Matrix Mathematics. Errata and Addenda for the Second Edition. Dennis S. Bernstein February 13, 2014
|
|
- Trevor Hubbard
- 5 years ago
- Views:
Transcription
1 Matrix Mathematics Errata and Addenda for the Second Edition Dennis S. Bernstein February 13, 2014 This document contains an updated list of errata for the second edition of Matrix Mathematics. Please me if you discover additional errors, and I will include them in future updates. 1. Page 3, line 20: Replace and with and, respectively. 2. Page 13, Fact , statement i): Delete f 1 [f(a)]. 3. Page 24, Fact : Append ) to the left hand side of the inequality, and ] to the right hand side of the inequality. 4. Page 46, Fact : In the inequality below In particular, multiply the right hand side by Page 49, Proof of Fact : Replace obtuse with isosceles. 6. Page 51, Fact : Replace (y 2 + z 2 ) 2 with (y 2 z 2 ) Page 55, Fact : Replace complex with real. 8. Page 61, Fact : For r = 0, define M r by ( n i=1 xα i i ) 1/n. 9. Page 67, Fact : Delete the statement beginning with Now, and the following statement. 10. Page 68, Fact : Replace the last sentence with: Furthermore, equality holds if and only if there exists α 0 such that either [x 1 x n ] = α[y 1 y n ] or [y 1 y n ] = α[x 1 x n ] Page 75, Fact : In statements xix) and xx) change (Re z) 2 to 2(Re z) Page 76, Fact : In xxiii), change to =. 13. Page 83, Fact , statement xxviii): Replace tan 1 xy x+y 1 xy with tan 1 1 xy. 14. Page 105, Fact 2.5.6: Replace F n m with F n n. 15. Page 106, Proof of Proposition 2.5.9: Change the second to =. 1
2 16. Page 110, line -2: Change F m to F n. 17. Page 123, Fact : In statement i), require S F m. 18. Page 124, Fact : In statement i), delete f 1 [f(s)]. 19. Page 126, Fact , statement iii): Replace N(A ) R(B ) with N(B ) R(A ). 20. Page 128, Fact : For the two equalities, assume that both x and y are nonzero. 21. Page 131, Fact : In iv) replace the first = with = {0} and. 22. Page 137, Fact : In ii) change R to rank. 23. Page 138, Fact : In the definition of C replace (A T + yx T ) k y with (A T + yx T ) k 1 y. 24. Page 155, Fact : Replace 1 T 1 n AA 1 with 1 1 n A A 1 n Page 166, line 6: Replace nonsingular. Then, with nonsingular, then 26. Page 170, Fact : Replace 18R 2 a 2 + b 2 + c 2 with 18rR a 2 + b 2 + c Page 172, Fact , lines 6 and 12: Replace ac + bc with ac + bd. 28. Page 175, Fact , line 2: Replace [x y z] with det [x y z]. 29. Page 185, Section 3.3, line 2: Replace Section 11.5 with Section Page 193, Fact 3.7.7: Change Let A R n n. to Let A R n n, and assume that A is symmetric Page 193, Fact 3.7.9: In v) and vi), change if and only if to only if. 32. Page 194, Fact : Assume that A is skew-hermitian. 33. Pages 197 and 198, Fact and Fact : Change then ˆB and Ĉ are given by to then B and C are given by. 34. Page 203, Fact : In xliii) replace (x T x) 1 with (x T x) Page 215, Fact : Replace only if with only if A is semisimple and. 36. Page 227, Fact : In statement iii) replace = with. 37. Page 227, Fact : The solution to the problem is yes. 38. Page 231, Fact : Change p. 114 to p Page 236, Fact : Delete Toeplitz. 40. Page 237, Fact : In statement ii), change A to A Page 248: Replace c 2 + c 2 with c 2 + d Page 272, line -4: Change n = 1 to i = Page 276, Fact 4.8.2, line -5: Change β 3 to β n 3. 2
