1 00:00:08,320 --> 00:00:10,134 PROFESSOR: Hi, and welcome. 2 00:00:10,134 --> 00:00:11,550 Today, we're going to do a problem 3 00:00:11,550 --> 00:00:15,630 about powers of a matrix. 4 00:00:15,630 --> 00:00:21,270 Our problem is first to find a formula for the k-th power 5 00:00:21,270 --> 00:00:24,510 of this matrix C. This is a two by two matrix that 6 00:00:24,510 --> 00:00:27,834 depends on variables a and b. 7 00:00:27,834 --> 00:00:29,250 And the second part of our problem 8 00:00:29,250 --> 00:00:34,210 is to calculate C to the 100th in the special case 9 00:00:34,210 --> 00:00:38,710 where a and b are -1. 10 00:00:38,710 --> 00:00:40,840 You can hit pause now, and I'll give you a minute 11 00:00:40,840 --> 00:00:42,990 to do the problem yourself. 12 00:00:42,990 --> 00:00:45,210 And then, I'll come back and we can do it together. 13 00:00:57,620 --> 00:00:58,720 OK. 14 00:00:58,720 --> 00:01:00,220 We're back. 15 00:01:00,220 --> 00:01:05,980 Now, what's the first step in finding powers of a matrix? 16 00:01:05,980 --> 00:01:09,130 Well, we need to find the eigenvalues and eigenvectors 17 00:01:09,130 --> 00:01:10,900 of this matrix. 18 00:01:10,900 --> 00:01:13,030 So how do we do that? 19 00:01:13,030 --> 00:01:22,840 We compute the determinant of C minus lambda I, which is just 20 00:01:22,840 --> 00:01:35,090 the determinant of this matrix: 2b minus a minus lambda, 21 00:01:35,090 --> 00:01:47,320 2b minus 2a; a minus b, and 2a minus b minus lambda. 22 00:01:47,320 --> 00:01:48,780 OK. 23 00:01:48,780 --> 00:01:54,940 If you compute this, well, we have a lambda squared term. 24 00:01:54,940 --> 00:01:56,760 OK. 25 00:01:56,760 --> 00:01:59,500 Our lambda term, if you look at it, 26 00:01:59,500 --> 00:02:06,800 you'll see we get 2b minus a plus 2a minus b, 27 00:02:06,800 --> 00:02:08,470 which is just a plus b. 28 00:02:08,470 --> 00:02:10,680 And we have a negative sign. 29 00:02:10,680 --> 00:02:15,080 And it's negative a plus b times lambda. 30 00:02:15,080 --> 00:02:18,930 And our last term is a little tougher to compute. 31 00:02:18,930 --> 00:02:21,040 So I'll let you do it yourself. 32 00:02:21,040 --> 00:02:25,390 But you're just going to get plus a*b. 33 00:02:25,390 --> 00:02:32,390 And this will factor as lambda minus a times lambda minus b. 34 00:02:32,390 --> 00:02:38,820 So our eigenvalues are just a and b. 35 00:02:38,820 --> 00:02:41,370 Now we need to find our eigenvectors. 36 00:02:41,370 --> 00:02:43,720 So how do we do that? 37 00:02:43,720 --> 00:02:46,170 Well, what we need to do is we need 38 00:02:46,170 --> 00:02:51,230 to look at C minus a times the identity. 39 00:02:51,230 --> 00:02:54,770 And we need to find the null space of this matrix. 40 00:02:54,770 --> 00:02:58,360 So what do we get here? 41 00:02:58,360 --> 00:03:02,010 We get 2b minus 2a. 42 00:03:07,330 --> 00:03:13,080 And then our next entry here, we get 2a minus b minus a. 43 00:03:13,080 --> 00:03:15,810 So this is a minus b. 44 00:03:15,810 --> 00:03:16,850 Good. 45 00:03:16,850 --> 00:03:27,310 So you can see that this matrix has the same columns 46 00:03:27,310 --> 00:03:30,740 and the same rows. 47 00:03:30,740 --> 00:03:37,670 And so you can see that a vector in the null space, 48 00:03:37,670 --> 00:03:43,690 since this column is -2 times this column, 49 00:03:43,690 --> 00:03:48,370 we can see that our first eigenvector is just-- or 1, 2, 50 00:03:48,370 --> 00:03:51,050 I should say. 51 00:03:51,050 --> 00:03:54,964 It's just [1, 2]. 52 00:03:54,964 --> 00:03:55,464 Good. 53 00:03:58,220 --> 00:04:02,280 Well, I guess we have space to do the second one too. 54 00:04:02,280 --> 00:04:04,950 Why not? 55 00:04:04,950 --> 00:04:08,780 So let's write out the second one also. 56 00:04:08,780 --> 00:04:11,976 Here, we're subtracting b instead of a. 57 00:04:11,976 --> 00:04:14,020 You get b minus a. 58 00:04:14,020 --> 00:04:17,620 You get 2b minus 2a. 59 00:04:17,620 --> 00:04:20,870 We get a minus b. 60 00:04:20,870 --> 00:04:22,430 And what do we have here? 61 00:04:22,430 --> 00:04:25,071 We have 2a minus 2b. 62 00:04:28,400 --> 00:04:33,000 So now, what's in the null space of this matrix? 63 00:04:33,000 --> 00:04:40,780 Well, what you can see is that this column 64 00:04:40,780 --> 00:04:43,090 is -1 times that column. 65 00:04:43,090 --> 00:04:50,910 So our second eigenvector is just going to be [1, 1]. 