r (t) = 2r(t) + sin t θ (t) = r(t) θ(t) + 1 = 1 1 θ(t) Write the given system in matrix form x = Ax + f ( ) sin(t) x y z = dy cos(t)


 Maryann Garrison
 2 years ago
 Views:
Transcription
1 Solutions HW Write the given system in matrix form x = Ax + f r (t) = 2r(t) + sin t θ (t) = r(t) θ(t) + We write this as ( ) r (t) θ (t) = ( ) ( ) 2 r(t) θ(t) + ( ) sin(t) Write the given system in matrix form x = Ax + f dx = x + y + z = 2x y + z dz = x + 5z dy We write this as dx dy dz = 2 x y 5 z Rewrite d y dy + y = cos(t) as a first order system in normal form. Note that the equation says that d y = dy y + cos(t). Setting x = y, x 2 = dy, x = d2 y, (so d y 2 = x 2 x + cos(t)) we get x x 2 = x = x x 2 x dy d 2 y 2 d y + = cos(t) x 2 x x 2 x + cos(t)
2 9.4. Write the given system as a set of scalar equations x = This becomes the equations ( ) ( ) 2 t x + e t x = 2x + x 2 + te t x 2 = x + x 2 + e t Determine whether the given vector functions are linearly dependent or independent on the interval (, ) ( ) ( ) sin t sin 2t, cos t cos 2t We compute the Wronskian ( ) sin t sin 2t det = sin t cos 2t sin 2t cos t = sin t cos t cos 2t where the last step can be deduced by using trig identities. Since sin t is not identically, the vector functions are linearly independent. (Alternatively, one can check that the Wronksian is nonzero at a point such as t = π 2.) Determine whether the given vector functions are linearly dependent or independent on the interval (, ), t t, t2 t 2 These functions are linearly independent, since a linear relations requires finding nonzero constants c, c 2, c such that c + c 2 t + c t 2 =. But, t, t 2 are linearly independent, so no such constants exist. Note that even though the vector functions are linearly independent, their Wronksian is still zero Determine whether the given functions form a fundamental solution set to an equation x (t) = Ax. If they do, find a fundamental matrix for the system and give a general solution.
3 x = et e t, x 2 = e t sin t cos t sin t We start by computing the Wronksian, x = cos t sin t cos t e t sin t cos t det e cos t sin t = e t (cos 2 t+sin 2 t) e t (sin t cos t sin t cos t)+e t (sin 2 t+cos 2 t) = 2e t e t sin t cos t Since this is nowhere, the solutions are linearly independent and form a fundamental set. A fundamental matrix is e t sin t cos t e cos t sin t e t sin t cos t and a general solution is c x + c 2 x 2 + c x Verify that the vector functions e t x =, x 2 = e t e t e t are solutions to the homogenous system on (, ) and that, x = 2 2 x = Ax = 2 2 x, 2 2 e t e t e t is a particular solution to 5t + x p = 2t 4t + 2 9t x = Ax + 8t = Ax + f(t)
4 Find a general solution to x = Ax + f(t). We check directly that e t e t x = = Ax e t x 2 = e t = Ax 2 e t x = e t = Ax e t 5 x p = 2 = Ax p + f(t) 4 A general solution to x = Ax + f(t) is c x + c 2 x 2 + c x + x p Prove that the operator L[x] = x Ax is a linear operator. We must show L[x + y] = L[x] + L[y] and L[cx] = cl[x]. L[x + y] = (x + y) A(x + y) = x + y Ax Ay = (x Ax) + (y Ay) = L[x] + L[y] L[cx] = (cx) A(cx) = cx cax = c(x Ax) = cl[x] Let X(t) be a fundamental matrix for the system x = Ax. Show that x(t) = X(t)X (t )x is the solution to the initial value problem x = Ax, x(t o ) = x. Since x(t) is a linear combination of the columns of the fundamental matrix, we just need to check that it satisfies the initial conditions. But x(t ) = X(t )X (t )x = Ix = x as desired, so x(t) is the dersired solutions Find eigenvalues and eigenvectors of the matrix We start by computing the characteristic polynomial.
