Section Let A =. Then A has characteristic equation λ. 2 4λ + 3 = 0 or (λ 3)(λ 1) = 0. Hence the eigenvalues of A are λ 1 = 3 and λ 2 = 1.

Similar documents
Eigenvalues and Eigenvectors

1 Last time: similar and diagonalizable matrices

SOLUTION SET VI FOR FALL [(n + 2)(n + 1)a n+2 a n 1 ]x n = 0,

Symmetric Matrices and Quadratic Forms

We are mainly going to be concerned with power series in x, such as. (x)} converges - that is, lims N n

Application of Jordan Canonical Form

Where do eigenvalues/eigenvectors/eigenfunctions come from, and why are they important anyway?

ECE-S352 Introduction to Digital Signal Processing Lecture 3A Direct Solution of Difference Equations

1 1 2 = show that: over variables x and y. [2 marks] Write down necessary conditions involving first and second-order partial derivatives for ( x0, y

Topics in Eigen-analysis

NICK DUFRESNE. 1 1 p(x). To determine some formulas for the generating function of the Schröder numbers, r(x) = a(x) =

SOLVING LINEAR RECURSIONS OVER ALL FIELDS

Chapter Vectors

Subject: Differential Equations & Mathematical Modeling-III

For a 3 3 diagonal matrix we find. Thus e 1 is a eigenvector corresponding to eigenvalue λ = a 11. Thus matrix A has eigenvalues 2 and 3.

Complex Analysis Spring 2001 Homework I Solution

IIT JAM Mathematical Statistics (MS) 2006 SECTION A

Lecture 8: October 20, Applications of SVD: least squares approximation

Polynomials with Rational Roots that Differ by a Non-zero Constant. Generalities

LECTURE 8: ORTHOGONALITY (CHAPTER 5 IN THE BOOK)

M 340L CS Homew ork Set 6 Solutions

M 340L CS Homew ork Set 6 Solutions

Apply change-of-basis formula to rewrite x as a linear combination of eigenvectors v j.

CHAPTER 5. Theory and Solution Using Matrix Techniques

PHYSICS 116A Homework 2 Solutions

MH1101 AY1617 Sem 2. Question 1. NOT TESTED THIS TIME

Lemma Let f(x) K[x] be a separable polynomial of degree n. Then the Galois group is a subgroup of S n, the permutations of the roots.

MATH 31B: MIDTERM 2 REVIEW

Z - Transform. It offers the techniques for digital filter design and frequency analysis of digital signals.

Subject: Differential Equations & Mathematical Modeling -III. Lesson: Power series solutions of Differential Equations. about ordinary points

De Moivre s Theorem - ALL

PAPER : IIT-JAM 2010

PUTNAM TRAINING, 2008 COMPLEX NUMBERS

The Jordan Normal Form: A General Approach to Solving Homogeneous Linear Systems. Mike Raugh. March 20, 2005

5.1. The Rayleigh s quotient. Definition 49. Let A = A be a self-adjoint matrix. quotient is the function. R(x) = x,ax, for x = 0.

BESSEL EQUATION and BESSEL FUNCTIONS

Solutions for Math 411 Assignment #2 1

Appendix: The Laplace Transform

Complex Numbers Solutions

Math 778S Spectral Graph Theory Handout #3: Eigenvalues of Adjacency Matrix

In algebra one spends much time finding common denominators and thus simplifying rational expressions. For example:

Gernot Hoffmann. Faddejev Algorithm for Eigenvalues and Eigenvectors

Math 220B Final Exam Solutions March 18, 2002

Linear Differential Equations of Higher Order Basic Theory: Initial-Value Problems d y d y dy

Honors Calculus Homework 13 Solutions, due 12/8/5

18.01 Calculus Jason Starr Fall 2005

Notes The Incremental Motion Model:

62. Power series Definition 16. (Power series) Given a sequence {c n }, the series. c n x n = c 0 + c 1 x + c 2 x 2 + c 3 x 3 +

ECE Spring Prof. David R. Jackson ECE Dept. Notes 20

Iterative Techniques for Solving Ax b -(3.8). Assume that the system has a unique solution. Let x be the solution. Then x A 1 b.

