Eigenvalue method

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3.4 Eigenvalue method

Note: 2 lectures, §5.2 in [EP], part of §7.3, §7.5, and §7.6 in [BD]

In this section we will learn how to solve linear homogeneous constant coefficient systems of ODEs by the eigenvalue method. Suppose we have such a system

⃗x′ = Px⃗,

where $P$ is a constant square matrix. We wish to adapt the method for the single constant coefficient equation by trying the function $λt e$ . However, $⃗x$ is a vector. So we try $λt ⃗x = ⃗ve$ , where $⃗v$ is an arbitrary constant vector. We plug this $⃗x$ into the equation to get

λ⃗veλt = P⃗veλt.

We divide by $eλt$ and notice that we are looking for a scalar $λ$ and a vector $⃗v$ that satisfy the equation

λ⃗v = P⃗v.

To solve this equation we need a little bit more linear algebra, which we now review.

3.4.1 Eigenvalues and eigenvectors of a matrix

Let $A$ be a constant square matrix. Suppose there is a scalar $λ$ and a nonzero vector $⃗v$ such that

A⃗v = λ⃗v.

We then call $λ$ an eigenvalue of $A$ and $⃗v$ is said to be a corresponding eigenvector.

Example 3.4.1: The matrix $[20 1 1]$ has an eigenvalue of $λ = 2$ with a corresponding eigenvector $[10]$ because

[ ] [ ] [ ] [ ] 2 1 1 2 1 0 1 0 = 0 = 2 0 .

Let us see how to compute the eigenvalues for any matrix. We rewrite the equation for an eigenvalue as

⃗ (A - λI)⃗v = 0.

We notice that this equation has a nonzero solution $⃗v$ only if $A - λI$ is not invertible. Were it invertible, we could write $(A - λI)-1(A - λI)⃗v = (A - λI)-1⃗0$ , which implies $⃗v = ⃗0$ . Therefore, $A$ has the eigenvalue $λ$ if and only if $λ$ solves the equation

det(A - λI) = 0.

Consequently, we will be able to find an eigenvalue of $A$ without finding a corresponding eigenvector. An eigenvector will have to be found later, once $λ$ is known.

Example 3.4.2: Find all eigenvalues of $[ ] 2 1 1 10 20 0 2$ .

We write

( ⌊ ⌋ ⌊ ⌋) ( ⌊ ⌋) ||||| |||||2 1 1||||| |||||1 0 0 |||||||||| ||||| |||||2 - λ 1 1 |||||||||| det|||( |||⌈1 2 0|||⌉ - λ |||⌈0 1 0 |||⌉|||) = det|||( |||⌈ 1 2 - λ 0 |||⌉|||) = 0 0 2 0 0 1 0 0 2 - λ = (2 - λ)((2 - λ )2 - 1) = - (λ - 1)(λ - 2)(λ - 3).

and so the eigenvalues are $λ = 1$ , $λ = 2$ , and $λ = 3$ .

Note that for an $n × n$ matrix, the polynomial we get by computing $det(A - λI)$ will be of degree $n$ , and hence we will in general have $n$ eigenvalues. Some may be repeated, some may be complex.

To find an eigenvector corresponding to an eigenvalue $λ$ , we write

(A - λI)⃗v = ⃗0,

and solve for a nontrivial (nonzero) vector $⃗v$ . If $λ$ is an eigenvalue, this will always be possible.

Example 3.4.3: Find an eigenvector of $[ ] 21 12 1 0 0 0 2$ corresponding to the eigenvalue $λ = 3$ .

