1 00:00:06,739 --> 00:00:07,530 ANA RITA PIRES: Hi. 2 00:00:07,530 --> 00:00:09,132 Welcome back to recitation. 3 00:00:09,132 --> 00:00:11,340 In lecture, you've been learning about the properties 4 00:00:11,340 --> 00:00:12,600 of determinants. 5 00:00:12,600 --> 00:00:15,040 To remember, there were three main properties, 6 00:00:15,040 --> 00:00:17,680 and then seven more that fall out of those three. 7 00:00:17,680 --> 00:00:19,309 I'll tell you what these three were. 8 00:00:19,309 --> 00:00:21,350 The first one was the determinant of the identity 9 00:00:21,350 --> 00:00:23,850 matrix is always equal to 1. 10 00:00:23,850 --> 00:00:25,780 If you switch two rows in a matrix, 11 00:00:25,780 --> 00:00:27,730 the determinant switches sign. 12 00:00:27,730 --> 00:00:30,770 And the determinant is a function 13 00:00:30,770 --> 00:00:34,630 of each-- it's a linear function of each row separately. 14 00:00:34,630 --> 00:00:36,780 And there's seven more. 15 00:00:36,780 --> 00:00:38,430 We'll use them here. 16 00:00:38,430 --> 00:00:41,630 Today's problem is about finding the determinants of matrices 17 00:00:41,630 --> 00:00:43,380 by using these properties. 18 00:00:43,380 --> 00:00:46,640 So here you have four matrices. 19 00:00:46,640 --> 00:00:50,600 A has lots of 100's, 200's, and 300's numbers. 20 00:00:50,600 --> 00:00:54,180 B is called a Vandermonde matrix. 21 00:00:54,180 --> 00:00:57,560 It has a very nice structure with 1's, and then a, b, c; 22 00:00:57,560 --> 00:00:59,130 a squared, b squared, c squared. 23 00:00:59,130 --> 00:01:00,830 It can be bigger, and you'll just 24 00:01:00,830 --> 00:01:04,750 have cubes and more letters down here. 25 00:01:04,750 --> 00:01:07,380 C is given by the product of these two, 26 00:01:07,380 --> 00:01:11,510 and D is this matrix. 27 00:01:11,510 --> 00:01:12,050 Good luck. 28 00:01:12,050 --> 00:01:14,669 Hit pause, work on them, and when you're ready, come back 29 00:01:14,669 --> 00:01:15,960 and I'll show you how I did it. 30 00:01:25,290 --> 00:01:26,440 Did you get some? 31 00:01:26,440 --> 00:01:28,910 OK, let's do it. 32 00:01:28,910 --> 00:01:36,770 Starting with matrix A. I have lots of big numbers. 33 00:01:41,400 --> 00:01:44,960 I suggest that we do a little bit of elimination, 34 00:01:44,960 --> 00:01:47,250 because as you know, doing elimination steps, 35 00:01:47,250 --> 00:01:49,870 except for permuting rows, doesn't change 36 00:01:49,870 --> 00:01:52,100 the determinant of the matrix. 37 00:01:52,100 --> 00:02:04,660 So let's do determinant of A is equal to-- 101, 201, 301. 38 00:02:04,660 --> 00:02:08,580 Then if I subtract off the first row from the second one, 39 00:02:08,580 --> 00:02:12,000 I'll get 1, 1, 1, which is very convenient. 40 00:02:12,000 --> 00:02:15,490 And actually, if you subtract the second row 41 00:02:15,490 --> 00:02:21,480 from the third one, you'll get 1, 1, 1. 42 00:02:21,480 --> 00:02:23,690 Here's the property of the determinant: 43 00:02:23,690 --> 00:02:25,990 if you have two equal rows on your matrix, 44 00:02:25,990 --> 00:02:28,105 the determinant is automatically equal to zero. 45 00:02:30,750 --> 00:02:31,310 All right. 