C 1 x(t) = e ta C = e C n. 2! A2 + t3



Similar documents
Similarity and Diagonalization. Similar Matrices

LS.6 Solution Matrices

x = + x 2 + x

Chapter 6. Orthogonality

by the matrix A results in a vector which is a reflection of the given

MAT 242 Test 2 SOLUTIONS, FORM T

HW6 Solutions. MATH 20D Fall 2013 Prof: Sun Hui TA: Zezhou Zhang (David) November 14, Checklist: Section 7.8: 1c, 2, 7, 10, [16]

Orthogonal Diagonalization of Symmetric Matrices

Similar matrices and Jordan form

MATH 423 Linear Algebra II Lecture 38: Generalized eigenvectors. Jordan canonical form (continued).

Section The given line has equations. x = 3 + t(13 3) = t, y = 2 + t(3 + 2) = 2 + 5t, z = 7 + t( 8 7) = 7 15t.

Linear Maps. Isaiah Lankham, Bruno Nachtergaele, Anne Schilling (February 5, 2007)

Au = = = 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 17. Orthogonal Matrices and Symmetries of Space

Brief Introduction to Vectors and Matrices

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)

13 MATH FACTS a = The elements of a vector have a graphical interpretation, which is particularly easy to see in two or three dimensions.

Examination paper for TMA4115 Matematikk 3

System of First Order Differential Equations

Linear Algebra Notes for Marsden and Tromba Vector Calculus

Problem Set 5 Due: In class Thursday, Oct. 18 Late papers will be accepted until 1:00 PM Friday.

Linear Algebra Review. Vectors

4.5 Linear Dependence and Linear Independence

University of Lille I PC first year list of exercises n 7. Review

Recall the basic property of the transpose (for any A): v A t Aw = v w, v, w R n.

The Characteristic Polynomial

So far, we have looked at homogeneous equations

Introduction to Matrix Algebra

Inner products on R n, and more

Math 550 Notes. Chapter 7. Jesse Crawford. Department of Mathematics Tarleton State University. Fall 2010

Chapter 20. Vector Spaces and Bases

[1] Diagonal factorization

MATH APPLIED MATRIX THEORY

A =

Inner Product Spaces

Chapter 12 Modal Decomposition of State-Space Models 12.1 Introduction The solutions obtained in previous chapters, whether in time domain or transfor

Dynamic Eigenvalues for Scalar Linear Time-Varying Systems

Lecture 1: Schur s Unitary Triangularization Theorem

NOTES ON LINEAR TRANSFORMATIONS

1 Introduction to Matrices

8 Hyperbolic Systems of First-Order Equations

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS

Notes on Orthogonal and Symmetric Matrices MENU, Winter 2013

MATH 304 Linear Algebra Lecture 18: Rank and nullity of a matrix.

Math 115A HW4 Solutions University of California, Los Angeles. 5 2i 6 + 4i. (5 2i)7i (6 + 4i)( 3 + i) = 35i + 14 ( 22 6i) = i.

LINEAR ALGEBRA. September 23, 2010

5.3 The Cross Product in R 3

Chapter 19. General Matrices. An n m matrix is an array. a 11 a 12 a 1m a 21 a 22 a 2m A = a n1 a n2 a nm. The matrix A has n row vectors

Numerical Analysis Lecture Notes

Name: Section Registered In:

8 Square matrices continued: Determinants

CHAPTER 2. Eigenvalue Problems (EVP s) for ODE s

Linearly Independent Sets and Linearly Dependent Sets

Solving Linear Systems, Continued and The Inverse of a Matrix

NOV /II. 1. Let f(z) = sin z, z C. Then f(z) : 3. Let the sequence {a n } be given. (A) is bounded in the complex plane

December 4, 2013 MATH 171 BASIC LINEAR ALGEBRA B. KITCHENS

SF2940: Probability theory Lecture 8: Multivariate Normal Distribution

Review Jeopardy. Blue vs. Orange. Review Jeopardy

ORDINARY DIFFERENTIAL EQUATIONS

Systems of Linear Equations

Solving Systems of Linear Equations

Linear Algebraic Equations, SVD, and the Pseudo-Inverse

4: EIGENVALUES, EIGENVECTORS, DIAGONALIZATION

3.1 State Space Models

Inner Product Spaces and Orthogonality

3. Let A and B be two n n orthogonal matrices. Then prove that AB and BA are both orthogonal matrices. Prove a similar result for unitary matrices.