3 44. Page 276, Fact 4.8.2, line -2: Change Fact to Fact Page 300, Fact : Change perturbation to permutation twice. 46. Page 304, line 8: Replace L k+1 L k with lim k L k+1 L k. 47. Page 306, Fact : Change [174, p. 27] to [186, p. 27]. 48. Page 306, Fact : Change x > 0 and y > 0 to x >> 0 and y >> Page 320, proof of Proposition 5.4.6: Replace S by Ŝ, diag(b 1, 0, B 2 ) by diag( B 1, 0, B 2 ), I ν (A) by I ν (A), and I ν+ (A) by I ν+ (A). 50. Page 328, replace (5.6.9) by: {σ 1,..., σ n } ms = { λ 1 (A),..., λ n (A) } ms. 51. Page 333, Proposition 5.7.4: Change hold to are equivalent. 52. Page 337, Fact : Delete, and assume that S is nonsingular. 53. Page 338, Fact 5.9.2: Change min { a, b } to a 2 + b Page 342, Fact : Change mspec(i n ) = mspec(i n ) to mspec(în) = mspec(în). 55. Page 342, Fact : Change the (1,2) block of S to I. 56. Page 342, Fact : Change F n m to F n n. 57. Page 347, Fact : Change an orthogonal matrix to a projector. 58. Page 355, Fact : Delete the last sentence. 59. Page 355, Fact : Delete the left-hand inequalities in the first two strings. 60. Page 365, Fact : Assume B F m m and replace σ i (B) by σ 2 i (B) 61. Page 394, Fact : Can omit assume that A is nonsingular 62. Page 399, Fact 6.1.6: In xxx), change orthogonal to unitary. 63. Page 404, Fact 6.3.6: Change Let A F n m to Let n 2, let A F n n. 64. Page 411, Fact : Change (A I) = (A I) + to (A I) # = (A I) Page 412, Fact 6.4.2: Change Finally, to Finally, assume that A does not have full rank. Then,. 66. Page 419, Fact : Change A B to AB. 67. Page 423, Fact 6.5.5: Change and assume to and assume that A is Hermitian and. 68. Page 424, Fact 6.5.8: Change rank(d B A + B) to rank(c B A + B). 69. Page 431, Fact 6.6.3: Delete AB = Page 435, Fact : Change BA is nonsingular to CB is nonsingular. 71. Page 452, Fact : Change A, D to A, C and change C, B to B, D 72. Page 452, Fact : Change A (k) B (k) to A (n) B (n). 3
4 73. Page 456, Fact : Change x 1,..., x n R n to x 1,..., x n R. 74. Page 463, Proposition 8.2.7: In v) change β 0,..., β n 1 0 to ( 1) n i β i Page 466, proof of Corollary 8.3.7, line 2: Replace R by F. 76. Page 473, Lemma 8.5.1: Change λ nr to λ r ; change µ nr to µ r ; and change λ r R to µ r R. 77. Page 488, Fact 8.7.8: Delete and for S {1,..., n} and change inclusions to inequalities. 78. Page 502, Fact : Assume that A I is positive definite. 79. Page 505, Fact : Assume that S is nonsingular. 80. Page 505, Fact and Fact : Change H n n to H n. 81. Page 524, Fact : Delete tr AB. 82. Page 534, Fact : Change A + B on the left hand side to B 83. Page 526, Fact : The upper left inequality of the Problem is false. 84. Page 534, Fact : Change and assume that A is positive semidefinite to assume that A is positive semidefinite, and assume that B is skew Hermitian. Also, on the left hand side of the first inequality, replace A + B with B. 85. Page 535, Fact : Change det(a + ȷB) to det(a + ȷB). 86. Page 543, Fact : Change det A det à to det A det Ãn. 87. Page 547, Fact : Change the second instance of λ min to λ max. 88. Page 553, Fact : Change R n to F n and change min to max. 89. Page 561, Fact : Change σ i (A) to rσ i (A) 90. Page 563, Fact : Change λ i to λ i (A) twice. 91. Page 565, Fact : Change λ i [f(αa + (1 α)b)] αλ i [f(a)] + (1 α)λ i [f(b)]. to λ i [f(αa + (1 α)b)] λ i [αf(a) + (1 α)f(b)] Page 566, Fact : Multiply the left hand sides of the third and fourth strings by 1/2 93. Page 568, Fact : The answer to the problem is yes. 94. Page 572, Fact : Change spec(a) to spec(ab). 95. Page 578, Fact : Delete 1/2 on the right-hand side of the equation. 96. Page 578, Fact : In the definition of S, change A to A. Also, change A AA + A to A AA + A. 97. Page 585, Fact : Change 1 spec(a A 1 ) to 1 spec(a A T ). 98. Page 590, Fact : Change Hermitian to positive semidefinite. 4