5 det λ λ = λ + λ + 2 = (2 λ)( + λ) 2 λ So the eigenvalues are 2 and . For λ = we must find the kernel of Row reducing we get which gives eigenvectors, For λ = 2 we must find the kernel of Row reducing we get which gives the eigenvector Find all eigenvalues and eigenvectors of
6 2 We start by computing the characteristic polynomial. λ 2 det λ = ( λ)(λ 2 2λ + 2) λ The first factor gives eigenvalue, the second gives eigenvalues ± i. For λ =, we must find the kernel of which gives the eigenvector 2 det For λ = i we must find the kernel of Solving this we get the eigenvector i 2 det i i 2 + i i Taking conjugates, we get that the eigenvector for λ = + i is 2 i i Find a general solution to the equation x = Ax where
7 A = 2 We start by computing the characteristic polynomial. λ det 2 λ = (λ 7λ 6) = (λ + )(λ + 2)(λ ) λ So the eigenvalues are, 2,. For λ =, we must find the kernel of Row reducing we get which gives the eigenvector For λ = 2, we must find the kernel of Row reducing we get which gives the eigenvector 4
8 For λ =, we must find the kernel of Row reducing we get which gives the eigenvector is 4 Combining these, we get that the general solution to the differential equation c e t + c 2 e 2t + c e t Find a fundamental matrix for the system x =Ax, where A = ( 5 ) 4 The characteristic polynomial of A is λ 2 5λ + 4 = (λ )(λ 4), so the eigenvalues are λ =, 4. For λ = we must find the kernel of ( ) which is spanned by ( )
9 For λ = 4 we must find the kernel of ( ) 4 which is spanned by 4 ( ) 4 The corresponding fundamental matrix is ( ) e t 4e 4t e t e 4t Find a general solution to the system of equations x = x 4y y = 4x 7y This system can be rewritten as x = Ax, where A = ( ) The characteristic polynomial is λ 2 + 4λ 5 = (λ )(λ + 5), so the eigenvalues are and 5. For λ = we must find the kernel of which is spanned by ( ) ( ) 2 For λ = 5 we must find the kernel of which is spanned by ( )
10 ( ) 2 Combining these, we see the general solution to the initial system is x = 2c e t + c 2 e 5t, y = c e t + 2c 2 e 5t Solve the initial value problem x (t) = x, x() = 4 From the eigenvectors and eigenvalues from problem 6, the general solution to this equation is x(t) = c e t + c 2 e t + c e 2t Plugging in the initial condition, we must solve the equations c c 2 = 4 c Row reducing the system and backsolving gives, c =, c 2 =, c =, so the desired solution is x(t) = e t e t + c e 2t a. Show that the matrix A = ( ) 4 has a repeated eigenvalue, and only one eigenvector. The characteristic polynomial is λ 2 +2λ+ = (λ+) 2, so the only eigenvalue is λ =. Searching for eigenvectors, we must find the kernel of ( ) 2 4 2
11 which is spanned by ( 2) b. Use your answer to part a. to find a nontrivial solution to x = Ax. ( ) e t 2 c. Try to find a second solution of the form te t u + e t u 2. Plugging this expression into x = Ax, we get te t u + e t u e t u 2 = te t Au + e t Au 2. Grouping the e t and te t terms together, we get to vector relations u 2 + u = Au 2 or (A + I)u 2 = u and u = Au, or (A + I)u =. We want u to be an eigenvector. To find u 2, we can either solve the given set of linear equations, or just guess a u 2 and see if (A + I)u 2 is an eigenvector. (This may seem ad hoc, but it works as long as your guess for u 2 is not already an eigenvector.) If we guess then ( u 2 = ) (A + I)u = ( ) = ( ) 2 4 which is an eigenvector. So we get a solution of the differential equation e t ( ) + te t ( 2 4 ) d. What is (A + I) 2 u 2? (A + I) 2 u 2 = (A + I)(A + I)u 2 = (A + I)u = from the equations we derived in part c Use the method of problem 5 to find a general solution to the system
12 x (t) = ( ) 5 x(t) Computing the characteristic polynomial, we get that λ = 2 is a double root, and ( v = ) is an eigenvector, so e 2t v is a solution to the differential equation. As in problem 5, we guess a solution of the form te 2t u + e 2t u 2. This gives rise to the equations (A 2I)u =, (A 2I) 2 u 2 = u. Guessing we get ( u 2 = ) ( ) ( ) u = = ( ) which is an eigenvector. So another solution is te 2t u + e 2t u 2. Combining these, we get a general solution ( ) ( ) ( ) c e 2t + c 2 (te 2t + e 2t )
LS.6 Solution Matrices
LS.6 Solution Matrices In the literature, solutions to linear systems often are expressed using square matrices rather than vectors. You need to get used to the terminology. As before, we state the definitions
More informationby the matrix A results in a vector which is a reflection of the given
Eigenvalues & Eigenvectors Example Suppose Then So, geometrically, multiplying a vector in by the matrix A results in a vector which is a reflection of the given vector about the yaxis We observe that
More informationHigher Order Equations
Higher Order Equations We briefly consider how what we have done with order two equations generalizes to higher order linear equations. Fortunately, the generalization is very straightforward: 1. Theory.