We write

(⌊ ⌋ ⌊ ⌋)⌊ ⌋ ⌊ ⌋ ⌊ ⌋ ||||||||2 1 1|||| ||||1 0 0||||||||||||v1|||| ||||-1 1 1 |||| ||||v1|||| (A - λI)⃗v = ||||||||1 2 0||||- 3 ||||0 1 0||||||||||||v2||||= ||||1 - 1 0 |||| ||||v2||||= ⃗0. (⌈0 0 2⌉ ⌈0 0 1⌉)⌈v3⌉ ⌈0 0 -1⌉ ⌈v3⌉

It is easy to solve this system of linear equations. We write down the augmented matrix

⌊| | ⌋| |||| -1 1 1 |0 |||| |||⌈ 1 - 1 0 |0 |||⌉, 0 0 -1 |0

and perform row operations (exercise: which ones?) until we get

⌊ | ⌋ ||| 1 - 1 0 |0 ||| |||| 0 0 1 |0 ||||. |⌈ 0 0 0 |0 |⌉ |

The equations the entries of $⃗v$ have to satisfy are, therefore, $v1 - v2 = 0$ , $v3 = 0$ , and $v 2$ is a free variable. We can pick $v2$ to be arbitrary (but nonzero) and let $v1 = v2$ and of course $v3 = 0$ . For example, $[] 1 ⃗v = 10$ . Let us verify that we really have an eigenvector corresponding to $λ = 3$ :

⌊ ⌋ ||||2 1 1 ||||⌊||1⌋|| ⌊||3⌋|| ⌊||1⌋|| ||||1 2 0 ||||||||1|||| = ||||3|||| = 3||||1||||. |||||0 0 2 |||||||⌈ ||⌉ ||⌈ ||⌉ ||⌈ ||⌉ ⌈ ⌉ 0 0 0

Yay! It worked.

Exercise 3.4.1 (easy): Are eigenvectors unique? Can you find a different eigenvector for $λ = 3$ in the example above? How are the two eigenvectors related?

Exercise 3.4.2: Note that when the matrix is $2 × 2$ you do not need to write down the augmented matrix and do row operations when computing eigenvectors (if you have computed the eigenvalues correctly). Can you see why? Try it for the matrix $[2 1] 1 2$ .

3.4.2 The eigenvalue method with distinct real eigenvalues

OK. We have the system of equations

⃗x′ = P ⃗x.

We find the eigenvalues $λ 1$ , $λ2$ , …, $λn$ of the matrix $P$ , and corresponding eigenvectors $⃗v1$ , $⃗v2$ , …, $⃗vn$ . Now we notice that the functions $⃗v eλ1t 1$ , $⃗v eλ2t 2$ , …, $⃗veλnt n$ are solutions of the system of equations and hence $λ1t λ2t λnt ⃗x = c1⃗v1e + c2⃗v2e + ⋅⋅⋅ + cn⃗vne$ is a solution.

Theorem 3.4.1. Take $⃗x′ = P⃗x$ . If $P$ is an $n × n$ constant matrix that has $n$ distinct real eigenvalues $λ1$ , $λ2$ , …, $λn$ , then there exist $n$ linearly independent corresponding eigenvectors $⃗v1$ , $⃗v2$ , …, $⃗vn$ , and the general solution to $⃗x ′ = P⃗x$ can be written as

|--------------------------------------| | ⃗x = c1⃗v1eλ1t + c2⃗v2eλ2t + ⋅⋅⋅ + cn⃗vneλnt.| ---------------------------------------

The corresponding fundamental matrix solution is $X(t) = [⃗v1eλ1t ⃗v2eλ2t ⋅⋅⋅ ⃗vneλnt]$ . That is, $X (t)$ is the matrix whose $jth$ column is $⃗v eλjt j$ .

Example 3.4.4: Suppose we take the system

⌊ ⌋ ′ |||||2 1 1 ||||| ⃗x = |||⌈1 2 0 |||⌉⃗x. 0 0 2

Find the general solution.

