Eigenvalues and Eigenvectors


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1 LECTURE 3 Eigenvalues and Eigenvectors Definition 3.. Let A be an n n matrix. The eigenvalueeigenvector problem for A is the problem of finding numbers λ and vectors v R 3 such that Av = λv. If λ, v are solutions of a eigenvectoreigenvalue problem then the vector v is called an eigenvector of A and λ is called an eigenvalue of A. Remark 3.2. Because eigenvectors and eigenvalues always come in pairs (λ, v) one often uses language like λ is the eigenvalue of the vector v or v is an eigenvector of A with eigenvalue λ. Note that the multiplication on the left hand side is matrix multiplication (complicated) while the multiplication on the right hand side is scalar multiplication (easy). Thus, one way of thinking about the eigenvectoreigenvalue problem is that, when a solution is found we can simplify matrix multiplication. I remark that so doing, we ll also get a better idea of what is actually going on in many problems.. Motivating Examples.. Rotations and Axes of Rotation. Suppose R is a 3 3 matrix with the property that its transpose equals its inverse: R t = R Such a matrix will define a linear transformation T R : R 3 R 3 that preserves the lengths of vectors. For, since x 2 = x t x = [ x x x 2 x 3 x 2 = (x ) 2 + (x 2 ) 2 + (x 3 ) 2 x 3 if we set we ll have T R (x) = Rx Rx 2 = (Rx) t (Rx) = x t R t Rx = x t R Rx = x t x We call such matrices rotation matrices, because these are exactly the 3 3 matrices that implement rotations on 3dimensional vectors. Now one property of rotations is that they always have an axis of rotation: meaning there is always one line in R 3 that is left undisturbed by the rotation. We note, for example, that in the case of the planet Earth, the line that that passes through the north and south poles is unaffected by its daily rotation. Given an arbitary rotation matrix R, one can thus ask: How to figure out its axis of rotation?. Well, the condition we d want is that Rx = () x That is, we d want to find an eigenvector x of R with eigenvalue. Once we find such a vector, then we ll have the corresponding axis of rotation.
2 . MOTIVATING EXAMPLES 2 Example 3.3. Consider the following linear transformation T : R 3 R 3 : The corresponding matrix is T ([x, y, z) = [x, y cos(θ) + z sin (θ), y sin(θ) + z cos(θ) A T = cos(θ) sin(θ) sin(θ) cos (θ) This transformation corresponds to a rotation about the xaxis by θ. Question 3.4. What vectors in R 3 are unaffected by this transformation? This question is equivalent to solving the linear system (3.) A T x = x or or (A T I) x = cos(θ) sin(θ) sin(θ) cos (θ) Rowreducing the matrix on the lefthand side yields sin(θ) cos(θ) 2 cos(θ) sin(θ)) x y z = x is arbitrary y = z = In other words, the transformation T leaves the points along the xaxis unaffected, and only those points remain unaffected by the transformation. Perhaps the preceding example is so simpleminded that the importance of the eigenvectoreigenvalue problem is obfuscated; surely a rotation about the zaxis will leave points along the zaxis fixed. However, consider the linear transformation T : R 3 R 3 that combines a rotation about zaxis with a rotation about the xaxis: x cos θ sin θ x T : y cos φ sin φ sin θ cos θ y z sin φ cos φ z = (cos θ) x + (sin θ) y (cos φ sin θ) x + (cos φ cos θ) y + (sin φ) z (sin φ sin θ) x (sin φ cos θ) y + (cos φ) z This is in fact another rotation; but which rotation? What is the axis of rotation? The answer to this question can be found by solving i.e, by solving an eigenvectoreigenvalue problem. T x = ()x Example 3.5. Let C (R) denote the vector space of smooth (infinitely differentiable) functions on the real line. Consider the transformation D : C (R) C (R) defined by D[f = df dx It is easy to see that this is a linear transformation (it preserves scalar multiplication and vector addition in C (R)). We can associate with D an infinite matrix (employing simple monomials in x, {, x, x 2, x 3,... }
3 2. COMPUTING EIGENVALUES 3 as a standard basis for C (R)): D [ = D [x = D [ x 2 = 2x D [ x 3 = 3x 2 Solution of a differential equation of the form. 2 A D = df dx = λf is then equivalent to a linear system of the form (3.2) A D f = λf Note the similarity of this equation with (3.). In terms of these new semantics, the first example shows that the vectors along the xaxis are eigenvectors of the linear transformation T with eigenvalues ; and the second example shows that the solution of a differential equation can be thought of as a problem of finding an eigenvector for a linear transformation on the vector space of smooth functions on R. 2. Computing Eigenvalues For a given n n matrix A, the eigenvalue problem is the problem of finding the eigenvalues and eigenvectors of A. Our abstract machinery now plays a crucial role. Before finding a solution v of (3.3) Av = λv, λ R we first ask, when do nontrivial solutions exist? First we rewrite (3.3) as or Av λv = (A λi) v = Now recall that demanding that the existence of nonzero solutions to linear system Bx = is equivalent to demanding that the null space of B is at least dimensional, which is equivalent to saying that when B is rowreduced to rowechelon form there is at least one row full of zeros, which implies det (B) =. Thus, we arrive at the following criteria for the existence of eigenvectors and eigenvalues.