5 99. Page 611, Corollary : Change A F m l to B F m l Page 617, Corollary 9.6.9: Assume that n m Page 618, Fact 9.7.4: In v), change the second + to Page 625, Fact : In the second string, change the fifth + to. In the last string, change the last + to Page 636, Fact 9.9.5: Change A B to B A B Page 642, Fact : Change λ i (A) to λ i, and change λ j (A) to λ j 105. Page 643, Fact : Change all to σp 106. Page 658, Fact : Change λ j to µ j 107. Page 659, Fact : Delete the second remark Page 663, Fact : Change the upper limit of the last summation to n 109. Page 665, Fact : Define r = min {m, n} Page 671, Fact : Replace A, B F n m by A F n m and B F m n 111. Page 702, Fact : Change 2sB to 2sB Page 731, first displayed equation: Change x [(ȷωI to x [(ȷωI Page 798, line 3: Change ( ) to ( ) 114. Page 809, 2 lines above (12.6.5): Change m nm to n nm 115. Page 813, Proposition : Change K R n m to K R m n. Also, the proof is missing a change of basis transformation Page 813: A 1 R q q and B k F q m. In line 4, replace A by A T Page 821, Line 6 of the proof of Proposition : Change Proposition to Proposition Page 823, Proof of Proposition : In the matrix at the bottom of the page, the entries below the sub-anti-diagonal are incorrect Page 824, Line 2: Multiply the 2 2 matrix with 1 s on the diagonal by Page 824: Proposition : Change A Rˆn ˆn to  Rˆn ˆn Page 825: Corollary : Define S c = [O(A, C)] 1 O(A c, C c ) and S o = [O(A, C)] 1 O(A o, C o ) Page 840, line 3: Change G 2 + G 2 to G 1 + G Page 842, line above ( ): Change +ȷ cos ϕ 0 to ȷ cos ϕ Page 842, one line above ( ): Delete Re. 5
6 125. Page 852, line 2: Change P 4 to P Page 858, line 8: Change Z R n n to Z R 2n 2n Page 859, line 4: Change λz to λx Page 859, Section 12.18: Change the first paragraph to In this section we consider the existence of the maximal solution of ( ). Example shows that ( ) may not have a maximal solution. 6
CHARACTERIZATIONS. is pd/psd. Possible for all pd/psd matrices! Generating a pd/psd matrix: Choose any B Mn, then
LECTURE 6: POSITIVE DEFINITE MATRICES Definition: A Hermitian matrix A Mn is positive definite (pd) if x Ax > 0 x C n,x 0 A is positive semidefinite (psd) if x Ax 0. Definition: A Mn is negative (semi)definite
More informationLecture Notes in Linear Algebra
Lecture Notes in Linear Algebra Dr. Abdullah Al-Azemi Mathematics Department Kuwait University February 4, 2017 Contents 1 Linear Equations and Matrices 1 1.2 Matrices............................................
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 informationChapter SSM: Linear Algebra Section Fails to be invertible; since det = 6 6 = Invertible; since det = = 2.
SSM: Linear Algebra Section 61 61 Chapter 6 1 2 1 Fails to be invertible; since det = 6 6 = 0 3 6 3 5 3 Invertible; since det = 33 35 = 2 7 11 5 Invertible; since det 2 5 7 0 11 7 = 2 11 5 + 0 + 0 0 0
More informationLinear Algebra: Lecture notes from Kolman and Hill 9th edition.
Linear Algebra: Lecture notes from Kolman and Hill 9th edition Taylan Şengül March 20, 2019 Please let me know of any mistakes in these notes Contents Week 1 1 11 Systems of Linear Equations 1 12 Matrices
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 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 informationInstructions. 2. Four possible answers are provided for each question and only one of these is correct.
Instructions 1. This question paper has forty multiple choice questions. 2. Four possible answers are provided for each question and only one of these is correct. 3. Marking scheme: Each correct answer
More informationCP3 REVISION LECTURES VECTORS AND MATRICES Lecture 1. Prof. N. Harnew University of Oxford TT 2013
CP3 REVISION LECTURES VECTORS AND MATRICES Lecture 1 Prof. N. Harnew University of Oxford TT 2013 1 OUTLINE 1. Vector Algebra 2. Vector Geometry 3. Types of Matrices and Matrix Operations 4. Determinants
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 informationMatrix Algebra. Matrix Algebra. Chapter 8 - S&B
Chapter 8 - S&B Algebraic operations Matrix: The size of a matrix is indicated by the number of its rows and the number of its columns. A matrix with k rows and n columns is called a k n matrix. The number
More informationMatrices. Chapter What is a Matrix? We review the basic matrix operations. An array of numbers a a 1n A = a m1...
