UPSC Civil Services Main Mathematics Linear Algebra
|
|
- Grant Strickland
- 5 years ago
- Views:
Transcription
1 UPSC Civil Services Main 99 - Mathematics Linear Algebra Sunder Lal Retired Professor of Mathematics Panjab Universit Chandigarh June 4, 7 Question (a Let U and V be vector spaces over a field K and let V be of finite dimension. Let T : V U be a linear transformation, prove that dim V = dim T(V + dim nullit T. Solution. See question 3(a, ear 998. Question (b Let S = {(x,, z x + + z =, x,, z R}. Prove that S is a subspace of R 3. Find a basis of S. Solution. S because (,, S. If (x,, z, (x,, z S then α (x,, z + α (x,, z S because (α x + α x + (α + α + (α z + α z = α (x + + z + α (x + + z =. Thus S is a subspace of R 3. Clearl (,,, (,, S and are linearl independent. Thus dim S. However (,, S, so S R 3. Thus dim S = and {(,,, (,, } is a basis for S. Question (c Which of the following are linear transformations?. T : R R defined b T(x = (x, x.. T : R R 3 defined b T(x, = (x,, x. 3. T : R R 3 defined b T(x, = (x +,, x. 4. T : R R defined b T(x = (,. Solution.
2 . Thus T is a linear transformation. T(αx + β = (αx + β, αx β = (αx, αx + (β, β = αt(x + βt(. T((, = T(, = (4,, T(, = (,, Thus T is not a linear transformation. 3. T(α(x, + β(x + = T(αx + βx, α + β Thus T is a linear transformation. = (αx + βx + α + β, α + β, αx + βx = α(x +,, x + β(x +,, x = αt(x, + βt(x, 4. T((, = T(, = (, T(, Thus T is not a linear transformation. Question (a Let T : M, M,3 be a linear transformation defined b (with the usual notation ( ( ( ( 3 6 T =, T = 4 5 Find T. Solution. T ( ( ( = x + ( 3 = (x ( 6 = ( x + 4 x 3x 3 4x 4 x 5x 3
3 Question (b For what values of η do the following equations x + + z = x + + 4z = η x z = η have a solution? Solve them in each case. Solution. Since the determinant of the coefficient matrix ( 4 is, the sstem has to 4 be consistent to be solvable. Clearl x z = 3(x + + 4z (x + + z. Thus for the sstem to be consistent we must have η = 3η, or η =,. If η =, then x + + z =, x + + 4z = so + 3z =, or = 3z, x = + z. Thus the space of solutions is {( + z, 3z, z z R}. Note that the rank of the coefficient matrix is, and consequentl the space of solutions is one dimensional. If η =, then x + + z =, x + + 4z =, so + 3z = or = 3z, hence x = z. Consequentl, the space of solutions is {(z, 3z, z z R}. Question (c Prove that a necessar and sufficient condition of a real quadratic form x Ax to be positive definite is that the leading principal minors of A are all positive. Solution. Let all the principal minors be positive. We have to prove that the quadratic form is positive definite. We prove the result b induction. If n =, then a x > a >. Suppose as induction hpothesis the result is true for n = m. Let S = ( B B B be a matrix of a quadratic form in m + variables, where B is m m, B k is m and k is a single element. Since all principle minors of B are leading principal minors of S, and are hence positive, the induction hpothesis gives that B is positive definite. This means that there exists a non-singular m m matrix P such that P BP = I m (We shall prove this presentl. Let C be an m-rowed column to be determined soon. Then ( ( ( ( P B B P C P C = BP P BC + P B k C B P + B P C BC + C B + B C + k B Let C be so chosen that BC + B =, or C = B B. Then ( ( ( ( P B B P C P C = BP k B C + k B Taking determinants, we get P S P = B C + k, because P BP = I m, and B C + k is a single element. Since S ( >, it( follows that B C + k >, so let B C + k = α. Then P C Q Im SQ = I m+ with Q = α. Thus the quadratic forms of S and I m+ take the same values. Hence S is positive definite, so the condition is sufficient. 3