4 2. COMPUTING EIGENVALUES 4 Assertion 3.. If nontrivial solutions of Av = λv exist then det (A λi) =. Example 3.6. Find the possible eigenvalues of the matrix [ 3 2 A = 2 If A is to have eigenvalues, then we must have [ 3 λ 2 = det (A λi) = det = (3 λ) ( λ) 4 = λ 2 3λ 4 = (λ 4) (λ + ) 2 λ Hence, the possible eigenvalues are 4 and. This simple example actually serves as the prototype for the general case: To find the eigenvalues of an n n matrix A : Fact 3.7. () Compute the determinant of the matrix H = A λi, regarding λ as a symbolic variable. (2) det (A λi) will be a polynomial P A (λ) of degree n in λ. We shall call this polynomial the characteristic polynomial of A. (3) Solve P A (λ) =. The roots of this equation will be the possible eigenvalues of A. At this point it is worthwhile to recall the Fundamental Theorem of Algebra. Theorem 3.8. Every polynomial p(λ) of degree n has a unique factorization in terms of linear polynomials: p(λ) = c (λ r ) m (λ r 2 ) m2 (λ r s ) ms where r r 2 r s, and s i= m i = n. The numbers r i are, in general, complex numbers and are called the roots of p(x); they coincide precisely with the solutions of p(λ) =. The integers m i are called the multiplicities of the corresponding roots. This theorem tells us that the eigenvalues of a matrix can be complex numbers (in fact, this is what one must expect in general), and that they may occur with some multiplicity. The multiplicity of an eigenvalue will be very relevant to the problem of diagonalizing matrices (which we will take up in the next lecture). Example 3.9. Find the possible eigenvalues of We have The characteristic polynomial is thus A = [ 2 2 [ λ 2 det (A λi) = det 2 λ Thus, the possible eigenvalues are λ = ±2i. = λ P A (λ) = λ = (λ + 2i) (λ 2i) Example 3.. Find the possible eigenvalues of A = 2 2
5 3. COMPUTATION OF EIGENVECTORS 5 We have det (A λi) = det = ( λ) det λ 2 λ 2 λ [ λ λ = λ ( λ) ( + λ) + (2)( λ) = 3λ λ 3 2 = (2 + λ) (λ ) 2 [ 2 () det 2 λ [ 2 λ + () det 2 We thus see that A has two possible eigenvalues, λ = 2 and λ =. The eigenvalue λ = occurs with multiplicity Computation of Eigenvectors In finding the eigenvalues of a matrix, all we have accomplished is figuring out for what values of λ (3.4) Av = λv can have a solution. I will now show you how to find the corresponding eigenvectors v. This is very straightforward given our recent discussion of homogeneous linear systems. For finding vectors v such that equation (3.4) is satisfied is equivalent to finding solutions of (A λi) x = Let s do a quick example. Example 3.. Find the eigenvectors of A = [ In the first example, we discovered that the possible eigenvalues for this matrix are λ = 4,. Let s consider first the solution of (3.5) (A 4I) x = (which should be an eigenvector of A corresponding to the eigenvalue λ = 4). We have [ [ A 4I = = This matrix row reduces to [ 2 And so the solutions of (3.5) coincide with solutions of x 2x 2 = = Thus, any vector of the form x = 2x 2 x = [ 2 v = r [ 2x2 x 2 = x 2 [ 2, r R, x 2 R will be an eigenvector of A with eigenvalue 4. Now let s look for eigenvectors corresponding to the eigenvalue. We thus look for solutions of (3.6) (A + I) x =
6 3. COMPUTATION OF EIGENVECTORS 6 We have which is rowequivalent to A + I = [ [ 2 = So the solutions of (3.6) coincide with the solutions of 2x + x 2 = = [ x = 2 x 2 x = x 2 [ 2 So the corresponding eigenvectors will be vectors of the form [ v = r 2 Example 3.2. Find the eigenvectors of A = 2 2 Recall from Example 5.9 that the characteristic polynomial for this matrix factorizes as P A (λ) = (2 + λ) (λ ) 2 and so we have two possible eigenvalues: λ = 2 with multiplicity, and λ = with multiplicity 2. Let s look first for eigenvectors corresponding to the eigenvalue λ = 2. We have 2 2 A ( 2)I = 2 3 row reduction and so the solutions x of (A + 2I) x = satisfy 2x + x 3 = 3x 2 + 2x 3 = = x = 2 x x 3 x 3 span So the eigenvectors of A corresponding to the eigenvalue 2 are scalar multiples of the vector [ 2, 2 3,. We ll now repeat the calculation for the double root λ =. We have A ()I = 2 row reduction 2 2 and so the solutions x of (A I) x = satisfy x = x 3 = = x = x 2 So the eigenvectors of A corresponding to the eigenvalue are scalar multiples of the vector [,,.