Mathematics 3 Outcome 1. Vectors (9/10 pers) Lesson, Outline, Approach etc. This is page number 13. produced for TeeJay Publishers by Tom Strang

Assignment 1 : Real Numbers, Sequences. for n 1. Show that (x n ) converges. Further, by observing that x n+2 + x n+1

Complex numbers 1D. 1 a. = cos + C cos θ(isin θ) + + cos θ(isin θ) (isin θ) Hence, Equating the imaginary parts gives, 2 3

Assignment 2 Solutions SOLUTION. ϕ 1 Â = 3 ϕ 1 4i ϕ 2. The other case can be dealt with in a similar way. { ϕ 2 Â} χ = { 4i ϕ 1 3 ϕ 2 } χ.

MATH : Matrices & Linear Algebra Spring Final Review

The Discrete Fourier Transform

State Space Representation

3. Z Transform. Recall that the Fourier transform (FT) of a DT signal xn [ ] is ( ) [ ] = In order for the FT to exist in the finite magnitude sense,

B U Department of Mathematics Math 101 Calculus I

EXAM-3 MATH 261: Elementary Differential Equations MATH 261 FALL 2006 EXAMINATION COVER PAGE Professor Moseley

MATH10212 Linear Algebra B Proof Problems

Recurrence Relations

On Nonsingularity of Saddle Point Matrices. with Vectors of Ones

MATH 10550, EXAM 3 SOLUTIONS

IYGB. Special Extension Paper E. Time: 3 hours 30 minutes. Created by T. Madas. Created by T. Madas

Properties and Tests of Zeros of Polynomial Functions

Time-Domain Representations of LTI Systems

4 The Sperner property.

MATH 6101 Fall 2008 Newton and Differential Equations

It is often useful to approximate complicated functions using simpler ones. We consider the task of approximating a function by a polynomial.

PUTNAM TRAINING INEQUALITIES

TEACHER CERTIFICATION STUDY GUIDE

Presentation of complex number in Cartesian and polar coordinate system

MATH2007* Partial Answers to Review Exercises Fall 2004

Ma 530 Introduction to Power Series

Math 210A Homework 1

Matrix Algebra 2.2 THE INVERSE OF A MATRIX Pearson Education, Inc.

Homework Set #3 - Solutions

Further Methods for Advanced Mathematics (FP2) WEDNESDAY 9 JANUARY 2008

MIDTERM 3 CALCULUS 2. Monday, December 3, :15 PM to 6:45 PM. Name PRACTICE EXAM SOLUTIONS

After the completion of this section the student should recall

Inverse Matrix. A meaning that matrix B is an inverse of matrix A.

8. Applications To Linear Differential Equations

Maths /2014. CCP Maths 2. Reduction, projector,endomorphism of rank 1... Hadamard s inequality and some applications. Solution.

Stochastic Matrices in a Finite Field

, then cv V. Differential Equations Elements of Lineaer Algebra Name: Consider the differential equation. and y2 cos( kx)

Assignment Number 3 Solutions

M A T H F A L L CORRECTION. Algebra I 1 4 / 1 0 / U N I V E R S I T Y O F T O R O N T O

Numerical Methods for Partial Differential Equations

( ) (( ) ) ANSWERS TO EXERCISES IN APPENDIX B. Section B.1 VECTORS AND SETS. Exercise B.1-1: Convex sets. are convex, , hence. and. (a) Let.


[ 11 ] z of degree 2 as both degree 2 each. The degree of a polynomial in n variables is the maximum of the degrees of its terms.

where c is a scaling constant, 0, 0,. r c sinh cos csinh cos cos, csinh cos sin, ccosh sin U csinh sin sin, csinh sin cos,0

Fourier Series and their Applications

Some remarks for codes and lattices over imaginary quadratic

SOLVED EXAMPLES

UNIVERSITY OF CALIFORNIA - SANTA CRUZ DEPARTMENT OF PHYSICS PHYS 116C. Problem Set 4. Benjamin Stahl. November 6, 2014

SECTION 2.6 THE SECOND ALTERNATIVE

Matrix Algebra 2.3 CHARACTERIZATIONS OF INVERTIBLE MATRICES Pearson Education, Inc.