4 The condition is necessar - Since x Ax is positive definite, there is a non-singular matrix P such that P AP = I A P = A >. Let r < n. Let x r+ =... = x n =, then we obtain a quadratic form in r variables which is positive definite. Clearl the determinant of this quadratic form is the r r principal minor of A which shows the result. Proof of the result used: Let A be positive definite, then there exists a non-singular P such that P AP = I. We will prove this b induction. If n =, then the form corresponding to A is a x and a >, so that P = ( a. Take a a... P =. (n (n then a a 3... a n P AP = a 3. (n (n a n Repeating this process, we get a non-singular Q such that a... Q AQ =. (n (n Given the (n (n matrix on the lower right, we get b induction P s.t. P ((n (n matrixp is diagonal. Thus P, P, P AP = [α,..., α n ] sa. Take R = diagonal[ α,..., α n ], then R P APR = I n. Question 3(a State the Cale-Hamilton theorem and use it to find the inverse of ( 4 3. Solution. Let A be an n n matrix. If λi A = λ n + a λ n a n = is the characteristic equation of A, then the Cale-Hamilton theorem sas that A n + a A n a n I = i.e. a matrix satisfies its characteristic equation. The characteristic equation of A = ( 4 3 is λ 4 3 λ = λ 5λ + = 4
5 B the Cale-Hamilton theorem, A 5A + I =, so A(A 5I = I, thus A = (A 5I. Thus A = [( ( ] ( 5 3 = Question 3(b Transform the following into diagonal form x + x, 8x 4x + 5 and give the transformation emploed. Solution. Let A = ( (, B = 8 5 Let = A λb = 8λ + λ + λ 5λ = 5λ + 4λ 4λ 4λ Thus 36λ 9λ =, so λ = 9± 8+44 =,. 7 3 Let (x, x be the vector such that (A λb ( x x = with λ =. Thus 5x x 3 = x = x. We take x = ( so that (A λbx = with λ =. Similarl, if (x 3, x is the vector such that (A λb ( x x = with λ =, then 5x 3 + 5x 6 =, so x + x =. We take x = (. Now x Ax = ( ( ( = ( ( = 3 x Ax = ( ( ( = ( ( = 3 x Ax = ( ( ( = ( ( = If P = (x x, then P AP = ( 3 3 Similarl Thus P BP = ( 9 36, thus x + x 3X 3Y b P = (. x Bx = ( ( ( 8 5 = ( ( 6 3 = 9 x Bx = ( ( ( 8 = ( ( 5 = 36 x Bx = ( ( ( 8 5 = ( ( =, so 8x 4x + 5 is transformed to 9X + 36Y b ( ( X Y = P x Question 3(c Prove that the characteristic roots of a Hermitian matrix are all real, and the characteristic roots of a skew Hermitian matrix are all zero or pure imaginar. Solution. For Hermitian matrices, see question (c, ear 995. If H is skew-hermitian, then ih is Hermitian, because (ih = ih = ih = ih as H = H. Thus the eigenvalues of ih are real. Therefore the eigenvalues of H are ix where x R. So the must be (if x = or pure imaginar. 5
Linear Algebra Lecture Notes-II
Linear Algebra Lecture Notes-II Vikas Bist Department of Mathematics Panjab University, Chandigarh-64 email: bistvikas@gmail.com Last revised on March 5, 8 This text is based on the lectures delivered
More information22.3. Repeated Eigenvalues and Symmetric Matrices. Introduction. Prerequisites. Learning Outcomes
Repeated Eigenvalues and Symmetric Matrices. Introduction In this Section we further develop the theory of eigenvalues and eigenvectors in two distinct directions. Firstly we look at matrices where one
More informationMath 489AB Exercises for Chapter 2 Fall Section 2.3
Math 489AB Exercises for Chapter 2 Fall 2008 Section 2.3 2.3.3. Let A M n (R). Then the eigenvalues of A are the roots of the characteristic polynomial p A (t). Since A is real, p A (t) is a polynomial
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 Linear Algebra Problems
Linear Algebra Problems. Let A be the conjugate transpose of the complex matrix A; i.e., A = A t : A is said to be Hermitian if A = A; real symmetric if A is real and A t = A; skew-hermitian if A = A and
More informationLinear Algebra Practice Problems
Linear Algebra Practice Problems Math 24 Calculus III Summer 25, Session II. Determine whether the given set is a vector space. If not, give at least one axiom that is not satisfied. Unless otherwise stated,
More informationHomework Solutions Week of November = 1(1) + 0(x 1) + 0(x 1) 2 x = 1(1) + 1(x 1) + 0(x 1) 2 x 2 = 1(1) + 2(x 1) + 1(x 1) 2