7 5. TWO KINDS OF EIGENVALUE MULTIPLICITIES 7 4. Eigenspaces Definition 3.3. Let A be an n n matrix and suppose r R is an eigenvalue of A. The reigenspace of A is the subset {v R n Av = rv} Lemma 3.4. Let A be an n n matrix and suppose r R is an eigenvalue of A. The reigenspace of A is a subspace of R n. Proof. This can be seen two ways. First of all, suppose r is an eigenvalue of A and v, v 2 lie in reigenspace of A. Then Av = rv and Av 2 = rv 2 and so A (v + v 2 ) = Av + Av 2 = rv + rv 2 = r (v + v 2 ) Thus, v + v 2 is also in the reigenspace of A. We also have A (cv ) = cav = crv = r (cv ) Since the reigenspace of A is closed under both vector addition and scalar multiplication, it is a subspace. Even more easily, v reigenspace of A Av = rv v is a solution of (A ri) x = So reigenspace of A = NullSp (A ri) = a subspace of R n 5. Two Kinds of Eigenvalue Multiplicities Here I want to introduce some nomenclature that will become important latter. Suppose r is an eigenvalue of an n n matrix A. This means that λ = r is a root of det (A λi) = We call the right hand side of this equation the characterist polynomial of A and we shall denote by p A (λ). Thus, p A (λ) det (A λi) Since r is a root of p A (λ) =, it follows from the Fundamental Theorem of Algebra that (λ r) is a factor of p A (λ). In fact, one has Lemma 3.5. If r is a eigenvalue of an n n matrix A, there is an integer m such that p A (λ) = (λ r) m Q (λ) with Q (λ) a polynomial of degree n m such that Q (r). We call the integer m in the lemma, the algebraic multiplicity of the eigenvalue r. Suppose again that r is an eigenvalue of an n n matrix A. The dimension of the reigenspace of A is the geometric multiplicity of the eigenvalue r.
8 5. TWO KINDS OF EIGENVALUE MULTIPLICITIES 8 Example 3.6. Consider the matrix A = find the eigenvalues of A and their algebraic and geometric multiplicities. Well first, let s find the eigenvalues of A. It s characteristic polynomial is 3 λ p A (λ) = det (A λi) = det 3 λ 2 λ Since the matrix A λi is already in Row Echelon Form, its determinant is just the product of its diagonal elements. Thus, p A (λ) = (3 λ) 2 (2 λ) From this we see that λ = 3 is an eigenvalue with algebraic multiplicity 2 and λ = 2 is an eigenvalue with algebraic multiplicity. To determine the geometric muliplicities we need to determine the dimension of the corresponding eigenspaces. λ = 3 eigenspace. The 3eigespace is the null space of A 3I = Rowreducing this matrix (pretty easily) we get 3eigenspace = N ullsp = solutions set of = span { x2 = x 3 = Since we have one basis vector the dimension of the 3eigenspace is. multiplicity of λ = 3 is. (Recall its algebraic multiplicity was 2.) λ = 2 eigenspace The 2eigenspace is the solution set of (A 2I) x =. We have (A 2I) = row reduces to and so the null space is corresponds to solutions of x = x = = x x 2 = 3 x 3 Since we have one basis vector, multiplicity of the eigenvalue λ = 2 is. span Thus, the geometric, the 2eigenspace is dimensional. Hence, the geometric Remark 3.7. Actually, to determine the geometric multiplicity of an eigenvalue r all we really had to do is rowreduce A ri to row echelong form and count the number of columns without pivots  that would give the dimension of NullSp (A ri) which is the same thing is the geometric multiplicity of the eigenvalue r.
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