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Solutions to problem set 7 of a linear algebra course, focusing on eigenvalues and eigenvectors. The solutions cover various problems, including proving that an eigenvector of a matrix t has the same eigenvalue when multiplied by a scalar a, finding the eigenvectors of a shift operator s, and proving that the eigenvalues of an upper triangular matrix are its diagonal entries. The document also includes an optional problem about the characteristic polynomial of a graph's adjacency matrix.
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Problem Set 7 Solutions MATH 110: Linear Algebra
Each problem is worth 5 points. PART 1
Solutions in book. PART 2 Problem 1(10) a) If T : V → V has an eigenvalue λ, prove that aT has the eigenvalue aλ. Proof: Let x be an eigenvector for the eigenvalue λ. (aT )(x) = a(T (x)) = a(λx) = λ(ax) so ax is an eigenvector of aT with eigenvalue λ. b) If x is an eigenvector for both T 1 and T 2 , prove that x is an eigenvector for aT 1 + bT 2 and find the eigenvalues of aT 1 + bT 2 in terms of the eigenvalues of T 1 and T 2. Proof: Let λ be the eigenvalue of x for T 1 and let μ be the eigenvalue of x for T 2. (aT 1 + bT 2 )(x) = (aT 1 )(x) + (bT 2 )(x) = (λa + μb)x. c) Suppose that x is an eigenvector of T with eigenvalue λ. Show that x is an eigenvector of T 2 with eigenvalue λ^2. Proof: T 2 (x) = T (T (x)) = T (λx) = λ(T (x)) = λ(λx) = λ^2 x. d) Let P be a polynomial. Show that if x is an eigenvector of T with eigenvalue λ then x is an eigenvector of P (T ) with eigenvalue P (λ). Proof: By induction on the degree of P (we will denote the degree with the variable n). Suppose n = 0: the polynomial is a constant, and certainly x is an eigenvector of P (T ) (every vector actually is) with eigenvalue P (λ). Now suppose the theorem is true for all polynomials of degree less than or equal to n. Consider P of degree n + 1. If P has no constant term, then P (T ) = T Q(T ) where Q has degree n. By induction x is an eigenvector of Q with eigenvalue Q(λ). Therefore, T Q(T )(x) = T (Q(λ)x) = Q(λ)T (x) = Q(λ)(λx) = λQ(λ)x. Thus x is an eigenvector of P with eigenvalue λQ(λ) = P (λ). Now, if P has a constant term c, then P − c is a polynomial of degree
n + 1 with no constant term. By the above argument x is an eigenvector of P − c with eigenvalue (P − c)(λ). Notice that x is also an eigenvector of c with eigenvalue c (proved for the base case). By part (b) x is an eigenvector of the polynomial P − c + c = P with eigenvalue (P − c)(λ) + c = P (λ). Problem 2(10) If T : V → V has the property that T 2 has a nonnegative eigenvalue λ^2 , prove that at least one of λ or −λ is an eigenvalue for T. (Hint: T 2 − λ^2 I = (T + λI)(T − λI)). Proof: Let x be an eigenvector of T 2 with the eigenvalue λ^2. (T 2 − λ^2 I)(x) = 0. Therefore, (T + λI)(T − λI)(x) = 0. If (T − λI)(x) = 0 then x is an eigenvector of T with eigenvalue λ. Otherwise, (T − λI)(x) = y where y is some nonzero vector. In this case, (T + λI)(y) = 0, which means that y is an eigenvector of T with eigenvalue −λ. Thus, either λ or −λ is an eigenvalue. Problem 3 (10) Let V be the linear space of all real convergent sequences {xn}. Define T : V → V as follows: if x = {xn} is a convergent sequence with limit a, let T (x) = {yn} where yn = a − xn for n ≥ 1. Prove that T has only two eigenvalues λ = 0 and λ = −1 and determine the eigenvectors belonging to each such λ. Solution: Let λ be an eigenvalue of T. Then a − xn = λxn for each n, which means that xn = (^) λ+1a (assuming that λ 6 = −1). Taking the limit, as n → ∞, we see that (^) λ+1a = a which implies that λ = 0. Thus λ is either 0 or −1. If λ = 0 this means that xn is a constant sequence, thus the eigenvectors corresponding to eigenvalue 0 are the constant sequences. If λ = −1 then we see that there is no restriction on xn, other than that a = 0. Thus, the sequences with limit 0 are the eigenvectors with eigenvalue −1. Problem 4 (20) Let V be the vector space of sequences {an} over the real numbers. The shift operator S : V → V is defined by
S((a 1 , a 2 ,.. .) = (a 2 , a 3 , a 4 ,.. .).
Find the eigenvectors of S, and show that the subspace W consisting of the sequences {xn} satisfying xn+2 = xn+1 + xn is a two dimensional, S-invariant subspace of V. Also, find an explicit basis for W. Using these results, find an explicit formula for the nth Fibonacci number fn where fn+2 = fn+1 + fn and f 1 = f 2 = 1.
Let f (x) = xn^ +c 1 xn−^1 +c 2 xn−^2 +.. .+cn be the characteristic polynomial of the adjacency matrix of a graph. Show that c 1 = 0, −c 2 is the number of edges in the graph, and −c 3 is twice the number of triangles in the graph.