th (next Friday) Exam covers: Sections 3,32,5,52,53 and 55 Section 53 Diagonalization Motivation: Difference
More informationLinear Independence Reading: Lay 1.7
Linear Independence Reading: Lay 17 September 11, 213 In this section, we discuss the concept of linear dependence and independence I am going to introduce the definitions and then work some examples and
More informationMath 3C Lecture 25. John Douglas Moore
Math 3C Lecture 25 John Douglas Moore June 1, 2009 Let V be a vector space. A basis for V is a collection of vectors {v 1,..., v k } such that 1. V = Span{v 1,..., v k }, and 2. {v 1,..., v k } are linearly
More informationThe University of British Columbia Final Examination - April 20, 2009 Mathematics 152 All Sections. Closed book examination. No calculators.
The University of British Columbia Final Examination - April 20, 2009 Mathematics 152 All Sections Closed book examination. No calculators. Time: 2.5 hours Last Name First Signature Student Number Section
More informationAnnouncements Monday, November 13
Announcements Monday, November 13 The third midterm is on this Friday, November 17 The exam covers 31, 32, 51, 52, 53, and 55 About half the problems will be conceptual, and the other half computational
More information