More informationMatrix Methods for Linear Systems of Differential Equations
Matrix Methods for Linear Systems of Differential Equations We now present an application of matrix methods to linear systems of differential equations. We shall follow the development given in Chapter
More informationMATH 423 Linear Algebra II Lecture 38: Generalized eigenvectors. Jordan canonical form (continued).
MATH 423 Linear Algebra II Lecture 38: Generalized eigenvectors Jordan canonical form (continued) Jordan canonical form A Jordan block is a square matrix of the form λ 1 0 0 0 0 λ 1 0 0 0 0 λ 0 0 J = 0
More informationEXAM. Practice Questions for Exam #2. Math 3350, Spring April 3, 2004 ANSWERS
EXAM Practice Questions for Exam #2 Math 3350, Spring 2004 April 3, 2004 ANSWERS i Problem 1. Find the general solution. A. D 3 (D 2)(D 3) 2 y = 0. The characteristic polynomial is λ 3 (λ 2)(λ 3) 2. Thus,
More informationSimilarity and Diagonalization. Similar Matrices
MATH022 Linear Algebra Brief lecture notes 48 Similarity and Diagonalization Similar Matrices Let A and B be n n matrices. We say that A is similar to B if there is an invertible n n matrix P such that
More informationDiagonalisation. Chapter 3. Introduction. Eigenvalues and eigenvectors. Reading. Definitions
Chapter 3 Diagonalisation Eigenvalues and eigenvectors, diagonalisation of a matrix, orthogonal diagonalisation fo symmetric matrices Reading As in the previous chapter, there is no specific essential
More informationSection 1.7 22 Continued
Section 1.5 23 A homogeneous equation is always consistent. TRUE  The trivial solution is always a solution. The equation Ax = 0 gives an explicit descriptions of its solution set. FALSE  The equation
More informationExamination paper for TMA4115 Matematikk 3
Department of Mathematical Sciences Examination paper for TMA45 Matematikk 3 Academic contact during examination: Antoine Julien a, Alexander Schmeding b, Gereon Quick c Phone: a 73 59 77 82, b 40 53 99
More informationComplex Eigenvalues. 1 Complex Eigenvalues
Complex Eigenvalues Today we consider how to deal with complex eigenvalues in a linear homogeneous system of first der equations We will also look back briefly at how what we have done with systems recapitulates
More informationChapter 20. Vector Spaces and Bases
Chapter 20. Vector Spaces and Bases In this course, we have proceeded stepbystep through lowdimensional Linear Algebra. We have looked at lines, planes, hyperplanes, and have seen that there is no limit
More informationSolutions to Linear Algebra Practice Problems
Solutions to Linear Algebra Practice Problems. Find all solutions to the following systems of linear equations. (a) x x + x 5 x x x + x + x 5 (b) x + x + x x + x + x x + x + 8x Answer: (a) We create the
More informationHomogeneous systems of algebraic equations. A homogeneous (homogeen ius) system of linear algebraic equations is one in which
Homogeneous systems of algebraic equations A homogeneous (homogeen ius) system of linear algebraic equations is one in which all the numbers on the right hand side are equal to : a x + + a n x n = a
More informationAu = = = 3u. Aw = = = 2w. so the action of A on u and w is very easy to picture: it simply amounts to a stretching by 3 and 2, respectively.
Chapter 7 Eigenvalues and Eigenvectors In this last chapter of our exploration of Linear Algebra we will revisit eigenvalues and eigenvectors of matrices, concepts that were already introduced in Geometry
More information1 Eigenvalues and Eigenvectors
Math 20 Chapter 5 Eigenvalues and Eigenvectors Eigenvalues and Eigenvectors. Definition: A scalar λ is called an eigenvalue of the n n matrix A is there is a nontrivial solution x of Ax = λx. Such an x
More informationChapter 17. Orthogonal Matrices and Symmetries of Space
Chapter 17. Orthogonal Matrices and Symmetries of Space Take a random matrix, say 1 3 A = 4 5 6, 7 8 9 and compare the lengths of e 1 and Ae 1. The vector e 1 has length 1, while Ae 1 = (1, 4, 7) has length
More informationA Second Course in Elementary Differential Equations: Problems and Solutions. Marcel B. Finan Arkansas Tech University c All Rights Reserved
A Second Course in Elementary Differential Equations: Problems and Solutions Marcel B. Finan Arkansas Tech University c All Rights Reserved Contents 8 Calculus of MatrixValued Functions of a Real Variable
More informationBrief Introduction to Vectors and Matrices
CHAPTER 1 Brief Introduction to Vectors and Matrices In this chapter, we will discuss some needed concepts found in introductory course in linear algebra. We will introduce matrix, vector, vectorvalued