A Note on the Symmetric Powers of the Standard Representation of S n

Transcription:

Sectio 63 4 3 Let A The A has characteristic equatio λ 2 4λ + 3 or (λ 3)(λ ) Hece the eigevalues of A are λ 3 ad λ 2 λ 3 The correspodig eigevectors satisfy (A λ I 2 )X, or 3 3 or equivaletly x 3y Hece x 3y y y ad we take X 3, 3 y Similarly for λ 2 we fid the eigevector X 2 3 Hece if P X X 2 P AP, the P is o sigular ad 3 Hece A P 3 P ad cosequetly A 3 P P 3 3 2 3 3 + 2 3 3 3 + 3 + + 3 2 3 3 + 3 3 A + 3 3 I 2 2 2 65

3/5 4/5 2 Let A 2/5 /5 λ 2 /5, with correspodig eigevectors 2 X ad X 2 The if P X X 2, P is o sigular ad P AP ad A P /5 The we fid that the eigevalues are λ ad /5 P Hece A P ( /5) P P P 2 3 2 3 2 2 2 2/3 2/3 3 /3 /3 2 3 The give system of differetial equatios is equivalet to Ẋ AX, where 3 2 x A ad X 5 4 y 2 The matrix P is a o-sigular matrix of eigevectors correspodig to eigevalues λ 2 ad λ 2 The 5 P 2 AP The substitutio X P Y, where Y x, y t, gives Ẏ 2 Y, 66

or equivaletly x 2x ad y y Hece x x ()e 2t ad y y ()e t To determie x () ad y (), we ote that x () P x() 3 3 y () y() 3 5 2 22 7 Hece x 3e 2t ad y 7e t Cosequetly x 2x + y 6e 2t + 7e t ad y 5x + y 5e 2t + 7e t 4 Itroducig the vector X x y, the system of recurrece relatios x + 3x y y + x + 3y, 3 becomes X + AX, where A Hece X 3 A X, where X 2 To fid A we ca use the eigevalue method We get A 2 + 4 2 4 2 2 4 2 + 4 Hece 2 + 4 2 4 X 2 2 4 2 + 4 2 + 4 + 2(2 4 ) 2 2 2 4 + 2(2 + 4 ) 3 2 4 2 3 2 + 4 (3 2 4 )/2 (3 2 + 4 )/2 Hece x 2 (3 2 4 ) ad y 2 (3 2 + 4 ) a b 5 Let A be a real or complex matrix with distict eigevalues c d λ, λ 2 ad correspodig eigevectors X, X 2 Also let P X X 2 (a) The system of recurrece relatios x + ax + by y + cx + dy 67

has the solutio x y ( ) A x λ P P x y λ 2 y λ P λ P x 2 y λ X X 2 α λ 2 β λ X X 2 α λ 2 β λ αx + λ 2 βx 2, where α β P x y (b) I matrix form, the system is Ẋ AX, where X x y X P Y, where Y x, y t The We substitute Ẋ P Ẏ AX A(P Y ), so Ẏ (P λ AP )Y λ 2 Hece x λ x ad y λ 2 y The x y x x ()e λ t ad y y ()e λ 2t But so x () y () x() y() P x () y () P x() y(), α β Cosequetly x () α ad y () β ad x x αe λ t P X y X 2 βe λ 2t y αe λ t X + βe λ 2t X 2 68

a b 6 Let A be a real matrix with o real eigevalues λ a + ib c d ad λ a ib, with correspodig eigevectors X U +iv ad X U iv, where U ad V are real vectors Also let P be the real matrix defied by P U V Fially let a + ib re iθ, where r > ad θ is real (a) As X is a eigevector correspodig to the eigevalue λ, we have AX λx ad hece A(U + iv ) (a + ib)(u + iv ) AU + iav au bv + i(bu + av ) Equatig real ad imagiary parts the gives AU au bv AV bu + av (b) a AP AU V AU AV au bv bu+av U V b b a P a b b a Hece, as P ca be show to be o sigular, P a b AP b a (The fact that P is o sigular is easily proved by showig the colums of P are liearly idepedet: Assume xu + yv, where x ad y are real The we fid (x + iy)(u iv ) + (x iy)(u + iv ) Cosequetly x+iy as U iv ad U +iv are eigevectors correspodig to distict eigevalues a ib ad a + ib ad are hece liearly idepedet Hece x ad y ) (c) The system of recurrece relatios x + ax + by y + cx + dy 69