Chapter Matrices We review the basic matrix operations What is a Matrix? An array of numbers a a n A = a m a mn with m rows and n columns is a m n matrix Element a ij in located in position (i, j The elements
More informationThroughout these notes we assume V, W are finite dimensional inner product spaces over C.
Math 342 - Linear Algebra II Notes Throughout these notes we assume V, W are finite dimensional inner product spaces over C 1 Upper Triangular Representation Proposition: Let T L(V ) There exists an orthonormal
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 informationRecall the convention that, for us, all vectors are column vectors.
Some linear algebra Recall the convention that, for us, all vectors are column vectors. 1. Symmetric matrices Let A be a real matrix. Recall that a complex number λ is an eigenvalue of A if there exists
More informationMIT Final Exam Solutions, Spring 2017
MIT 8.6 Final Exam Solutions, Spring 7 Problem : For some real matrix A, the following vectors form a basis for its column space and null space: C(A) = span,, N(A) = span,,. (a) What is the size m n of
More informationHOMEWORK PROBLEMS FROM STRANG S LINEAR ALGEBRA AND ITS APPLICATIONS (4TH EDITION)
HOMEWORK PROBLEMS FROM STRANG S LINEAR ALGEBRA AND ITS APPLICATIONS (4TH EDITION) PROFESSOR STEVEN MILLER: BROWN UNIVERSITY: SPRING 2007 1. CHAPTER 1: MATRICES AND GAUSSIAN ELIMINATION Page 9, # 3: Describe
More informationMath 407: Linear Optimization
Math 407: Linear Optimization Lecture 16: The Linear Least Squares Problem II Math Dept, University of Washington February 28, 2018 Lecture 16: The Linear Least Squares Problem II (Math Dept, University
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 informationSome properties of index of Lie algebras
arxiv:math/0001042v1 [math.rt] 7 Jan 2000 Some properties of index of Lie algebras Contents Vladimir Dergachev February 1, 2008 1 Introduction 1 2 Index of Lie algebras 2 3 Tensor products 3 4 Associative
More informationExercise Sheet 1.
Exercise Sheet 1 You can download my lecture and exercise sheets at the address http://sami.hust.edu.vn/giang-vien/?name=huynt 1) Let A, B be sets. What does the statement "A is not a subset of B " mean?
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 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 informationMATRIX ALGEBRA AND SYSTEMS OF EQUATIONS. + + x 1 x 2. x n 8 (4) 3 4 2
MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS SYSTEMS OF EQUATIONS AND MATRICES Representation of a linear system The general system of m equations in n unknowns can be written a x + a 2 x 2 + + a n x n b a
More informationComputational math: Assignment 1
Computational math: Assignment 1 Thanks Ting Gao for her Latex file 11 Let B be a 4 4 matrix to which we apply the following operations: 1double column 1, halve row 3, 3add row 3 to row 1, 4interchange
More informationGENERALIZED MATRIX MULTIPLICATION AND ITS SOME APPLICATION. 1. Introduction
FACTA UNIVERSITATIS (NIŠ) Ser Math Inform Vol, No 5 (7), 789 798 https://doiorg/9/fumi75789k GENERALIZED MATRIX MULTIPLICATION AND ITS SOME APPLICATION Osman Keçilioğlu and Halit Gündoğan Abstract In this
More informationSymmetric and anti symmetric matrices
Symmetric and anti symmetric matrices In linear algebra, a symmetric matrix is a square matrix that is equal to its transpose. Formally, matrix A is symmetric if. A = A Because equal matrices have equal
More information18.06 Problem Set 8 - Solutions Due Wednesday, 14 November 2007 at 4 pm in
806 Problem Set 8 - Solutions Due Wednesday, 4 November 2007 at 4 pm in 2-06 08 03 Problem : 205+5+5+5 Consider the matrix A 02 07 a Check that A is a positive Markov matrix, and find its steady state
More informationEE/ACM Applications of Convex Optimization in Signal Processing and Communications Lecture 2
EE/ACM 150 - Applications of Convex Optimization in Signal Processing and Communications Lecture 2 Andre Tkacenko Signal Processing Research Group Jet Propulsion Laboratory April 5, 2012 Andre Tkacenko
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 informationMatrix Completion Problems for Pairs of Related Classes of Matrices