Math 34/84: Matrix heor Dr S Cooper Fall 8 Section 63: Homework Solutions Week of November 8 (7) he sets B { x (x ) } C { x x } are both bases for P We need to find [p(x) B Observe that () + (x ) + (x
More informationVector Spaces and Linear Transformations
Vector Spaces and Linear Transformations Wei Shi, Jinan University 2017.11.1 1 / 18 Definition (Field) A field F = {F, +, } is an algebraic structure formed by a set F, and closed under binary operations
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 informationI. Multiple Choice Questions (Answer any eight)
Name of the student : Roll No : CS65: Linear Algebra and Random Processes Exam - Course Instructor : Prashanth L.A. Date : Sep-24, 27 Duration : 5 minutes INSTRUCTIONS: The test will be evaluated ONLY
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 informationJUST THE MATHS UNIT NUMBER 9.8. MATRICES 8 (Characteristic properties) & (Similarity transformations) A.J.Hobson
JUST THE MATHS UNIT NUMBER 9.8 MATRICES 8 (Characteristic properties) & (Similarity transformations) by A.J.Hobson 9.8. Properties of eigenvalues and eigenvectors 9.8. Similar matrices 9.8.3 Exercises
More informationLecture 15 Review of Matrix Theory III. Dr. Radhakant Padhi Asst. Professor Dept. of Aerospace Engineering Indian Institute of Science - Bangalore
Lecture 15 Review of Matrix Theory III Dr. Radhakant Padhi Asst. Professor Dept. of Aerospace Engineering Indian Institute of Science - Bangalore Matrix An m n matrix is a rectangular or square array of
More informationThe converse is clear, since
14. The minimal polynomial For an example of a matrix which cannot be diagonalised, consider the matrix ( ) 0 1 A =. 0 0 The characteristic polynomial is λ 2 = 0 so that the only eigenvalue is λ = 0. The
More informationYORK UNIVERSITY. Faculty of Science Department of Mathematics and Statistics MATH M Test #2 Solutions
YORK UNIVERSITY Faculty of Science Department of Mathematics and Statistics MATH 3. M Test # Solutions. (8 pts) For each statement indicate whether it is always TRUE or sometimes FALSE. Note: For this
More informationElementary linear algebra
Chapter 1 Elementary linear algebra 1.1 Vector spaces Vector spaces owe their importance to the fact that so many models arising in the solutions of specific problems turn out to be vector spaces. The
More informationRepeated Eigenvalues and Symmetric Matrices
Repeated Eigenvalues and Symmetric Matrices. Introduction In this Section we further develop the theory of eigenvalues and eigenvectors in two distinct directions. Firstly we look at matrices where one
More informationWe start by computing the characteristic polynomial of A as. det (A λi) = det. = ( 2 λ)(1 λ) (2)(2) = (λ 2)(λ + 3)
Let A [ 2 2 2 Compute the eigenvalues and eigenspaces of A We start b computing the characteristic polnomial of A as [ 2 λ 2 det (A λi) det 2 λ ( 2 λ)( λ) (2)(2) λ 2 + λ 2 4 λ 2 + λ 6 (λ 2)(λ + 3) The
More informationNOTES ON BILINEAR FORMS
NOTES ON BILINEAR FORMS PARAMESWARAN SANKARAN These notes are intended as a supplement to the talk given by the author at the IMSc Outreach Programme Enriching Collegiate Education-2015. Symmetric bilinear
More informationDiagonalizing Matrices
Diagonalizing Matrices Massoud Malek A A Let A = A k be an n n non-singular matrix and let B = A = [B, B,, B k,, B n ] Then A n A B = A A 0 0 A k [B, B,, B k,, B n ] = 0 0 = I n 0 A n Notice that A i B
More information2. Linear algebra. matrices and vectors. linear equations. range and nullspace of matrices. function of vectors, gradient and Hessian
FE661 - Statistical Methods for Financial Engineering 2. Linear algebra Jitkomut Songsiri matrices and vectors linear equations range and nullspace of matrices function of vectors, gradient and Hessian