More informationSection 2.1. Section 2.2. Exercise 6: We have to compute the product AB in two ways, where , B =. 2 1 3 5 A =
Section 2.1 Exercise 6: We have to compute the product AB in two ways, where 4 2 A = 3 0 1 3, B =. 2 1 3 5 Solution 1. Let b 1 = (1, 2) and b 2 = (3, 1) be the columns of B. Then Ab 1 = (0, 3, 13) and
More informationLinearly Independent Sets and Linearly Dependent Sets
These notes closely follow the presentation of the material given in David C. Lay s textbook Linear Algebra and its Applications (3rd edition). These notes are intended primarily for inclass presentation
More informationSystem of First Order Differential Equations
CHAPTER System of First Order Differential Equations In this chapter, we will discuss system of first order differential equations. There are many applications that involving find several unknown functions
More informationLinear Transformations
a Calculus III Summer 2013, Session II Tuesday, July 23, 2013 Agenda a 1. Linear transformations 2. 3. a linear transformation linear transformations a In the m n linear system Ax = 0, Motivation we can
More informationHigher Order Linear Differential Equations with Constant Coefficients
Higher Order Linear Differential Equations with Constant Coefficients Part I. Homogeneous Equations: Characteristic Roots Objectives: Solve nth order homogeneous linear equations where a n,, a 1, a 0
More informationLINEAR ALGEBRA W W L CHEN
LINEAR ALGEBRA W W L CHEN c W W L Chen, 1997, 2008 This chapter is available free to all individuals, on understanding that it is not to be used for financial gain, and may be downloaded and/or photocopied,
More informationSummary of week 8 (Lectures 22, 23 and 24)
WEEK 8 Summary of week 8 (Lectures 22, 23 and 24) This week we completed our discussion of Chapter 5 of [VST] Recall that if V and W are inner product spaces then a linear map T : V W is called an isometry
More informationMath 2280  Assignment 6
Math 2280  Assignment 6 Dylan Zwick Spring 2014 Section 3.81, 3, 5, 8, 13 Section 4.11, 2, 13, 15, 22 Section 4.21, 10, 19, 28 1 Section 3.8  Endpoint Problems and Eigenvalues 3.8.1 For the eigenvalue
More informationPresentation 3: Eigenvalues and Eigenvectors of a Matrix
Colleen Kirksey, Beth Van Schoyck, Dennis Bowers MATH 280: Problem Solving November 18, 2011 Presentation 3: Eigenvalues and Eigenvectors of a Matrix Order of Presentation: 1. Definitions of Eigenvalues
More informationMATH 551  APPLIED MATRIX THEORY
MATH 55  APPLIED MATRIX THEORY FINAL TEST: SAMPLE with SOLUTIONS (25 points NAME: PROBLEM (3 points A web of 5 pages is described by a directed graph whose matrix is given by A Do the following ( points
More informationx + y + z = 1 2x + 3y + 4z = 0 5x + 6y + 7z = 3
Math 24 FINAL EXAM (2/9/9  SOLUTIONS ( Find the general solution to the system of equations 2 4 5 6 7 ( r 2 2r r 2 r 5r r x + y + z 2x + y + 4z 5x + 6y + 7z 2 2 2 2 So x z + y 2z 2 and z is free. ( r
More informationLinear algebra and the geometry of quadratic equations. Similarity transformations and orthogonal matrices
MATH 30 Differential Equations Spring 006 Linear algebra and the geometry of quadratic equations Similarity transformations and orthogonal matrices First, some things to recall from linear algebra Two
More information22 Matrix exponent. Equal eigenvalues
22 Matrix exponent. Equal eigenvalues 22. Matrix exponent Consider a first order differential equation of the form y = ay, a R, with the initial condition y) = y. Of course, we know that the solution to
More information4. Matrix inverses. left and right inverse. linear independence. nonsingular matrices. matrices with linearly independent columns
L. Vandenberghe EE133A (Spring 2016) 4. Matrix inverses left and right inverse linear independence nonsingular matrices matrices with linearly independent columns matrices with linearly independent rows
More informationMatrices in Statics and Mechanics
Matrices in Statics and Mechanics Casey Pearson 3/19/2012 Abstract The goal of this project is to show how linear algebra can be used to solve complex, multivariable statics problems as well as illustrate
More informationC 1 x(t) = e ta C = e C n. 2! A2 + t3
Matrix Exponential Fundamental Matrix Solution Objective: Solve dt A x with an n n constant coefficient matrix A x (t) Here the unknown is the vector function x(t) x n (t) General Solution Formula in Matrix
More information[1] Diagonal factorization
8.03 LA.6: Diagonalization and Orthogonal Matrices [ Diagonal factorization [2 Solving systems of first order differential equations [3 Symmetric and Orthonormal Matrices [ Diagonal factorization Recall:
More informationThese axioms must hold for all vectors ū, v, and w in V and all scalars c and d.