Section Inner Products and Norms

PYTHAGOREAN TRIPLES KEITH CONRAD

3.2 Sources, Sinks, Saddles, and Spirals

SF2940: Probability theory Lecture 8: Multivariate Normal Distribution

Applied Linear Algebra I Review page 1

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS. + + x 2. x n. a 11 a 12 a 1n b 1 a 21 a 22 a 2n b 2 a 31 a 32 a 3n b 3. a m1 a m2 a mn b m

Using row reduction to calculate the inverse and the determinant of a square matrix

1 Determinants and the Solvability of Linear Systems

T ( a i x i ) = a i T (x i ).

Section Continued

Linear Control Systems

Eigenvalues, Eigenvectors, and Differential Equations

BANACH AND HILBERT SPACE REVIEW

Linear algebra and the geometry of quadratic equations. Similarity transformations and orthogonal matrices

Factorization Theorems

Eigenvalues, Eigenvectors, Matrix Factoring, and Principal Components

CONTROLLABILITY. Chapter Reachable Set and Controllability. Suppose we have a linear system described by the state equation

These axioms must hold for all vectors ū, v, and w in V and all scalars c and d.

Second Order Linear Nonhomogeneous Differential Equations; Method of Undetermined Coefficients. y + p(t) y + q(t) y = g(t), g(t) 0.

a 11 x 1 + a 12 x a 1n x n = b 1 a 21 x 1 + a 22 x a 2n x n = b 2.

4 MT210 Notebook Eigenvalues and Eigenvectors Definitions; Graphical Illustrations... 3

160 CHAPTER 4. VECTOR SPACES

Lecture L3 - Vectors, Matrices and Coordinate Transformations

ISOMETRIES OF R n KEITH CONRAD

Linear Algebra: Determinants, Inverses, Rank

Time-Domain Solution of LTI State Equations. 2 State-Variable Response of Linear Systems

The Determinant: a Means to Calculate Volume

THREE DIMENSIONAL GEOMETRY

MATH 304 Linear Algebra Lecture 9: Subspaces of vector spaces (continued). Span. Spanning set.

Applied Linear Algebra

MATH10212 Linear Algebra. Systems of Linear Equations. Definition. An n-dimensional vector is a row or a column of n numbers (or letters): a 1.

Notes on Determinant

Elasticity Theory Basics

x + y + z = 1 2x + 3y + 4z = 0 5x + 6y + 7z = 3

Transcription:

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 Exponential Form: C x(t) e ta C e ta C n where C C n are arbitrary constants The solution of the initial value problem is given by dt A x x(t ) x x(t) e (t t )A x Definition (Matrix Exponential): For a square matrix A e ta k t k k! Ak I + ta + t2 2! A2 + t3 3! A3 +

Evaluation of Matrix Exponential in the Diagonalizable Case: Suppose that A is diagonalizable; that is there are an invertible matrix P and a diagonal matrix D λ such that A P DP In this case we have e ta P e td P P e λ t e λnt P λ n EXAMPLE Let A 6 3 4 2 3 9 3 (a) Evaluate e ta (b) Find the general solutions of dt A x (c) Solve the initial value probelm dt Solution: A x x() 4 The given matrix A is diagonalized: A P DP with /2 P 2 /2 D 2 Part (a): We have e ta P e td P /2 2 /2 et e 2t 5 3 e t 6 4 2 5e t e 2t 3e t 3e t e 2t 2e t e t + e t 5e t + 2e 2t + 3e t 3e t + 2e 2t + 2e t e t e t 5e t + e 2t 6e t 3e t + e 2t 4e t e t + 2e t Part (b): The general solutions to the given system are x(t) e ta C C 2 C 3 where C C 2 C 3 are free parameters Part (c): The solution to the initial value problem is e t + e 2t + 8e t x(t) e ta 4 e t 2e 2t 8e t e t e 2t + 6e t 2

Evaluation of Matrix Exponential Using Fundamental Matrix: In the case A is not diagonalizable one approach to obtain matrix exponential is to use Jordan forms Here we use another approach We have already learned how to solve the initial value problem dt A x x() x We shall compare the solution formula with x(t) e ta x to figure out what e ta is We know the general solutions of /dt A x are of the following structure: x(t) C x (t) + + C n x n (t) where x (t) x n (t) are n linearly independent particular solutions rewritten as C x(t) x (t) x n (t) C with C C n For the initial value problem C is determined by the initial condition The formula can be x () x n () C x C x () x n () x Thus the solution of the initial value problem is given by x(t) x (t) x n (t) x () x n () x Comparing this with x(t) e ta x we obtain e ta x (t) x n (t) x () x n () In this method of evaluating e ta the matrix M(t) x (t) x n (t) plays an essential role Indeed e ta M(t)M() Definition (Fundamental Matrix Solution): If x (t) x n (t) are n linearly independent solutions of the n dimensional homogeneous linear system /dt A x we call M(t) x (t) x n (t) a fundamental matrix solution of the system (Remark : The matrix function M(t) satisfies the equation M (t) AM(t) Moreover M(t) is an invertible matrix for every t These two properties characterize fundamental matrix solutions) (Remark 2: Given a linear system fundamental matrix solutions are not unique However when we make any choice of a fundamental matrix solution M(t) and compute M(t)M() we always get the same result) 3