We have found the eigenvalues $1,2, 3$ earlier. We have found the eigenvector $[1] 1 0$ for the eigenvalue 3. Similarly we find the eigenvector $[1] -1 0$ for the eigenvalue 1, and $[0 ] 1 -1$ for the eigenvalue 2 (exercise: check). Hence our general solution is

⌊ ⌋ ⌊ ⌋ ⌊ ⌋ ⌊ ⌋ ||1 || ||0 || ||1|| || c1et + c3e3t || x⃗= c ||||- 1||||et + c ||||1 ||||e2t + c ||||1||||e3t = ||||-c et + c e2t + c e3t||||. 1 ||⌈ ||⌉ 2 ||⌈ ||⌉ 3||⌈ ||⌉ ||⌈ 1 2 2t 3 ||⌉ 0 - 1 0 -c2e

In terms of a fundamental matrix solution

⌊ ⌋ ⌊ ⌋ |||et 0 e3t||| |||c1||| ⃗x = X(t)⃗c = ||||- et e2t e3t|||| ||||c2||||. |⌈0 - e2t 0|⌉ |⌈c3|⌉

Exercise 3.4.3: Check that this $⃗x$ really solves the system.

Note: If we write a homogeneous linear constant coefficient $nth$ order equation as a first order system (as we did in § 3.1), then the eigenvalue equation

d et(P - λI) = 0

is essentially the same as the characteristic equation we got in § 2.2 and § 2.3.

3.4.3 Complex eigenvalues

A matrix might very well have complex eigenvalues even if all the entries are real. For example, suppose that we have the system

[ ] ′ 1 1 ⃗x = - 1 1 ⃗x.

Let us compute the eigenvalues of the matrix $[1 1] P = -1 1$ .

( [ ]) det(P - λI) = det 1 - λ 1 = (1 - λ)2 + 1 = λ2 - 2λ + 2 = 0. -1 1 - λ

Thus $λ = 1 ± i$ . The corresponding eigenvectors will also be complex. First take $λ = 1 - i$ ,

(P - (1 - i)I)⃗v = ⃗0, [ ] i 1 ⃗v = ⃗0. -1 i

The equations $iv + v = 0 1 2$ and $- v + iv = 0 1 2$ are multiples of each other. So we only need to consider one of them. After picking $v2 = 1$ , for example, we have an eigenvector $[i] ⃗v = 1$ . In similar fashion we find that $[ ] -i1$ is an eigenvector corresponding to the eigenvalue $1 + i$ .

We could write the solution as

[ ] [ ] [ (1-i)t (1+i)t] ⃗x = c i e(1-i)t + c -i e(1+i)t = c1ie - c2ie . 1 1 2 1 c1e(1-i)t + c2e(1+i)t

We would then need to look for complex values $c 1$ and $c 2$ to solve any initial conditions. It is perhaps not completely clear that we get a real solution. We could use Euler’s formula and do the whole song and dance we did before, but we will not. We will do something a bit smarter first.

We claim that we did not have to look for a second eigenvector (nor for the second eigenvalue). All complex eigenvalues come in pairs (because the matrix $P$ is real).

First a small side note. The real part of a complex number $z$ can be computed as $z+ˉz 2$ , where the bar above $z$ means $------ a + ib = a - ib$ . This operation is called the complex conjugate. Note that if $a$ is a real number, then $ˉa = a$ . Similarly we can bar whole vectors or matrices. If a matrix $P$ is real, then $-- P = P$ . We note that $--- --- -- P ⃗x = P⃗x = P⃗x$ . Therefore,

--------- -- (P - λI)⃗v = (P - λˉI)⃗v.

So if $⃗v$ is an eigenvector corresponding to the eigenvalue $λ = a + ib$ , then $-- ⃗v$ is an eigenvector corresponding to the eigenvalue $ˉλ = a - ib$ .

Suppose that $a + ib$ is a complex eigenvalue of $P$ , and $⃗v$ is a corresponding eigenvector. Then

(a+ib)t ⃗x1 = ⃗ve

is a solution (complex valued) of $⃗x′ = P ⃗x$ . Then note that $----- ea+ib = ea-ib$ , and so

-- --(a-ib)t ⃗x2 = ⃗x1 = ⃗ve

is also a solution. The function

-- ⃗x = Re ⃗x = R e⃗ve(a+ib)t = ⃗x1 +-⃗x1-= ⃗x-1 +-⃗x2 3 1 2 2

is also a solution. And $⃗x3$ is real-valued! Similarly as $Im z = z-ˉz 2i$ is the imaginary part, we find that

⃗x4 = Im ⃗x1 = ⃗x1---⃗x2. 2i

is also a real-valued solution. It turns out that $⃗x3$ and $⃗x4$ are linearly independent. We will use Euler’s formula to separate out the real and imaginary part.