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 informationThe eigenvalues are the roots of the characteristic polynomial, det(a λi). We can compute
A. [ 3. Let A = 5 5 ]. Find all (complex) eigenvalues and eigenvectors of The eigenvalues are the roots of the characteristic polynomial, det(a λi). We can compute 3 λ A λi =, 5 5 λ from which det(a λi)
More informationNon-singular Linear Transformations
Non-singular Linear Transformations and SUBMITTED BY: Ms. Harjeet Kaur Associate Professor Department of Mathematics PGGCG 11, Chandigarh Definition: A linear transformation T : V V is said to be non-singular
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 informationEcon 204 Supplement to Section 3.6 Diagonalization and Quadratic Forms. 1 Diagonalization and Change of Basis
Econ 204 Supplement to Section 3.6 Diagonalization and Quadratic Forms De La Fuente notes that, if an n n matrix has n distinct eigenvalues, it can be diagonalized. In this supplement, we will provide
More informationMath Matrix Algebra
Math 44 - Matrix Algebra Review notes - (Alberto Bressan, Spring 7) sec: Orthogonal diagonalization of symmetric matrices When we seek to diagonalize a general n n matrix A, two difficulties may arise:
More informationGet Solution of These Packages & Learn by Video Tutorials on Matrices
FEE Download Stud Package from website: wwwtekoclassescom & wwwmathsbsuhagcom Get Solution of These Packages & Learn b Video Tutorials on wwwmathsbsuhagcom Matrices An rectangular arrangement of numbers
More informationMATH 423 Linear Algebra II Lecture 33: Diagonalization of normal operators.
MATH 423 Linear Algebra II Lecture 33: Diagonalization of normal operators. Adjoint operator and adjoint matrix Given a linear operator L on an inner product space V, the adjoint of L is a transformation
More information7. Symmetric Matrices and Quadratic Forms
Linear Algebra 7. Symmetric Matrices and Quadratic Forms CSIE NCU 1 7. Symmetric Matrices and Quadratic Forms 7.1 Diagonalization of symmetric matrices 2 7.2 Quadratic forms.. 9 7.4 The singular value
More informationMath 408 Advanced Linear Algebra
Math 408 Advanced Linear Algebra Chi-Kwong Li Chapter 4 Hermitian and symmetric matrices Basic properties Theorem Let A M n. The following are equivalent. Remark (a) A is Hermitian, i.e., A = A. (b) x
More informationDimension. Eigenvalue and eigenvector
Dimension. Eigenvalue and eigenvector Math 112, week 9 Goals: Bases, dimension, rank-nullity theorem. Eigenvalue and eigenvector. Suggested Textbook Readings: Sections 4.5, 4.6, 5.1, 5.2 Week 9: Dimension,
More informationALGEBRA QUALIFYING EXAM PROBLEMS LINEAR ALGEBRA
ALGEBRA QUALIFYING EXAM PROBLEMS LINEAR ALGEBRA Kent State University Department of Mathematical Sciences Compiled and Maintained by Donald L. White Version: August 29, 2017 CONTENTS LINEAR ALGEBRA AND
More informationLinear Algebra Review. Fei-Fei Li
Linear Algebra Review Fei-Fei Li 1 / 51 Vectors Vectors and matrices are just collections of ordered numbers that represent something: movements in space, scaling factors, pixel brightnesses, etc. A vector
More informationProblem Set (T) If A is an m n matrix, B is an n p matrix and D is a p s matrix, then show
MTH 0: Linear Algebra Department of Mathematics and Statistics Indian Institute of Technology - Kanpur Problem Set Problems marked (T) are for discussions in Tutorial sessions (T) If A is an m n matrix,
More informationMath 310 Final Exam Solutions
Math 3 Final Exam Solutions. ( pts) Consider the system of equations Ax = b where: A, b (a) Compute deta. Is A singular or nonsingular? (b) Compute A, if possible. (c) Write the row reduced echelon form
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 informationHW2 - Due 01/30. Each answer must be mathematically justified. Don t forget your name.