7 9 9 EXAMPLE 2 Evaluate e ta for A 3 5 3 3 3 5 Solution: We first solve /dt A x We obtain 3 x(t) C e t + C 2 e 2t + C 3 e 2t This gives a fundamental matrix solution: 3e t e 2t e 2t M(t) e t e 2t e t e 2t The matrix exponential is 3e t e 2t e 2t 3 e ta M(t)M() e t e 2t e t e 2t 3e t 2e 2t 3e t 3e 2t 3e t + 3e 2t e t + 2e 2t e t + 2e 2t e t e 2t e t e 2t e t e 2t e t + 2e 2t 5 8 4 EXAMPLE 3 Evaluate e ta for A 2 3 6 4 5 Solution: We first solve /dt A x We obtain 3 x(t) C e t + C 2 e 3t This gives a fundamental matrix solution: M(t) + C 3 e 3t 3e t e 3t e 3t 2te 3t e t e 3t 2 e 3t + te 3t e t e 3t te 3t 2t /2 + t t The matrix exponential is 3e t e 3t e 3t 2te 3t 3 e ta M(t)M() e t e 3t 2 e 3t + te 3t /2 e t e 3t te 3t 3e t 2e 3t 8te 3t 6e t 6e 3t 2te 3t 4te 3t e t + e 3t + 4te 3t e t + 3e 3t + te 3t te 3t e t e 3t + 4te 3t 2e t 2e 3t + te 3t e 3t 2te 3t 4

EXAMPLE 4 Evaluate e ta for A 9 5 4 5 Solution : (Use Diagonalization) Solving det(a λi) we obtain the eigenvalues of A: λ 7 + 4i λ 2 7 4i Eigenvectors for λ 7 + 4i: are obtained by solving A (7 + 4i)I v : v v 2 2 + i ( ) Eigenvectors for λ 2 7 4i: are complex conjugate of the vectors in ( ) The matrix A is now diagonalized: A P DP with P + i i 2 2 7 + 4i D 7 4i We have e ta P e td P + i i 2 2 e (7+4i)t i + i e (7 4i)t 2 2 4 i i 2 2 4 ( 2 4 i)e(7+4i)t + ( + 2 4 i)e(7 4i)t 5 8 ie(7+4i)t 5 8 ie(7 4i)t 2 ie(7+4i)t + 2 ie(7 4i)t ( + 2 4 i)e(7+4i)t + ( 2 4 i)e(7 4i)t Solution 2: (Use fundamental solutions and complex exp functions) A fundamental matrix solution can be obtained from the eigenvalues and eigenvectors: ( M(t) + 2 i)e(7+4i)t ( 2 i)e(7 4i)t e (7+4i)t e (7 4i)t The matrix exponential is ( e ta M(t)M() + 2 i)e(7+4i)t ( 2 i)e(7 4i)t + i i e (7+4i)t e (7 4i)t 2 2 ( 2 4 i)e(7+4i)t + ( + 2 4 i)e(7 4i)t 5 8 ie(7+4i)t 5 8 ie(7 4i)t 2 ie(7+4i)t + 2 ie(7 4i)t ( + 2 4 i)e(7+4i)t + ( 2 4 i)e(7 4i)t Solution 3: (Use fundamental solutions and avoid complex exp functions) A fundamental matrix solution can be obtained from the eigenvalues and eigenvectors: ( e 7t M(t) cos 4t sin 4t) e ( 7t cos 4t + sin 4t) 2 2 e 7t cos 4t e 7t sin 4t The matrix exponential is ( e e ta M(t)M() 7t cos 4t sin 4t) e ( 7t cos 4t + sin 4t) 2 2 e 7t cos 4t e 7t 2 sin 4t e 7t cos 4t + 2 e7t sin 4t 5 4 e7t sin 4t e 7t sin 4t e 7t cos 4t 2 e7t sin 4t 5