Matrix Completion Problems for Pairs of Related Classes of Matrices Leslie Hogben Department of Mathematics Iowa State University Ames, IA 50011 lhogben@iastate.edu Abstract For a class X of real matrices,
More informationNOTES on LINEAR ALGEBRA 1
School of Economics, Management and Statistics University of Bologna Academic Year 207/8 NOTES on LINEAR ALGEBRA for the students of Stats and Maths This is a modified version of the notes by Prof Laura
More informationCHAPTER 2 -idempotent matrices
CHAPTER 2 -idempotent matrices A -idempotent matrix is defined and some of its basic characterizations are derived (see [33]) in this chapter. It is shown that if is a -idempotent matrix then it is quadripotent
More informationNOTES ON LINEAR ALGEBRA. 1. Determinants
NOTES ON LINEAR ALGEBRA 1 Determinants In this section, we study determinants of matrices We study their properties, methods of computation and some applications We are familiar with the following formulas
More information18.06 Problem Set 8 Solution Due Wednesday, 22 April 2009 at 4 pm in Total: 160 points.
86 Problem Set 8 Solution Due Wednesday, April 9 at 4 pm in -6 Total: 6 points Problem : If A is real-symmetric, it has real eigenvalues What can you say about the eigenvalues if A is real and anti-symmetric
More informationMAT Linear Algebra Collection of sample exams
MAT 342 - Linear Algebra Collection of sample exams A-x. (0 pts Give the precise definition of the row echelon form. 2. ( 0 pts After performing row reductions on the augmented matrix for a certain system
More information2 Constructions of manifolds. (Solutions)
2 Constructions of manifolds. (Solutions) Last updated: February 16, 2012. Problem 1. The state of a double pendulum is entirely defined by the positions of the moving ends of the two simple pendula of
More informationSpectral inequalities and equalities involving products of matrices
Spectral inequalities and equalities involving products of matrices Chi-Kwong Li 1 Department of Mathematics, College of William & Mary, Williamsburg, Virginia 23187 (ckli@math.wm.edu) Yiu-Tung Poon Department
More informationMath 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 informationEigenvalues, Eigenvectors. Eigenvalues and eigenvector will be fundamentally related to the nature of the solutions of state space systems.
Chapter 3 Linear Algebra In this Chapter we provide a review of some basic concepts from Linear Algebra which will be required in order to compute solutions of LTI systems in state space form, discuss
More informationANSWERS (5 points) Let A be a 2 2 matrix such that A =. Compute A. 2
MATH 7- Final Exam Sample Problems Spring 7 ANSWERS ) ) ). 5 points) Let A be a matrix such that A =. Compute A. ) A = A ) = ) = ). 5 points) State ) the definition of norm, ) the Cauchy-Schwartz inequality
More informationOptimization problems on the rank and inertia of the Hermitian matrix expression A BX (BX) with applications
Optimization problems on the rank and inertia of the Hermitian matrix expression A BX (BX) with applications Yongge Tian China Economics and Management Academy, Central University of Finance and Economics,
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 informationErrata to Introduction to Lie Algebras
Errata to Introduction to Lie Algebras We would like to thank Thorsten Holm for many of the corrections below. The reprinted 1st edition (published June 2007) incorporates all corrections except those
More informationMassachusetts Institute of Technology Department of Economics Statistics. Lecture Notes on Matrix Algebra
Massachusetts Institute of Technology Department of Economics 14.381 Statistics Guido Kuersteiner Lecture Notes on Matrix Algebra These lecture notes summarize some basic results on matrix algebra used
More informationQuantum Computing Lecture 2. Review of Linear Algebra
Quantum Computing Lecture 2 Review of Linear Algebra Maris Ozols Linear algebra States of a quantum system form a vector space and their transformations are described by linear operators Vector spaces
