# Section 2.1. Section 2.2. Exercise 6: We have to compute the product AB in two ways, where , B = A =

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1 Section 2.1 Exercise 6: We have to compute the product AB in two ways, where 4 2 A = , B = Solution 1. Let b 1 = (1, 2) and b 2 = (3, 1) be the columns of B. Then Ab 1 = (0, 3, 13) and Ab 2 = (14, 9, 4). Thus AB = [Ab 1 Ab 2 ] = Solution 2. AB = ( 2) ( 2) ( 1) ( 3) ( 3) ( 1) ( 1) = Both answers are the same, which is reassuring! Exercise 18: Suppose the first two columns, b 1 and b 2 of B are equal. What can you say about the columns of AB (if AB is defined)? Solution. If B has m columns b 1,...,b m, then AB = [Ab 1 Ab 2... Ab m ]. We conclude that the first two columns of AB are equal too. Section 2.2 Exercise 18: Suppose that P is ivertible and A = PBP 1. Solve for B in terms of A. Solution. Multiply both sides of the equation by P 1 from left and by P from right to obtain P 1 AP = P 1 (PBP 1 )P = (P 1 P)B(P 1 P) = IBI = B. Thus B = P 1 AP. (Note that A and P 1 AP are in general two different matrices.) Exercise 24: Suppose A is n n and the equation Ax = b has a solution for each b R n. Explain why A must be invertible.

2 Solution. The assumption means that the columns of A span R n. This is equivalent to having a pivot in every row (by Theorem 4 in Section 1.4). But the number of rows is equal the number of columns, so each column has a pivot. Thus we have n pivots on the main diagonal, which means that A is row equivalent to I n. We conclude that A is invertible by Theorem 7 of Section 2.2. Exercise 30: To find the inverse of A, we row reduce the augmented matrix [A I]: [ / / / /5 1 7/5 2 Thus A is invertible and A 1 =. 4/5 1 ] Section 2.3 Exercise 36: Let T be a linear transformation that maps R n onto R n. Show that T 1 exists and maps R n onto R n. Is T 1 also one-to-one? Solution. Let A be the stardard n n-matrix for T. By the assumption on T, the columns of A span R n. Hence, by the Invertible Matrix Theorem A is an invertible matrix. The linear trasformation S : R n R n defined by S(x) = A 1 x is the inverse of T. The linear transformation S is onto because any x R n is the image under S of T(x): x = S(T(x)). It is also one-to-one because if S(y) = 0, then y = T(S(y)) = T(0) = 0. Alternatively, the last two claims can be proved by applying the Invertible Matrix Theorem to A 1. Section 2.7 Exercise 4: Write in homogeneous coordinates the matrix of the 2D transformation which first translates by ( 2, 3) and then scales the x-coordinate by.8 and the y-coordinate by 1.2. Solution. These two operations are given by the corresponding matrices: A = 0 1 3, B = Hence, their composition is given by BA =

3 Section 2.8 Exercise 30: Suppose the columns of a matrix A = [a 1,...,a p ] are linearly independent. Explain why {a 1,...,a p } is a basis for ColA. Solution. The pivot columns of A form a basis for ColA. Since the columns of A are linearly independent, every column has a pivot, that is, is a pivot column. Section 2.9 Exercise 10: We are given a matrix A and its echelon form: We see that Columns 1, 2, and 4 are pivot columns. Hence ColA has a basis made of vectors (1, 1, 2, 4), ( 2, 1, 0, 1), and (5, 5, 1, 1). Its dimension is 3. To find a basis for NulA, we have to bring A to the reduced echelon form: A The free variables are x 3 and x 5 and we can write the general solution to Ax = 0 as x = x x Hence, a basis for NulA consists of the vectors ( 3, 3, 1, 0, 0) and (0, 7, 0, 2, 1). Its dimension is 2. Section 3.1 Exercise 32: What is the determinant of an elementary scaling n n-matrix with k on the diagonal?

4 Solution We know that for a diagonal matrix, the determinant is the product of all diagonal entries. Hence, the answer here is k k = k n.

5 October 20: Xiaohui Luo s office hours will be 6:30-8:00pm on this day. October 28: Exam #2 (Chapters 2 & 3, and Sections 5.1 & 5.2) A Complete Proof that P A (λ) is a Polynomial Let me give here a carefull proof by induction that det(a λi) is a polynomial in λ. (I did not complete it on the last lecture.) First, we prove a slightly different statement: Theorem 1. Let B be n n-matrix with entries b ij = c ij λ + d ij for some constants c ij, d ij. Then P(λ) = det B is a polynomial. Moreover, the degree of P is at most n. Proof. We use induction on n to prove both claims about P. The statement is true for n = 1 as then P(λ) = c 11 λ + d 11. Suppose it is true for n 1. Let us prove it for n. By expanding along the first row, we obtain n P(λ) = ( 1) i+1 (c 1i λ + d 1i ) detb 1i. i=1 By the induction assumption, each detb 1i is a polynomial of degree at most n 1. Hence, P(λ) is the sum of polynomials of degree at most n, so it is itself a polynomial of degree at most n. By Theorem 1 we know that P A (λ) = det(a λi) is a polynomial of degree at most n. We cannot immediately conclude that the degree is precisely n because, perhaps, all terms λ n cancelled each other. So we recourse to induction again. Theorem 2. For any n n-matrix A, the polynomial P A (λ) = det(a λi) has degree precisely n. Proof. We use induction on n. For n = 1 we have P A (λ) = a 11 λ and the claim is true. (As usual, a ij is the ij-th entry of A.) Suppose the claim is true for n 1. Let us prove it for n. Let B = A λi. By applying the expansion along the first row, we obtain P A (λ) = (a 11 λ) detb 11 a 12 detb 12 + a 13 detb By the induction assumption, det B 11 = P A11 (λ) is a polynomial of degree precisely n 1. By Theorem 1, each detb 1i with i 2 is a polynomial of degree at most n 1. Hence, P A (λ) is the sum of one polynomial of degree n (namely (a 11 λ) detb 11 ) and some polymonials of degree at most n 1. Hence, the degree of P A (λ) is precisely n.