DEFINITION: A vector space is a nonempty set V of objects, called vectors, on which are defined two operations, called addition and multiplication by scalars (real numbers), subject to the following axioms
More information4.5 Linear Dependence and Linear Independence
4.5 Linear Dependence and Linear Independence 267 32. {v 1, v 2 }, where v 1, v 2 are collinear vectors in R 3. 33. Prove that if S and S are subsets of a vector space V such that S is a subset of S, then
More informationRecall the basic property of the transpose (for any A): v A t Aw = v w, v, w R n.
ORTHOGONAL MATRICES Informally, an orthogonal n n matrix is the ndimensional analogue of the rotation matrices R θ in R 2. When does a linear transformation of R 3 (or R n ) deserve to be called a rotation?
More information5 Homogeneous systems
5 Homogeneous systems Definition: A homogeneous (homojeen ius) system of linear algebraic equations is one in which all the numbers on the right hand side are equal to : a x +... + a n x n =.. a m
More informationHW6 Solutions. MATH 20D Fall 2013 Prof: Sun Hui TA: Zezhou Zhang (David) November 14, 2013. Checklist: Section 7.8: 1c, 2, 7, 10, [16]
HW6 Solutions MATH D Fall 3 Prof: Sun Hui TA: Zezhou Zhang David November 4, 3 Checklist: Section 7.8: c,, 7,, [6] Section 7.9:, 3, 7, 9 Section 7.8 In Problems 7.8. thru 4: a Draw a direction field and
More information4.6 Null Space, Column Space, Row Space
NULL SPACE, COLUMN SPACE, ROW SPACE Null Space, Column Space, Row Space In applications of linear algebra, subspaces of R n typically arise in one of two situations: ) as the set of solutions of a linear
More informationChapter 6. Orthogonality
6.3 Orthogonal Matrices 1 Chapter 6. Orthogonality 6.3 Orthogonal Matrices Definition 6.4. An n n matrix A is orthogonal if A T A = I. Note. We will see that the columns of an orthogonal matrix must be
More information3.7 Nonautonomous linear systems of ODE. General theory
3.7 Nonautonomous linear systems of ODE. General theory Now I will study the ODE in the form ẋ = A(t)x + g(t), x(t) R k, A, g C(I), (3.1) where now the matrix A is time dependent and continuous on some
More information7  Linear Transformations
7  Linear Transformations Mathematics has as its objects of study sets with various structures. These sets include sets of numbers (such as the integers, rationals, reals, and complexes) whose structure
More informationNotes on Jordan Canonical Form
Notes on Jordan Canonical Form Eric Klavins University of Washington 8 Jordan blocks and Jordan form A Jordan Block of size m and value λ is a matrix J m (λ) having the value λ repeated along the main
More informationLinear Algebra Notes
Linear Algebra Notes Chapter 19 KERNEL AND IMAGE OF A MATRIX Take an n m matrix a 11 a 12 a 1m a 21 a 22 a 2m a n1 a n2 a nm and think of it as a function A : R m R n The kernel of A is defined as Note
More information1 2 3 1 1 2 x = + x 2 + x 4 1 0 1
(d) If the vector b is the sum of the four columns of A, write down the complete solution to Ax = b. 1 2 3 1 1 2 x = + x 2 + x 4 1 0 0 1 0 1 2. (11 points) This problem finds the curve y = C + D 2 t which
More information( ) which must be a vector
MATH 37 Linear Transformations from Rn to Rm Dr. Neal, WKU Let T : R n R m be a function which maps vectors from R n to R m. Then T is called a linear transformation if the following two properties are
More informationInner Product Spaces
Math 571 Inner Product Spaces 1. Preliminaries An inner product space is a vector space V along with a function, called an inner product which associates each pair of vectors u, v with a scalar u, v, and
More informationMAT 242 Test 2 SOLUTIONS, FORM T
MAT 242 Test 2 SOLUTIONS, FORM T 5 3 5 3 3 3 3. Let v =, v 5 2 =, v 3 =, and v 5 4 =. 3 3 7 3 a. [ points] The set { v, v 2, v 3, v 4 } is linearly dependent. Find a nontrivial linear combination of these
More informationCITY UNIVERSITY LONDON. BEng Degree in Computer Systems Engineering Part II BSc Degree in Computer Systems Engineering Part III PART 2 EXAMINATION
No: CITY UNIVERSITY LONDON BEng Degree in Computer Systems Engineering Part II BSc Degree in Computer Systems Engineering Part III PART 2 EXAMINATION ENGINEERING MATHEMATICS 2 (resit) EX2005 Date: August