More information1 Matrices and vector spaces
Matrices and vector spaces. Which of the following statements about linear vector spaces are true? Where a statement is false, give a counter-example to demonstrate this. (a) Non-singular N N matrices
More informationLecture notes: Applied linear algebra Part 1. Version 2
Lecture notes: Applied linear algebra Part 1. Version 2 Michael Karow Berlin University of Technology karow@math.tu-berlin.de October 2, 2008 1 Notation, basic notions and facts 1.1 Subspaces, range and
More informationHomework 1 Elena Davidson (B) (C) (D) (E) (F) (G) (H) (I)
CS 106 Spring 2004 Homework 1 Elena Davidson 8 April 2004 Problem 1.1 Let B be a 4 4 matrix to which we apply the following operations: 1. double column 1, 2. halve row 3, 3. add row 3 to row 1, 4. interchange
More information0.1 Rational Canonical Forms
We have already seen that it is useful and simpler to study linear systems using matrices. But matrices are themselves cumbersome, as they are stuffed with many entries, and it turns out that it s best
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 informationOCR Maths FP1. Topic Questions from Papers. Matrices. Answers
OCR Maths FP Topic Questions from Papers Matrices Answers PhysicsAndMathsTutor.com . (i) A = 8 PhysicsAndMathsTutor.com Obtain given answer correctly Attempt to find A, elements correct All elements correct
More informationPart IA. Vectors and Matrices. Year
Part IA Vectors and Matrices Year 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2018 Paper 1, Section I 1C Vectors and Matrices For z, w C define the principal value of z w. State de Moivre s
More informationMathematical Methods wk 2: Linear Operators
John Magorrian, magog@thphysoxacuk These are work-in-progress notes for the second-year course on mathematical methods The most up-to-date version is available from http://www-thphysphysicsoxacuk/people/johnmagorrian/mm
More informationSolution to Homework 1
Solution to Homework Sec 2 (a) Yes It is condition (VS 3) (b) No If x, y are both zero vectors Then by condition (VS 3) x = x + y = y (c) No Let e be the zero vector We have e = 2e (d) No It will be false
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 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 informationLINEAR ALGEBRA WITH APPLICATIONS
SEVENTH EDITION LINEAR ALGEBRA WITH APPLICATIONS Instructor s Solutions Manual Steven J. Leon PREFACE This solutions manual is designed to accompany the seventh edition of Linear Algebra with Applications
More informationInequalities in Hilbert Spaces
Inequalities in Hilbert Spaces Jan Wigestrand Master of Science in Mathematics Submission date: March 8 Supervisor: Eugenia Malinnikova, MATH Norwegian University of Science and Technology Department of
More informationMATH 5720: Unconstrained Optimization Hung Phan, UMass Lowell September 13, 2018
MATH 57: Unconstrained Optimization Hung Phan, UMass Lowell September 13, 18 1 Global and Local Optima Let a function f : S R be defined on a set S R n Definition 1 (minimizers and maximizers) (i) x S
More informationMath 240 Calculus III
The Calculus III Summer 2015, Session II Wednesday, July 8, 2015 Agenda 1. of the determinant 2. determinants 3. of determinants What is the determinant? Yesterday: Ax = b has a unique solution when A
More informationMATRICES AND MATRIX OPERATIONS
SIZE OF THE MATRIX is defined by number of rows and columns in the matrix. For the matrix that have m rows and n columns we say the size of the matrix is m x n. If matrix have the same number of rows (n)
More informationLecture 2 - Unconstrained Optimization Definition[Global Minimum and Maximum]Let f : S R be defined on a set S R n. Then
Lecture 2 - Unconstrained Optimization Definition[Global Minimum and Maximum]Let f : S R be defined on a set S R n. Then 1. x S is a global minimum point of f over S if f (x) f (x ) for any x S. 2. x S
More informationMATH 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 informationPage 21, Exercise 6 should read: bysetting up a one-to-one correspondence between the set Z = {..., 3, 2, 1, 0, 1, 2, 3,...} and the set N.