HW2 - Due 0/30 Each answer must be mathematically justified. Don t forget your name. Problem. Use the row reduction algorithm to find the inverse of the matrix 0 0, 2 3 5 if it exists. Double check your
More informationChapter 1. Matrix Algebra
ST4233, Linear Models, Semester 1 2008-2009 Chapter 1. Matrix Algebra 1 Matrix and vector notation Definition 1.1 A matrix is a rectangular or square array of numbers of variables. We use uppercase boldface
More informationMATH 20F: LINEAR ALGEBRA LECTURE B00 (T. KEMP)
MATH 20F: LINEAR ALGEBRA LECTURE B00 (T KEMP) Definition 01 If T (x) = Ax is a linear transformation from R n to R m then Nul (T ) = {x R n : T (x) = 0} = Nul (A) Ran (T ) = {Ax R m : x R n } = {b R m
More informationChapter 7. Linear Algebra: Matrices, Vectors,
Chapter 7. Linear Algebra: Matrices, Vectors, Determinants. Linear Systems Linear algebra includes the theory and application of linear systems of equations, linear transformations, and eigenvalue problems.
More informationCayley-Hamilton Theorem
Cayley-Hamilton Theorem Massoud Malek In all that follows, the n n identity matrix is denoted by I n, the n n zero matrix by Z n, and the zero vector by θ n Let A be an n n matrix Although det (λ I n A
More informationEigenvalues and Eigenvectors
/88 Chia-Ping Chen Department of Computer Science and Engineering National Sun Yat-sen University Linear Algebra Eigenvalue Problem /88 Eigenvalue Equation By definition, the eigenvalue equation for matrix
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 informationMATH 220 FINAL EXAMINATION December 13, Name ID # Section #
MATH 22 FINAL EXAMINATION December 3, 2 Name ID # Section # There are??multiple choice questions. Each problem is worth 5 points. Four possible answers are given for each problem, only one of which is
More informationMATH 369 Linear Algebra
Assignment # Problem # A father and his two sons are together 00 years old. The father is twice as old as his older son and 30 years older than his younger son. How old is each person? Problem # 2 Determine
More informationLecture Summaries for Linear Algebra M51A
These lecture summaries may also be viewed online by clicking the L icon at the top right of any lecture screen. Lecture Summaries for Linear Algebra M51A refers to the section in the textbook. Lecture
More information(here, we allow β j = 0). Generally, vectors are represented as column vectors, not row vectors. β 1. β n. crd V (x) = R n
Economics 24 Lecture 9 Thursday, August 6, 29 Revised 8/6/9, revisions indicated by ** and Sticky Notes Section 3.3 Supplement, Quotient Vector Spaces (not in de la Fuente): Definition 1 Given a vector
More informationLinear Algebra Primer
Linear Algebra Primer David Doria daviddoria@gmail.com Wednesday 3 rd December, 2008 Contents Why is it called Linear Algebra? 4 2 What is a Matrix? 4 2. Input and Output.....................................