Returning to our problem,

[i] [i]( ) [iet cost + et sint] ⃗x 1 = e(1-i)t = etco st - iet sin t = t t . 1 1 e cost - ie sint

Then

[ t ] Re ⃗x1 = etsin t , eco st [ et cost ] Im ⃗x1 = t , -e sin t

are the two real-valued linearly independent solutions we seek.

Exercise 3.4.4: Check that these really are solutions.

The general solution is

[ t ] [ t ] [ t t ] ⃗x = c1 etsint + c2 e ctost = c1etsint + c2e ctos t. e cost - e sin t c1e cos t - c2e sin t

This solution is real-valued for real $c1$ and $c2$ . Now we can solve for any initial conditions that we may have.

Let us summarize as a theorem.

Theorem 3.4.2. Let $P$ be a real-valued constant matrix. If $P$ has a complex eigenvalue $a + ib$ and a corresponding eigenvector $⃗v$ , then $P$ also has a complex eigenvalue $a - ib$ with a corresponding eigenvector $ˉ⃗v$ . Furthermore , $⃗x′ = P ⃗x$ has two linearly independent real-valued solutions

(a+ib)t (a+ib)t ⃗x 1 = R e⃗ve , and ⃗x2 = Im ⃗ve .

So for each pair of complex eigenvalues we get two real-valued linearly independent solutions. We then go on to the next eigenvalue, which is either a real eigenvalue or another complex eigenvalue pair. If we had $n$ distinct eigenvalues (real or complex), then we will end up with $n$ linearly independent solutions.

We can now find a real-valued general solution to any homogeneous system where the matrix has distinct eigenvalues. When we have repeated eigenvalues, matters get a bit more complicated and we will look at that situation in § 3.7.

3.4.4 Exercises

Exercise 3.4.5 (easy): Let $A$ be a $3 × 3$ matrix with an eigenvalue of 3 and a corresponding eigenvector $[ ] 1 ⃗v = -31$ . Find $A⃗v$ .

Exercise 3.4.6: a) Find the general solution of $′ x1 = 2x1$ , $′ x2 = 3x2$ using the eigenvalue method (first write the system in the form $⃗x′ = A ⃗x$ ). b) Solve the system by solving each equation separately and verify you get the same general solution.

Exercise 3.4.7: Find the general solution of $x′ = 3x + x 1 1 2$ , $x′ = 2x + 4x 2 1 2$ using the eigenvalue method.

Exercise 3.4.8: Find the general solution of $x′1 = x1 - 2x2$ , $x′2 = 2x 1 + x2$ using the eigenvalue method. Do not use complex exponentials in your solution.

Exercise 3.4.9: a) Compute eigenvalues and eigenvectors of $[ 9 -2 -6] A = -8 3 6 10 -2 -6$ . b) Find the general solution of $′ ⃗x = Ax⃗$ .

Exercise 3.4.10: Compute eigenvalues and eigenvectors of $[-2 -1 -1] -33 2-1 10$ .

Exercise 3.4.11: Let $a,b,c,d,e,f$ be numbers. Find the eigenvalues of $[a b c] 0 d e 0 0 f$ .

Exercise 3.4.101: a) Compute eigenvalues and eigenvectors of $[ ] A = -11 00 3 1 2 0 2$ . b) Solve the system $⃗x′ = A⃗x$ .

Exercise 3.4.102: a) Compute eigenvalues and eigenvectors of $[ ] A = -11 1 0$ . b) Solve the system $⃗x′ = A⃗x$ .

Exercise 3.4.103: Solve $x′1 = x2$ , $x′2 = x1$ using the eigenvalue method.

Exercise 3.4.104: Solve $x′ = x2 1$ , $x′ = -x1 2$ using the eigenvalue method.