More informationLectures notes on orthogonal matrices (with exercises) 92.222  Linear Algebra II  Spring 2004 by D. Klain
Lectures notes on orthogonal matrices (with exercises) 92.222  Linear Algebra II  Spring 2004 by D. Klain 1. Orthogonal matrices and orthonormal sets An n n realvalued matrix A is said to be an orthogonal
More information4.1 VECTOR SPACES AND SUBSPACES
4.1 VECTOR SPACES AND SUBSPACES What is a vector space? (pg 229) A vector space is a nonempty set, V, of vectors together with two operations; addition and scalar multiplication which satisfies the following
More informationINTRODUCTORY LINEAR ALGEBRA WITH APPLICATIONS B. KOLMAN, D. R. HILL
SOLUTIONS OF THEORETICAL EXERCISES selected from INTRODUCTORY LINEAR ALGEBRA WITH APPLICATIONS B. KOLMAN, D. R. HILL Eighth Edition, Prentice Hall, 2005. Dr. Grigore CĂLUGĂREANU Department of Mathematics
More informationMath Practice Problems for Test 1
Math 290  Practice Problems for Test 1 UNSUBSTANTIATED ANSWERS MAY NOT RECEIVE CREDIT. 3 4 5 1. Let c 1 and c 2 be the columns of A 5 2 and b 1. Show that b Span{c 1, c 2 } by 6 6 6 writing b as a linear
More informationUniversity of Lille I PC first year list of exercises n 7. Review
University of Lille I PC first year list of exercises n 7 Review Exercise Solve the following systems in 4 different ways (by substitution, by the Gauss method, by inverting the matrix of coefficients
More informationLINEAR ALGEBRA. September 23, 2010
LINEAR ALGEBRA September 3, 00 Contents 0. LUdecomposition.................................... 0. Inverses and Transposes................................. 0.3 Column Spaces and NullSpaces.............................
More informationInner Product Spaces and Orthogonality
Inner Product Spaces and Orthogonality week 34 Fall 2006 Dot product of R n The inner product or dot product of R n is a function, defined by u, v a b + a 2 b 2 + + a n b n for u a, a 2,, a n T, v b,
More information13 MATH FACTS 101. 2 a = 1. 7. The elements of a vector have a graphical interpretation, which is particularly easy to see in two or three dimensions.
3 MATH FACTS 0 3 MATH FACTS 3. Vectors 3.. Definition We use the overhead arrow to denote a column vector, i.e., a linear segment with a direction. For example, in threespace, we write a vector in terms
More information10.3 POWER METHOD FOR APPROXIMATING EIGENVALUES
58 CHAPTER NUMERICAL METHODS. POWER METHOD FOR APPROXIMATING EIGENVALUES In Chapter 7 you saw that the eigenvalues of an n n matrix A are obtained by solving its characteristic equation n c nn c nn...
More informationMA 242 LINEAR ALGEBRA C1, Solutions to Second Midterm Exam
MA 4 LINEAR ALGEBRA C, Solutions to Second Midterm Exam Prof. Nikola Popovic, November 9, 6, 9:3am  :5am Problem (5 points). Let the matrix A be given by 5 6 5 4 5 (a) Find the inverse A of A, if it exists.
More informationMath 115A HW4 Solutions University of California, Los Angeles. 5 2i 6 + 4i. (5 2i)7i (6 + 4i)( 3 + i) = 35i + 14 ( 22 6i) = 36 + 41i.
Math 5A HW4 Solutions September 5, 202 University of California, Los Angeles Problem 4..3b Calculate the determinant, 5 2i 6 + 4i 3 + i 7i Solution: The textbook s instructions give us, (5 2i)7i (6 + 4i)(
More informationCHAPTER 2. Eigenvalue Problems (EVP s) for ODE s
A SERIES OF CLASS NOTES FOR 005006 TO INTRODUCE LINEAR AND NONLINEAR PROBLEMS TO ENGINEERS, SCIENTISTS, AND APPLIED MATHEMATICIANS DE CLASS NOTES 4 A COLLECTION OF HANDOUTS ON PARTIAL DIFFERENTIAL EQUATIONS
More informationProblems for Advanced Linear Algebra Fall 2012
Problems for Advanced Linear Algebra Fall 2012 Class will be structured around students presenting complete solutions to the problems in this handout. Please only agree to come to the board when you are
More informationSECONDORDER LINEAR HOMOGENEOUS DIFFERENTIAL EQUATIONS
L SECONDORDER LINEAR HOOGENEOUS DIFFERENTIAL EQUATIONS SECONDORDER LINEAR HOOGENEOUS DIFFERENTIAL EQUATIONS WITH CONSTANT COEFFICIENTS A secondorder linear differential equation is one of the form d
More informationNumerical Analysis Lecture Notes
Numerical Analysis Lecture Notes Peter J. Olver 6. Eigenvalues and Singular Values In this section, we collect together the basic facts about eigenvalues and eigenvectors. From a geometrical viewpoint,
More informationLinear Dependence Tests