More informationMaths for Signals and Systems Linear Algebra in Engineering
Maths for Signals and Systems Linear Algebra in Engineering Lecture 18, Friday 18 th November 2016 DR TANIA STATHAKI READER (ASSOCIATE PROFFESOR) IN SIGNAL PROCESSING IMPERIAL COLLEGE LONDON Mathematics
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 Homework 8 (selected problems)
Math 102 - Homework 8 (selected problems) David Lipshutz Problem 1. (Strang, 5.5: #14) In the list below, which classes of matrices contain A and which contain B? 1 1 1 1 A 0 0 1 0 0 0 0 1 and B 1 1 1
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 informationEigenvalue and Eigenvector Homework
Eigenvalue and Eigenvector Homework Olena Bormashenko November 4, 2 For each of the matrices A below, do the following:. Find the characteristic polynomial of A, and use it to find all the eigenvalues
More informationSystems of Linear Equations
LECTURE 6 Systems of Linear Equations You may recall that in Math 303, matrices were first introduced as a means of encapsulating the essential data underlying a system of linear equations; that is to
More informationMath 21b. Review for Final Exam
Math 21b. Review for Final Exam Thomas W. Judson Spring 2003 General Information The exam is on Thursday, May 15 from 2:15 am to 5:15 pm in Jefferson 250. Please check with the registrar if you have a
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 informationChapter 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 informationMATH 240 Spring, Chapter 1: Linear Equations and Matrices
MATH 240 Spring, 2006 Chapter Summaries for Kolman / Hill, Elementary Linear Algebra, 8th Ed. Sections 1.1 1.6, 2.1 2.2, 3.2 3.8, 4.3 4.5, 5.1 5.3, 5.5, 6.1 6.5, 7.1 7.2, 7.4 DEFINITIONS Chapter 1: Linear
More informationA Proof of Fundamental Theorem of Algebra Through Linear Algebra due to Derksen. Anant R. Shastri. February 13, 2011
A Proof of Fundamental Theorem of Algebra Through Linear Algebra due to Derksen February 13, 2011 MA106-Linear Algebra 2011 MA106-Linear Algebra 2011 We present a proof of the Fundamental Theorem of Algebra
More informationEconomics 204 Summer/Fall 2010 Lecture 10 Friday August 6, 2010
Economics 204 Summer/Fall 2010 Lecture 10 Friday August 6, 2010 Diagonalization of Symmetric Real Matrices (from Handout Definition 1 Let δ ij = { 1 if i = j 0 if i j A basis V = {v 1,..., v n } of R n
More informationMatrix Theory. A.Holst, V.Ufnarovski
Matrix Theory AHolst, VUfnarovski 55 HINTS AND ANSWERS 9 55 Hints and answers There are two different approaches In the first one write A as a block of rows and note that in B = E ij A all rows different
More informationMATH 2331 Linear Algebra. Section 2.1 Matrix Operations. Definition: A : m n, B : n p. Example: Compute AB, if possible.
MATH 2331 Linear Algebra Section 2.1 Matrix Operations Definition: A : m n, B : n p ( 1 2 p ) ( 1 2 p ) AB = A b b b = Ab Ab Ab Example: Compute AB, if possible. 1 Row-column rule: i-j-th entry of AB:
More informationOHSx XM511 Linear Algebra: Solutions to Online True/False Exercises
This document gives the solutions to all of the online exercises for OHSx XM511. The section ( ) numbers refer to the textbook. TYPE I are True/False. Answers are in square brackets [. Lecture 02 ( 1.1)
More informationLinear Algebra. Workbook
Linear Algebra Workbook Paul Yiu Department of Mathematics Florida Atlantic University Last Update: November 21 Student: Fall 2011 Checklist Name: A B C D E F F G H I J 1 2 3 4 5 6 7 8 9 10 xxx xxx xxx
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 informationq n. Q T Q = I. Projections Least Squares best fit solution to Ax = b. Gram-Schmidt process for getting an orthonormal basis from any basis.