has solutio x y A x y a b P P x b a y r cos θ r si θ α P r si θ r cos θ β P r cos θ si θ α si θ cos θ β r cos θ si θ α U V si θ cos θ β r α cos θ + β si θ U V α si θ + β cos θ r {(α cos θ + β si θ)u + ( α si θ + β cos θ)v } r {(cos θ)(αu + βv ) + (si θ)(βu αv )} (d) The system of differetial equatios dx dt dy dt ax + by cx + dy is attacked usig the substitutio X P Y, where Y x, y t The Ẏ (P AP )Y, so x y a b b a x y Equatig compoets gives Now let z x + iy The x ax + by y bx + ay ż x + iy (ax + by ) + i( bx + ay ) (a ib)(x + iy ) (a ib)z 7

Hece z z()e (a ib)t x + iy (x () + iy ())e at (cos bt i si bt) Equatig real ad imagiary parts gives Now if we defie α ad β by α β x e at {x () cos bt + y () si bt} y e at {y () cos bt x () si bt} P x() y() we see that α x () ad β y () The x x P y y e U V at (α cos bt + β si bt) e at (β cos bt α si bt), e at {(α cos bt + β si bt)u + (β cos bt α si bt)v } e at {cos bt(αu + βv ) + si bt(βu αv )} a b 7 (The case of repeated eigevalues) Let A ad suppose that c d the characteristic polyomial of A, λ 2 (a + d)λ + (ad bc), has a repeated root α Also assume that A αi 2 (i) λ 2 (a + d)λ + (ad bc) (λ α) 2 Hece a + d 2α ad ad bc α 2 ad λ 2 2αλ + α 2 (a + d) 2 4(ad bc), a 2 + 2ad + d 2 4ad 4bc, a 2 2ad + d 2 + 4bc, (a d) 2 + 4bc 7

(ii) Let B A αi 2 The B 2 (A αi 2 ) 2 A 2 2αA + α 2 I 2 A 2 (a + d)a + (ad bc)i 2, But by problem 3, chapter 24, A 2 (a + d)a + (ad bc)i 2, so B 2 (iii) Now suppose that B The BE or BE 2, as BE i is the i th colum of B Hece BX 2, where X 2 E or X 2 E 2 (iv) Let X BX 2 ad P X X 2 We prove P is o sigular by demostratig that X ad X 2 are liearly idepedet Assume xx + yx 2 The xbx 2 + yx 2 B(xBX 2 + yx 2 ) B xb 2 X 2 + ybx 2 xx 2 + ybx 2 ybx 2 Hece y as BX 2 Hece xbx 2 ad so x Fially, BX B(BX 2 ) B 2 X 2, so (A αi 2 )X ad Also Hece AX αx (2) X BX 2 (A αi 2 )X 2 AX 2 αx 2 The, usig (2) ad (3), we have AX 2 X + αx 2 (3) Hece ad hece AP AX X 2 AX AX 2 AP P αx X + αx 2 α X X 2 α α α 72

P α AP α 8 The system of differetial equatios is equivalet to the sigle matrix 4 equatio Ẋ AX, where A 4 8 The characteristic polyomial of A is λ 2 2λ + 36 (λ 6) 2, so we ca use the previous questio with α 6 Let 2 B A 6I 2 4 2 2 The BX 2 4 P X X 2, we have, if X 2 P AP 6 6 Now make the chage of variables X P Y, where Y Ẏ (P AP )Y Also let X BX 2 The if 6 6 Y, or equivaletly x 6x + y ad y 6y Solvig for y gives y y ()e 6t Cosequetly x 6x + y ()e 6t Multiplyig both side of this equatio by e 6t gives x d dt (e 6t x ) e 6t x 6e 6t x y () e 6t x y ()t + c, where c is a costat Substitutig t gives c x () Hece e 6t x y ()t + x () y The ad hece x e 6t (y ()t + x ()) 73