Errata for Elementary Classical Analysis, Second Edition Jerrold E. Marsden and Michael J. Hoffman marsden@cds.caltech.edu, mhoffma@calstatela.edu Version: August 10, 2001. What follows are the errata
More informationMathematical Foundations
Chapter 1 Mathematical Foundations 1.1 Big-O Notations In the description of algorithmic complexity, we often have to use the order notations, often in terms of big O and small o. Loosely speaking, for
More informationSymmetric and self-adjoint matrices
Symmetric and self-adjoint matrices A matrix A in M n (F) is called symmetric if A T = A, ie A ij = A ji for each i, j; and self-adjoint if A = A, ie A ij = A ji or each i, j Note for A in M n (R) that
More informationAPPENDIX: MATHEMATICAL INDUCTION AND OTHER FORMS OF PROOF
ELEMENTARY LINEAR ALGEBRA WORKBOOK/FOR USE WITH RON LARSON S TEXTBOOK ELEMENTARY LINEAR ALGEBRA CREATED BY SHANNON MARTIN MYERS APPENDIX: MATHEMATICAL INDUCTION AND OTHER FORMS OF PROOF When you are done
More informationMathematics. EC / EE / IN / ME / CE. for
Mathematics for EC / EE / IN / ME / CE By www.thegateacademy.com Syllabus Syllabus for Mathematics Linear Algebra: Matrix Algebra, Systems of Linear Equations, Eigenvalues and Eigenvectors. Probability
More informationMath 18.6, Spring 213 Problem Set #6 April 5, 213 Problem 1 ( 5.2, 4). Identify all the nonzero terms in the big formula for the determinants of the following matrices: 1 1 1 2 A = 1 1 1 1 1 1, B = 3 4
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 informationAssignment 11 (C + C ) = (C + C ) = (C + C) i(c C ) ] = i(c C) (AB) = (AB) = B A = BA 0 = [A, B] = [A, B] = (AB BA) = (AB) AB
Arfken 3.4.6 Matrix C is not Hermition. But which is Hermitian. Likewise, Assignment 11 (C + C ) = (C + C ) = (C + C) [ i(c C ) ] = i(c C ) = i(c C) = i ( C C ) Arfken 3.4.9 The matrices A and B are both
More informationMath 489AB Exercises for Chapter 1 Fall Section 1.0
Math 489AB Exercises for Chapter 1 Fall 2008 Section 1.0 1.0.2 We want to maximize x T Ax subject to the condition x T x = 1. We use the method of Lagrange multipliers. Let f(x) = x T Ax and g(x) = x T
More informationExtra Problems for Math 2050 Linear Algebra I
Extra Problems for Math 5 Linear Algebra I Find the vector AB and illustrate with a picture if A = (,) and B = (,4) Find B, given A = (,4) and [ AB = A = (,4) and [ AB = 8 If possible, express x = 7 as
More informationLectures on Linear Algebra for IT
Lectures on Linear Algebra for IT by Mgr. Tereza Kovářová, Ph.D. following content of lectures by Ing. Petr Beremlijski, Ph.D. Department of Applied Mathematics, VSB - TU Ostrava Czech Republic 11. Determinants
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 Camp Lecture 4: Linear Algebra. Xiao Yu Wang. Aug 2010 MIT. Xiao Yu Wang (MIT) Math Camp /10 1 / 88
Math Camp 2010 Lecture 4: Linear Algebra Xiao Yu Wang MIT Aug 2010 Xiao Yu Wang (MIT) Math Camp 2010 08/10 1 / 88 Linear Algebra Game Plan Vector Spaces Linear Transformations and Matrices Determinant
More informationECON 4117/5111 Mathematical Economics
Test 1 September 29, 2006 1. Use a truth table to show the following equivalence statement: (p q) (p q) 2. Consider the statement: A function f : X Y is continuous on X if for every open set V Y, the pre-image
More informationSAMPLE OF THE STUDY MATERIAL PART OF CHAPTER 1 Introduction to Linear Algebra
SAMPLE OF THE STUDY MATERIAL PART OF CHAPTER 1 Introduction to 1.1. Introduction Linear algebra is a specific branch of mathematics dealing with the study of vectors, vector spaces with functions that
More informationMATH36001 Generalized Inverses and the SVD 2015
MATH36001 Generalized Inverses and the SVD 201 1 Generalized Inverses of Matrices A matrix has an inverse only if it is square and nonsingular. However there are theoretical and practical applications
More informationNotes on Linear Algebra
1 Notes on Linear Algebra Jean Walrand August 2005 I INTRODUCTION Linear Algebra is the theory of linear transformations Applications abound in estimation control and Markov chains You should be familiar
More informationErrata and Updates for ASM Exam LTAM (First Edition Second Printing) Sorted by Page
Errata for ASM Exam LTAM Study Manual (First Edition Second Printing) Sorted by Page Errata and Updates for ASM Exam LTAM (First Edition Second Printing) Sorted by Page Practice Exam 0:A7, 2:A7, and 2:A8