6 Section 3.2 Exercise 24: Using determinats to decide if the given set of vectors is linearly dependent: , 0, Solution. We make a matrix whose columns are these vectors: A = This is 3 3-matrix, that is, a square matrix. By the Invertible Matrix Theorem, the colums of A are linearly independent if and only if A is invertible, which happens if and only if its determinant is non-zero. We have = ( 1) ( 7) ( 1) = = Hence, the vectors are linearly independent. Section 5.1 Exercise 14: Find a basis for the eigenspace for A = 1 3 0, λ = Solution. We have to find the solution set to A + 2I = 0. Apply row transforms: A + 2I = / / /3 The general solution can be written as x = x 3 (1/3, 1/3, 1). Hence, the basis of this eigenspace consists of one vector v, namely v = 1/3 1/3 1

7 Exercise 26: Show that if A 2 is the zero matrix, then the only eigenvalue of A is 0. Solution. If λ is an eigenvalue of A with an eigenvector x, then A 2 x = A(Ax) = A(λx) = λx = λ 2 x. On the other hand, we know that A 2 x = 0. Thus λ = 0. Section 5.2 Exercise 12: Find the characteristic polynomial of A = Solution. It is det(a λi) = 1 λ λ λ = (2 λ) 1 λ λ = (2 λ)( 1 λ)(4 λ). Although this is not required, we can immediately tell the eigenvalues: λ 1 = 1, λ 2 = 2, λ 3 = 4 and, with some extra work, compute the corresponding eigenvectors A λ 1 I = Thus v 1 = (5/3, 1, 0) is a corresponding eigenvector A λ 2 I = Thus v 2 = (1/3, 0, 1) is a corresponding eigenvector. Finally, A λ 3 I = Thus v 3 = (0, 1, 0) is a corresponding eigenvector / /3 0

8 Section 5.3 Exercise 14: Diagonalize the matrix A = Solution. First we compute the characteristic polynomial by expanding A λi along the third row: 4 λ 0 P A (λ) = (5 λ) 2 5 λ = (5 λ)2 (4 λ). Thus the eigenvalues are λ 1 = 4 and λ 2 = λ 3 = 5 (that is, the eigenvalue 5 has multiplicity 2). Now we look for corresponding eigenvectors: A λ 1 I = / /2 0 Thus we can take v 1 = ( 1, 2, 0) for an eigenvector corresponding to λ 1 = 4. Next, A λ 2 I = Thus the corresponding eigenspace is spanned by v 2 = (0, 1, 0) and v 3 = ( 2, 0, 1). We see that the vectors v 1,v 2,v 3 are linearly idependent; hence the matrix P = [v 1,v 2,v 3 ] = is invertible and we have A = PDP 1, where D is the diagonal matrix with diagonal entries 4, 5, 5. Just to make sure we did not make any mistake, let us check if AP = PD: AP = = PD So, our calculations are correct. Section 5.5 Exercise 2: Find the eigenvalues ans a basis for each eigenspace for the following matrix acting on C 2 : 5 5 A =. 1 1

9 Solution. We have det(a λi) = 5 λ λ = (5 λ)(1 λ) + 5 = λ2 6λ The discriminat is D = = 4, so the roots are (6 ± D)/2, that is, λ 1 = 3 i and λ 2 = 3 + i. We have 2 + i 5 A λ 1 I = i We already know that the rows of A λ 1 I are linearly dependent, so to find a solution to (A λ 1 I)x = 0, it is enough to satisfy the first equation, which reads (2 + i)x 1 5x 2 = 0. We can take x 1 = 5 so that x 2 looks simpler. Then we have x 2 = 2 + i. Hence, the corresponding eigenspace is spanned by 5 v 1 =. 2 + i Likewise, to solve (A λ 2 I)x = 0, we have to solve the first equation, which is (2 i)x 1 5x 2 = 0. We take x 1 = 5 and x 2 = 2 i and obtain an eigenvector 5 v 1 =. 2 i Section 5.6 Exercise 0: Classify the origin for the dynamical system x k+1 = Ax k, where A is the matrix of Exercise 2 of Section 5.5. Solution. We have two complex eigenvavues, each of modulus 5 which is greater than 1. Hence, the origin is the repellor. The sequence x 0,x 1,... starting from any x 0 0 spirals away from the origin.

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