APPENDIX: Common Mistakes Since the years of freshmen calculus we all loved the exponential function e x with scalar variable x There are tons of simple and beautiful formulas for the scalar function e x The matrix exponential is however a quite different beast We need to be a little careful in handling it Here are some common mistakes I have seen people make: e t(a+b) e ta e tb The solutions of dt A(t) x are x(t) eta(t) C The solutions of dt A(t) x are x(t) er t A(s)ds C ( e B(t)) B (t)e B(t) All the above four statements are WRONG! () It is true that e t(a+b) e ta e tb if AB BA But in general e t(a+b) e ta e tb Example: For A B A + B we have e ta e tb + e 2t e 2t e t e t 2 e 2t + e 2t e t + e t 2 e t e t 2 e t e t 2 e t + e t but e t(a+b) e t e t (2) We learned how to solve dt A x where A is a constant matrix Unfortunately there is no general solution method for ( ) In particular dt A(t) x where A(t) is nonconstant x(t) e ta(t) C does not solve ( ) This even fails in the scalar case Example: The solutions of the scalar equation dx dt (sin t)x is given by x(t) e cos t C but not e t sin t C 6

(3) Likewise x(t) e R t A(s)ds C is also a wrong solution formula for ( ) This formula is only valid for scalar equations ie when the space dimension is Example: Consider dt 2t x x() The solution of this initial value problem is x(t) e t (t 2 )et + 2 e t The function ( t ) exp A(s)ds does not give the solution Indeed (4) We do have: ( t ) () t exp A(s)ds exp t 2 t e t 2 tet 2 te t e t (e b(t) ) b (t)e b(t) holds for scalar functions b(t) and (e ta ) Ae ta e ta A for constant matrices A But in general for nonconstant matrix function B(t) (e B(t) ) is neither B (t)e B(t) nor e B(t) B (t) t Example: Let B(t) t 2 We have e t B(t) ( e B(t) ) e t t+ 2 et + t 2 e t e t B (t)e B(t) e t 2t 2 tet 2 te t e t e B(t) B e (t) t 2 tet 2 te t e t 2t where A(t) 2t e t 2 tet 2 te t e t 2 tet 2 te t e t and hence e t 3 2 tet + 2 te t e t e t 2 tet + 3 2 te t e t All three matrix functions (e B(t) ) B (t)e B(t) and e B(t) B (t) are different from each other 7

EXERCISES 5 3 Evaluate e ta for A 4 2 2 (a) Evaluate e ta for A 3 8 (b) Solve 5 A x x() dt 4 3 Evaluate e ta for A 3 4 3 3 5 4 4 Evaluate e ta for A 9 4 8 3 9 7 3 5 (a) Evaluate e ta for A 6 5 8 5 2 (b) Solve A x x() dt 5 4 6 Evaluate e ta for A 2 7 Evaluate e ta for A 2 3 2 See next page for answers 8

Answers: 3 e ta 2 e4t 2 e2t 3 2 e4t + 3 2 e2t 2 e4t 2 e2t 2 e4t + 3 2 e2t e 2 (a) e ta 6te 2t 2te 2t 3te 2t e 2t + 6te 2t 5 5e (b) x(t) e ta 42te 2t e 2t 2te 2t 3e t 2e 2t 5e t + 6e 2t e 3t 3e t 4e 2t + e 3t 3 e ta 3e t 3e 2t 5e t + 9e 2t 3e 3t 3e t 6e 2t + 3e 3t 3e t 3e 2t 5e t + 9e 2t 4e 3t 3e t 6e 2t + 4e 3t 3e t 2e t 2e t 2e t e t + e t 4 e ta 6e t + 6e t 4e t + 5e t 2e t 2e t 6e t + 6e t 4e t + 4e t 2e t e t 3e t 2e t + 4te t 2e t 2e t + 3te t e t + e t te t 5 (a) e ta 6e t + 6e t 4te t 4e t + 5e t 3te t 2e t 2e t + te t 6e t + 6e t + 4te t 4e t + 4e t + 3te t 2e t e t te t (b) x(t) e ta 4et 3e t + 6te t 8e t + 9e t 6te t 8e t + 9e t + 6te t cos 2t + sin 2t sin 2t 6 e ta e 3t sin 2t cos 2t sin 2t or equivalently e ta ( i)e (3+2i)t + ( + i)e (3i)t 2ie (3+2i)t 2ie (3i)t 2 ie (3+2i)t + ie (3i)t ( + i)e (3+2i)t + ( i)e (3i)t 7 e ta 2e 3t + 3 cos t + sin t e 3t + 2 cos t sin t e 3t cos t + 3 sin t 4e 3t + 4 cos t 2 sin t 4e 3t + cos t 3 sin t e 3t + 2 cos t + 4 sin t 5 e 3t + 2 cos t 6 sin t 2e 3t 2 cos t 4 sin t e 3t + 6 cos t + 2 sin t 9