Linear Dependence Tests The book omits a few key tests for checking the linear dependence of vectors. These short notes discuss these tests, as well as the reasoning behind them. Our first test checks
More informationVector and Matrix Norms
Chapter 1 Vector and Matrix Norms 11 Vector Spaces Let F be a field (such as the real numbers, R, or complex numbers, C) with elements called scalars A Vector Space, V, over the field F is a nonempty
More information1 Introduction to Matrices
1 Introduction to Matrices In this section, important definitions and results from matrix algebra that are useful in regression analysis are introduced. While all statements below regarding the columns
More informationBindel, Spring 2012 Intro to Scientific Computing (CS 3220) Week 3: Wednesday, Feb 8
Spaces and bases Week 3: Wednesday, Feb 8 I have two favorite vector spaces 1 : R n and the space P d of polynomials of degree at most d. For R n, we have a canonical basis: R n = span{e 1, e 2,..., e
More informationGeneral Theory of Differential Equations Sections 2.8, 3.13.2, 4.1
A B I L E N E C H R I S T I A N U N I V E R S I T Y Department of Mathematics General Theory of Differential Equations Sections 2.8, 3.13.2, 4.1 Dr. John Ehrke Department of Mathematics Fall 2012 Questions
More informationInverses. Stephen Boyd. EE103 Stanford University. October 27, 2015
Inverses Stephen Boyd EE103 Stanford University October 27, 2015 Outline Left and right inverses Inverse Solving linear equations Examples Pseudoinverse Left and right inverses 2 Left inverses a number
More informationThe Method of Partial Fractions Math 121 Calculus II Spring 2015
Rational functions. as The Method of Partial Fractions Math 11 Calculus II Spring 015 Recall that a rational function is a quotient of two polynomials such f(x) g(x) = 3x5 + x 3 + 16x x 60. The method
More informationOrthogonal Diagonalization of Symmetric Matrices
MATH10212 Linear Algebra Brief lecture notes 57 Gram Schmidt Process enables us to find an orthogonal basis of a subspace. Let u 1,..., u k be a basis of a subspace V of R n. We begin the process of finding
More informationMath 2280 Section 002 [SPRING 2013] 1
Math 2280 Section 002 [SPRING 2013] 1 Today well learn about a method for solving systems of differential equations, the method of elimination, that is very similar to the elimination methods we learned
More informationMAT 274 HW 2 Solutions c Bin Cheng. Due 11:59pm, W 9/07, 2011. 80 Points
MAT 274 HW 2 Solutions Due 11:59pm, W 9/07, 2011. 80 oints 1. (30 ) The last two problems of Webwork Set 03 Modeling. Show all the steps and, also, indicate the equilibrium solutions for each problem.
More informationLinear Algebra Notes for Marsden and Tromba Vector Calculus
Linear Algebra Notes for Marsden and Tromba Vector Calculus ndimensional Euclidean Space and Matrices Definition of n space As was learned in Math b, a point in Euclidean three space can be thought of
More informationReview Jeopardy. Blue vs. Orange. Review Jeopardy
Review Jeopardy Blue vs. Orange Review Jeopardy Jeopardy Round Lectures 03 Jeopardy Round $200 How could I measure how far apart (i.e. how different) two observations, y 1 and y 2, are from each other?
More informationMATH10212 Linear Algebra B Homework 7
MATH22 Linear Algebra B Homework 7 Students are strongly advised to acquire a copy of the Textbook: D C Lay, Linear Algebra and its Applications Pearson, 26 (or other editions) Normally, homework assignments
More informationUsing determinants, it is possible to express the solution to a system of equations whose coefficient matrix is invertible:
Cramer s Rule and the Adjugate Using determinants, it is possible to express the solution to a system of equations whose coefficient matrix is invertible: Theorem [Cramer s Rule] If A is an invertible
More informationNON SINGULAR MATRICES. DEFINITION. (Non singular matrix) An n n A is called non singular or invertible if there exists an n n matrix B such that
NON SINGULAR MATRICES DEFINITION. (Non singular matrix) An n n A is called non singular or invertible if there exists an n n matrix B such that AB = I n = BA. Any matrix B with the above property is called
More informationWe call this set an ndimensional parallelogram (with one vertex 0). We also refer to the vectors x 1,..., x n as the edges of P.