Exam Review Material covered by the exam [ Orthogonal matrices Q = q 1... ] q n. Q T Q = I. Projections Least Squares best fit solution to Ax = b. Gram-Schmidt process for getting an orthonormal basis
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 informationTopic 1: Matrix diagonalization
Topic : Matrix diagonalization Review of Matrices and Determinants Definition A matrix is a rectangular array of real numbers a a a m a A = a a m a n a n a nm The matrix is said to be of order n m if it
More informationLinear Algebra II Lecture 22
Linear Algebra II Lecture 22 Xi Chen University of Alberta March 4, 24 Outline Characteristic Polynomial, Eigenvalue, Eigenvector and Eigenvalue, Eigenvector and Let T : V V be a linear endomorphism. We
More informationMath 102, Winter Final Exam Review. Chapter 1. Matrices and Gaussian Elimination
Math 0, Winter 07 Final Exam Review Chapter. Matrices and Gaussian Elimination { x + x =,. Different forms of a system of linear equations. Example: The x + 4x = 4. [ ] [ ] [ ] vector form (or the column
More informationChapter 6 Inner product spaces
Chapter 6 Inner product spaces 6.1 Inner products and norms Definition 1 Let V be a vector space over F. An inner product on V is a function, : V V F such that the following conditions hold. x+z,y = x,y
More informationChapter 3. Determinants and Eigenvalues
Chapter 3. Determinants and Eigenvalues 3.1. Determinants With each square matrix we can associate a real number called the determinant of the matrix. Determinants have important applications to the theory
More informationPositive Definite Matrix
1/29 Chia-Ping Chen Professor Department of Computer Science and Engineering National Sun Yat-sen University Linear Algebra Positive Definite, Negative Definite, Indefinite 2/29 Pure Quadratic Function
More informationLinear Algebra. Matrices Operations. Consider, for example, a system of equations such as x + 2y z + 4w = 0, 3x 4y + 2z 6w = 0, x 3y 2z + w = 0.
Matrices Operations Linear Algebra Consider, for example, a system of equations such as x + 2y z + 4w = 0, 3x 4y + 2z 6w = 0, x 3y 2z + w = 0 The rectangular array 1 2 1 4 3 4 2 6 1 3 2 1 in which the
More informationSAMPLE OF THE STUDY MATERIAL PART OF CHAPTER 1 Introduction to Linear Algebra
1.1. Introduction SAMPLE OF THE STUDY MATERIAL PART OF CHAPTER 1 Introduction to Linear algebra is a specific branch of mathematics dealing with the study of vectors, vector spaces with functions that
More informationTopic 1 [145 marks] Markscheme. Examiners report. since. I is the identity A1 and. R is reflexive. it follows that B = AX 1 A1 and.
Topic [ marks] a. since = I where I is the identity and det(i) =, R is reflexive RB = BX where det(x) = M it follows that B = X and det( X ) = det(x ) = R is symmetric RB and BRC = BX and B = CY where
More informationMatrix 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 informationLINEAR ALGEBRA REVIEW
LINEAR ALGEBRA REVIEW SPENCER BECKER-KAHN Basic Definitions Domain and Codomain. Let f : X Y be any function. This notation means that X is the domain of f and Y is the codomain of f. This means that for
More informationChap 3. Linear Algebra
Chap 3. Linear Algebra Outlines 1. Introduction 2. Basis, Representation, and Orthonormalization 3. Linear Algebraic Equations 4. Similarity Transformation 5. Diagonal Form and Jordan Form 6. Functions
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 informationNumerical Linear Algebra Homework Assignment - Week 2
Numerical Linear Algebra Homework Assignment - Week 2 Đoàn Trần Nguyên Tùng Student ID: 1411352 8th October 2016 Exercise 2.1: Show that if a matrix A is both triangular and unitary, then it is diagonal.