However, sice we are assumig x() y(), we have x () P x() y () y() 4 4 2 4 6 Hece x e 6t ( 3 2 t + 4 ) ad y 3 2 e6t Fially, solvig for x ad y, x 2 x y 4 y 2 4 e 6t ( 3 2 t + 4 ) 3 2 e6t ( 2)e 6t ( 3 2 t + 4 ) + 3 2 e6t 4e 6t ( 3 2 t + 4 ) e 6t ( 3t) e 6t (6t + ) Hece x e 6t ( 3t) ad y e 6t (6t + ) 9 Let A /2 /2 /4 /4 /2 /4 /4 /2 /4 3/2 (a) We first determie the characteristic polyomial ch A (λ) λ /2 /2 ch A (λ) det (λi 3 A) /4 λ /4 /2 /4 /4 λ /2 ( λ ) λ /4 /2 2 /4 λ /2 + /4 /2 2 /4 λ /2 ( λ ) {( λ ) ( λ ) } + { ( λ ) } 2 4 2 8 2 4 2 8 ( λ ) ( λ 2 3λ ) λ 2 4 8 {( λ λ ) ( λ 3 ) } 2 4 8 74

( λ λ(λ ) ) λ 2 5λ 4 + 4 ( λ 4 ) (b) Hece the characteristic polyomial has o repeated roots ad we ca use Theorem 622 to fid a o sigular matrix P such that P AP diag(,, 4 ) We take P X X 2 X 3, where X, X 2, X 3 are eigevectors correspodig to the respective eigevalues,, 4 Fidig X : We have to solve (A I 3 )X we have A I 3 /2 /2 /4 3/4 /2 /4 /4 /2 Hece the eigespace cosists of vectors X x, y, z t satisfyig x z ad y z, with z arbitrary Hece z X z z z ad we ca take X,, t Fidig X 2 : We solve AX We have /2 /2 A /4 /4 /2 /4 /4 /2 Hece the eigespace cosists of vectors X x, y, z t satisfyig x y ad z, with y arbitrary Hece y X y y ad we ca take X 2,, t Fidig X 3 : We solve (A 4 I 3)X We have A /4 /2 2 4 I 3 /4 /2 /4 /4 /4 75

Hece the eigespace cosists of vectors X x, y, z t satisfyig x 2z ad y z, with z arbitrary Hece 2z 2 X z z ad we ca take X 3 2,, t 2 Hece we ca take P (c) A P diag(,, 4 )P so A P diag(,, 4 )P Hece Let A 3 2 3 3 2 4 4 4 + 2 4 3 3 2 4 + 2 4 4 4 4 4 + 2 4 4 4 + 2 4 + 3 4 A 3 3 3 2 5 2 2 2 5 2 2 2 5 2 2 4 2 2 (a) We first determie the characteristic polyomial ch A (λ) ch A (λ) (λ 3) λ 5 2 2 2 λ 5 2 2 2 λ 5 λ 5 2 2 2 λ 5 2 76 R 3 R 3 + R 2 λ 5 2 2 2 λ 5 2 λ 3 λ 3

C 3 C 3 C 2 (λ 3) (λ 3) λ 5 2 4 2 λ 5 λ + 7 λ 5 4 2 λ + 7 (λ 3) {(λ 5)( λ + 7) + 8} (λ 3)( λ 2 + 5λ + 7λ 35 + 8) (λ 3)( λ 2 + 2λ 27) (λ 3)( )(λ 3)(λ 9) (λ 3) 2 (λ 9) We have to fid bases for each of the eigespaces N(A 9I 3 ) ad N(A 3I 3 ) First we solve (A 3I 3 )X We have 2 2 2 A 3I 3 2 2 2 2 2 2 Hece the eigespace cosists of vectors X x, y, z t satisfyig x y+z, with y ad z arbitrary Hece y + z X y y + z, z so X,, t ad X 2,, t form a basis for the eigespace correspodig to the eigevalue 3 Next we solve (A 9I 3 )X We have A 9I 3 4 2 2 2 4 2 2 2 4 Hece the eigespace cosists of vectors X x, y, z t satisfyig x z ad y z, with z arbitrary Hece z X z z z ad we ca take X 3,, t as a basis for the eigespace correspodig to the eigevalue 9 77

The Theorem 623 assures us that P X X 2 X 3 is o sigular ad P AP 3 3 9 78