Volumes of parallelograms 1 Chapter 8 Volumes of parallelograms In the present short chapter we are going to discuss the elementary geometrical objects which we call parallelograms. These are going to
More information1 Determinants and the Solvability of Linear Systems
1 Determinants and the Solvability of Linear Systems In the last section we learned how to use Gaussian elimination to solve linear systems of n equations in n unknowns The section completely sidestepped
More informationSecond Order Linear Partial Differential Equations. Part I
Second Order Linear Partial Differential Equations Part I Second linear partial differential equations; Separation of Variables;  point boundary value problems; Eigenvalues and Eigenfunctions Introduction
More informationApplied Linear Algebra
Applied Linear Algebra OTTO BRETSCHER http://www.prenhall.com/bretscher Chapter 7 Eigenvalues and Eigenvectors ChiaHui Chang Email: chia@csie.ncu.edu.tw National Central University, Taiwan 7.1 DYNAMICAL
More informationMATH 240 Fall, Chapter 1: Linear Equations and Matrices
MATH 240 Fall, 2007 Chapter Summaries for Kolman / Hill, Elementary Linear Algebra, 9th Ed. written by Prof. J. Beachy Sections 1.1 1.5, 2.1 2.3, 4.2 4.9, 3.1 3.5, 5.3 5.5, 6.1 6.3, 6.5, 7.1 7.3 DEFINITIONS
More informationSolutions to Math 51 First Exam January 29, 2015
Solutions to Math 5 First Exam January 29, 25. ( points) (a) Complete the following sentence: A set of vectors {v,..., v k } is defined to be linearly dependent if (2 points) there exist c,... c k R, not
More informationAdding vectors We can do arithmetic with vectors. We ll start with vector addition and related operations. Suppose you have two vectors
1 Chapter 13. VECTORS IN THREE DIMENSIONAL SPACE Let s begin with some names and notation for things: R is the set (collection) of real numbers. We write x R to mean that x is a real number. A real number
More informationWe know a formula for and some properties of the determinant. Now we see how the determinant can be used.
Cramer s rule, inverse matrix, and volume We know a formula for and some properties of the determinant. Now we see how the determinant can be used. Formula for A We know: a b d b =. c d ad bc c a Can we
More informationEigenvalues and Eigenvectors
Chapter 6 Eigenvalues and Eigenvectors 6. Introduction to Eigenvalues Linear equations Ax D b come from steady state problems. Eigenvalues have their greatest importance in dynamic problems. The solution
More informationA Brief Review of Elementary Ordinary Differential Equations
1 A Brief Review of Elementary Ordinary Differential Equations At various points in the material we will be covering, we will need to recall and use material normally covered in an elementary course on
More informationNotes from FE Review for mathematics, University of Kentucky
Notes from FE Review for mathematics, University of Kentucky Dr. Alan Demlow March 19, 2012 Introduction These notes are based on reviews for the mathematics portion of the Fundamentals of Engineering
More informationMathematics Course 111: Algebra I Part IV: Vector Spaces
Mathematics Course 111: Algebra I Part IV: Vector Spaces D. R. Wilkins Academic Year 19967 9 Vector Spaces A vector space over some field K is an algebraic structure consisting of a set V on which are
More information9.3 Advanced Topics in Linear Algebra
548 93 Advanced Topics in Linear Algebra Diagonalization and Jordan s Theorem A system of differential equations x = Ax can be transformed to an uncoupled system y = diag(λ,, λ n y by a change of variables
More informationSergei Silvestrov, Christopher Engström, Karl Lundengård, Johan Richter, Jonas Österberg. November 13, 2014
Sergei Silvestrov,, Karl Lundengård, Johan Richter, Jonas Österberg November 13, 2014 Analysis Todays lecture: Course overview. Repetition of matrices elementary operations. Repetition of solvability of
More informationCHARACTERISTIC ROOTS AND VECTORS
CHARACTERISTIC ROOTS AND VECTORS 1 DEFINITION OF CHARACTERISTIC ROOTS AND VECTORS 11 Statement of the characteristic root problem Find values of a scalar λ for which there exist vectors x 0 satisfying
More informationSECONDORDER LINEAR DIFFERENTIAL EQUATIONS
SECONDORDER LINEAR DIFFERENTIAL EQUATIONS A secondorder linear differential equation has the form 1 Px d y dx dy Qx dx Rxy Gx where P, Q, R, and G are continuous functions. Equations of this type arise
More informationSection 5.4 (Systems of Linear Differential Equation); Eigenvalues and Eigenvectors
Section 5.4 (Systems of Linear Differential Equation); Eigenvalues and Eigenvectors July 1, 2009 Today s Session Today s Session A Summary of This Session: Today s Session A Summary of This Session: (1)
More informationMATH 304 Linear Algebra Lecture 18: Rank and nullity of a matrix.
MATH 304 Linear Algebra Lecture 18: Rank and nullity of a matrix. Nullspace Let A = (a ij ) be an m n matrix. Definition. The nullspace of the matrix A, denoted N(A), is the set of all ndimensional column
More information