More informationErrata and Updates for ASM Exam LTAM (First Edition Second Printing) Sorted by Date
Errata for ASM Exam LTAM Study Manual (First Edition Second Printing) Sorted by Date Errata and Updates for ASM Exam LTAM (First Edition Second Printing) Sorted by Date Practice Exam 0:A7, 2:A7, and 2:A8
More informationSTAT200C: Review of Linear Algebra
Stat200C Instructor: Zhaoxia Yu STAT200C: Review of Linear Algebra 1 Review of Linear Algebra 1.1 Vector Spaces, Rank, Trace, and Linear Equations 1.1.1 Rank and Vector Spaces Definition A vector whose
More informationThe Maximum Dimension of a Subspace of Nilpotent Matrices of Index 2
The Maximum Dimension of a Subspace of Nilpotent Matrices of Index L G Sweet Department of Mathematics and Statistics, University of Prince Edward Island Charlottetown, PEI C1A4P, Canada sweet@upeica J
More informationReview 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 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 informationELEMENTARY LINEAR ALGEBRA WITH APPLICATIONS. 1. Linear Equations and Matrices
ELEMENTARY LINEAR ALGEBRA WITH APPLICATIONS KOLMAN & HILL NOTES BY OTTO MUTZBAUER 11 Systems of Linear Equations 1 Linear Equations and Matrices Numbers in our context are either real numbers or complex
More informationG1110 & 852G1 Numerical Linear Algebra
The University of Sussex Department of Mathematics G & 85G Numerical Linear Algebra Lecture Notes Autumn Term Kerstin Hesse (w aw S w a w w (w aw H(wa = (w aw + w Figure : Geometric explanation of the
More informationAppendix A: Matrices
Appendix A: Matrices A matrix is a rectangular array of numbers Such arrays have rows and columns The numbers of rows and columns are referred to as the dimensions of a matrix A matrix with, say, 5 rows
More informationDigital Workbook for GRA 6035 Mathematics
Eivind Eriksen Digital Workbook for GRA 6035 Mathematics November 10, 2014 BI Norwegian Business School Contents Part I Lectures in GRA6035 Mathematics 1 Linear Systems and Gaussian Elimination........................
More informationThis MUST hold matrix multiplication satisfies the distributive property.
The columns of AB are combinations of the columns of A. The reason is that each column of AB equals A times the corresponding column of B. But that is a linear combination of the columns of A with coefficients
More informationFirst selection test
First selection test Problem 1. Let ABC be a triangle right at C and consider points D, E on the sides BC, CA, respectively such that BD = AE = k. Lines BE and AC CD AD intersect at point O. Show that
More informationLinear Algebra. Christos Michalopoulos. September 24, NTU, Department of Economics
Linear Algebra Christos Michalopoulos NTU, Department of Economics September 24, 2011 Christos Michalopoulos Linear Algebra September 24, 2011 1 / 93 Linear Equations Denition A linear equation in n-variables
More informationGROUPS. Chapter-1 EXAMPLES 1.1. INTRODUCTION 1.2. BINARY OPERATION
Chapter-1 GROUPS 1.1. INTRODUCTION The theory of groups arose from the theory of equations, during the nineteenth century. Originally, groups consisted only of transformations. The group of transformations
More informationMath 10 - Unit 5 Final Review - Polynomials
Class: Date: Math 10 - Unit 5 Final Review - Polynomials Multiple Choice Identify the choice that best completes the statement or answers the question. 1. Factor the binomial 44a + 99a 2. a. a(44 + 99a)
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 informationNonlinear Optimization
Nonlinear Optimization (Com S 477/577 Notes) Yan-Bin Jia Nov 7, 2017 1 Introduction Given a single function f that depends on one or more independent variable, we want to find the values of those variables
More informationKnowledge Discovery and Data Mining 1 (VO) ( )
Knowledge Discovery and Data Mining 1 (VO) (707.003) Review of Linear Algebra Denis Helic KTI, TU Graz Oct 9, 2014 Denis Helic (KTI, TU Graz) KDDM1 Oct 9, 2014 1 / 74 Big picture: KDDM Probability Theory
More informationCHAPTER 8: Matrices and Determinants
(Exercises for Chapter 8: Matrices and Determinants) E.8.1 CHAPTER 8: Matrices and Determinants (A) means refer to Part A, (B) means refer to Part B, etc. Most of these exercises can be done without a
More information