66 00:04:50,910 --> 00:04:54,020 And I should remind you that if you have a harder example, 67 00:04:54,020 --> 00:04:57,750 you can just find these null spaces by elimination 68 00:04:57,750 --> 00:04:59,490 like we always do. 69 00:04:59,490 --> 00:05:00,040 Great. 70 00:05:00,040 --> 00:05:02,610 Now we have our eigenvalues and our eigenvectors. 71 00:05:02,610 --> 00:05:05,400 So now we can write C in a nice easy way that 72 00:05:05,400 --> 00:05:09,810 allows us to take powers of it. 73 00:05:09,810 --> 00:05:11,300 So what's that way? 74 00:05:11,300 --> 00:05:21,060 So that's C equals S lambda S inverse. 75 00:05:21,060 --> 00:05:24,910 So this is just, what is S? 76 00:05:24,910 --> 00:05:29,750 Remember, S is our matrix of eigenvectors. 77 00:05:29,750 --> 00:05:36,700 So S is the matrix 1, 2; 1, 1. 78 00:05:36,700 --> 00:05:37,680 Good. 79 00:05:37,680 --> 00:05:39,250 Now what is lambda? 80 00:05:39,250 --> 00:05:40,951 Lambda is the matrix of eigenvalues. 81 00:05:40,951 --> 00:05:41,450 Right? 82 00:05:41,450 --> 00:05:44,360 So it's just a and b. 83 00:05:44,360 --> 00:05:49,330 Those are the diagonal entries of my lambda matrix. 84 00:05:49,330 --> 00:05:53,110 And then, we just find S inverse. 85 00:05:53,110 --> 00:05:58,040 So we just take negative signs here 86 00:05:58,040 --> 00:06:05,340 and recall that we have to divide by the determinant. 87 00:06:05,340 --> 00:06:10,840 And the determinant of this matrix is just -1. 88 00:06:10,840 --> 00:06:13,080 So we just change the signs there. 89 00:06:13,080 --> 00:06:13,680 Good. 90 00:06:13,680 --> 00:06:18,310 So this is our nice decomposition of C. 91 00:06:18,310 --> 00:06:20,740 Now how do we take powers of C? 92 00:06:20,740 --> 00:06:25,820 Well, C to the k is just S lambda to the k S inverse. 93 00:06:28,360 --> 00:06:39,040 [1, 1; 2, 1]; a to the k, b to the k; [-1, 1; 2, -1]. 94 00:06:39,040 --> 00:06:40,020 Good. 95 00:06:40,020 --> 00:06:46,820 And multiplying these matrices together, 96 00:06:46,820 --> 00:06:49,110 just do a little arithmetic here. 97 00:06:54,660 --> 00:06:56,880 Got a bunch of powers of a and b. 98 00:06:56,880 --> 00:07:02,040 Because we take powers of the eigenvalues. 99 00:07:02,040 --> 00:07:09,050 We have here, we have 2 b to the k minus a to the k. 100 00:07:09,050 --> 00:07:12,540 Have a to the k minus b to the k. 101 00:07:15,390 --> 00:07:20,820 2 b to the k minus 2 a to the k. 102 00:07:20,820 --> 00:07:27,800 And finally, we get 2 a to the k minus b to the k. 103 00:07:27,800 --> 00:07:30,540 And this is our k-th power matrix. 104 00:07:33,213 --> 00:07:33,713 Good. 105 00:07:36,560 --> 00:07:37,305 A quick check. 106 00:07:37,305 --> 00:07:39,900 It's always good to check your work here. 107 00:07:39,900 --> 00:07:42,880 Let's plug in k equals 1. 108 00:07:42,880 --> 00:07:43,880 And what do we get? 109 00:07:43,880 --> 00:07:51,080 We get 2b minus a, a minus b, 2b minus 2a, and 2a minus b. 110 00:07:51,080 --> 00:07:53,410 And if we can go all the way back to our matrix 111 00:07:53,410 --> 00:07:57,160 at the very beginning, all the way back here, 112 00:07:57,160 --> 00:08:00,550 that agrees perfectly with what we started with. 113 00:08:00,550 --> 00:08:02,250 So that's good. 114 00:08:02,250 --> 00:08:05,300 That means that we did this decomposition right. 115 00:08:05,300 --> 00:08:05,810 Good. 116 00:08:05,810 --> 00:08:09,160 So now, we've computed the k-th power of this matrix. 117 00:08:09,160 --> 00:08:13,750 Let's do a particular example. 118 00:08:13,750 --> 00:08:18,380 So let's plug in a and b are -1. 119 00:08:18,380 --> 00:08:22,470 So a equals b equals -1. 120 00:08:22,470 --> 00:08:25,690 And k equals 100. 121 00:08:25,690 --> 00:08:27,240 Then what do we get? 122 00:08:27,240 --> 00:08:31,060 Well, -1 to the 100th is just 1. 123 00:08:31,060 --> 00:08:34,830 So we're just plugging in 1 for b to the k and a to the k 124 00:08:34,830 --> 00:08:35,950 everywhere. 125 00:08:35,950 --> 00:08:42,049 And we just get, in this case, C to the 100th is just 126 00:08:42,049 --> 00:08:45,420 [1, 0; 0, 1]. 127 00:08:45,420 --> 00:08:49,160 It's just the identity matrix. 128 00:08:49,160 --> 00:08:51,720 Great. 129 00:08:51,720 --> 00:08:52,290 Great. 130 00:08:52,290 --> 00:08:52,790 OK. 131 00:08:52,790 --> 00:08:57,800 Now to summarize, how do we take powers of a matrix? 132 00:08:57,800 --> 00:08:59,940 Well, first we diagonalize. 133 00:08:59,940 --> 00:09:02,980 We write our matrix as S lambda S inverse. 134 00:09:02,980 --> 00:09:06,358 And then, we just take powers of the diagonal matrix.