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 informationSummary of Week 9 B = then A A =
Summary of Week 9 Finding the square root of a positive operator Last time we saw that positive operators have a unique positive square root We now briefly look at how one would go about calculating the
More informationLecture 1: Review of linear algebra
Lecture 1: Review of linear algebra Linear functions and linearization Inverse matrix, least-squares and least-norm solutions Subspaces, basis, and dimension Change of basis and similarity transformations
More informationBasics. A VECTOR is a quantity with a specified magnitude and direction. A MATRIX is a rectangular array of quantities
Some Linear Algebra Basics A VECTOR is a quantity with a specified magnitude and direction Vectors can exist in multidimensional space, with each element of the vector representing a quantity in a different
More informationSolutions to Final Practice Problems Written by Victoria Kala Last updated 12/5/2015
Solutions to Final Practice Problems Written by Victoria Kala vtkala@math.ucsb.edu Last updated /5/05 Answers This page contains answers only. See the following pages for detailed solutions. (. (a x. See
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 informationAssignment 1 Math 5341 Linear Algebra Review. Give complete answers to each of the following questions. Show all of your work.
Assignment 1 Math 5341 Linear Algebra Review Give complete answers to each of the following questions Show all of your work Note: You might struggle with some of these questions, either because it has
More information3 (Maths) Linear Algebra
3 (Maths) Linear Algebra References: Simon and Blume, chapters 6 to 11, 16 and 23; Pemberton and Rau, chapters 11 to 13 and 25; Sundaram, sections 1.3 and 1.5. The methods and concepts of linear algebra
More informationSolution: a) Let us consider the matrix form of the system, focusing on the augmented matrix: 0 k k + 1. k 2 1 = 0. k = 1
Exercise. Given the system of equations 8 < : x + y + z x + y + z k x + k y + z k where k R. a) Study the system in terms of the values of k. b) Find the solutions when k, using the reduced row echelon
More informationConceptual Questions for Review
Conceptual Questions for Review Chapter 1 1.1 Which vectors are linear combinations of v = (3, 1) and w = (4, 3)? 1.2 Compare the dot product of v = (3, 1) and w = (4, 3) to the product of their lengths.
More informationEigenvalues and Eigenvectors: An Introduction
Eigenvalues and Eigenvectors: An Introduction The eigenvalue problem is a problem of considerable theoretical interest and wide-ranging application. For example, this problem is crucial in solving systems
More informationThis paper is not to be removed from the Examination Halls
~~MT1173 ZA d0 This paper is not to be removed from the Examination Halls UNIVERSITY OF LONDON MT1173 ZA BSc degrees and Diplomas for Graduates in Economics, Management, Finance and the Social Sciences,
More informationAMS526: Numerical Analysis I (Numerical Linear Algebra)
AMS526: Numerical Analysis I (Numerical Linear Algebra) Lecture 16: Eigenvalue Problems; Similarity Transformations Xiangmin Jiao Stony Brook University Xiangmin Jiao Numerical Analysis I 1 / 18 Eigenvalue
More informationThe Singular Value Decomposition
The Singular Value Decomposition Philippe B. Laval KSU Fall 2015 Philippe B. Laval (KSU) SVD Fall 2015 1 / 13 Review of Key Concepts We review some key definitions and results about matrices that will
More informationDistribution for the Standard Eigenvalues of Quaternion Matrices
International Mathematical Forum, Vol. 7, 01, no. 17, 831-838 Distribution for the Standard Eigenvalues of Quaternion Matrices Shahid Qaisar College of Mathematics and Statistics, Chongqing University
More informationChapter 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 informationUNIT 6: The singular value decomposition.
UNIT 6: The singular value decomposition. María Barbero Liñán Universidad Carlos III de Madrid Bachelor in Statistics and Business Mathematical methods II 2011-2012 A square matrix is symmetric if A T
More informationThe skew-symmetric orthogonal solutions of the matrix equation AX = B
Linear Algebra and its Applications 402 (2005) 303 318 www.elsevier.com/locate/laa The skew-symmetric orthogonal solutions of the matrix equation AX = B Chunjun Meng, Xiyan Hu, Lei Zhang College of Mathematics
More information