46 00:02:31,310 --> 00:02:32,420 All done with one of them. 47 00:02:32,420 --> 00:02:35,660 Let's work on the second one. 48 00:02:35,660 --> 00:02:42,940 The determinant of B. Well, let's try elimination again. 49 00:02:42,940 --> 00:02:47,310 1, a, a squared. 50 00:02:47,310 --> 00:02:53,700 1 minus 1 is 0, b minus a, and b squared minus a squared. 51 00:02:53,700 --> 00:02:56,060 b squared minus a squared-- let me factor that 52 00:02:56,060 --> 00:03:01,420 into b minus a, b plus a, which will be very 53 00:03:01,420 --> 00:03:03,440 convenient in the next step. 54 00:03:03,440 --> 00:03:06,290 And then I'm going to subtract the first row again 55 00:03:06,290 --> 00:03:07,935 from the third one. 56 00:03:07,935 --> 00:03:10,430 I'll get 0, c minus a. 57 00:03:10,430 --> 00:03:13,550 And again, I'll get c squared minus a squared, c 58 00:03:13,550 --> 00:03:16,742 minus a, c plus a. 59 00:03:23,350 --> 00:03:30,680 Let's use that third property that was the determinant 60 00:03:30,680 --> 00:03:34,270 is linear on each row separately. 61 00:03:34,270 --> 00:03:38,320 So what I'm going to do is, see this factor of b minus a? 62 00:03:38,320 --> 00:03:41,170 It shows up in every entry of this row. 63 00:03:41,170 --> 00:03:45,900 Well it's a 0, so it's the zero multiple of b minus a. 64 00:03:45,900 --> 00:03:48,620 So I'm going to pull out this factor of b minus a, 65 00:03:48,620 --> 00:03:53,710 and this row is going to become 0, 1, b plus a. 66 00:03:53,710 --> 00:03:57,100 I will also, in the same step, do the same thing 67 00:03:57,100 --> 00:03:58,230 with the third row. 68 00:03:58,230 --> 00:04:02,110 I'll pull out a factor of c minus a, 69 00:04:02,110 --> 00:04:08,260 and it will become 0, 1, c plus a. 70 00:04:08,260 --> 00:04:11,540 Here's one factor from the second row. 71 00:04:11,540 --> 00:04:14,640 Another factor from the third row. 72 00:04:14,640 --> 00:04:18,420 And 1, a, a squared. 73 00:04:18,420 --> 00:04:22,200 0, 1, b plus a. 74 00:04:22,200 --> 00:04:25,410 0, 1, c plus a. 75 00:04:28,460 --> 00:04:29,760 Now what? 76 00:04:29,760 --> 00:04:32,250 Well remember, you know how to do 77 00:04:32,250 --> 00:04:34,670 the determinant of upper triangular matrices 78 00:04:34,670 --> 00:04:37,760 because all that you do is multiply the pivots. 79 00:04:37,760 --> 00:04:40,610 This is almost upper triangular, except there's a 1 over here. 80 00:04:40,610 --> 00:04:42,410 So let's do another elimination step. 81 00:04:45,250 --> 00:04:50,170 b minus a, c minus a. 82 00:04:50,170 --> 00:05:00,850 1, a, a squared; 0, 1, b plus a; 0, 0, c plus a minus 83 00:05:00,850 --> 00:05:06,400 b plus a is c minus b. 84 00:05:06,400 --> 00:05:10,540 So the determinant of this matrix is now 1 times 1 times 85 00:05:10,540 --> 00:05:12,390 c minus b. 86 00:05:12,390 --> 00:05:20,670 So we get b minus a, c minus a, c minus b, 87 00:05:20,670 --> 00:05:23,120 which has a really nice formula. 88 00:05:23,120 --> 00:05:25,540 This is called the Vandermonde determinant. 89 00:05:25,540 --> 00:05:28,430 It's always like this, even if your matrix is bigger than 3 90 00:05:28,430 --> 00:05:32,580 by 3, if it's 4 by 4, or 5 by 5 and so on, you just 91 00:05:32,580 --> 00:05:34,770 have more differences of all the letters 92 00:05:34,770 --> 00:05:37,870 that show up in your matrix. 93 00:05:37,870 --> 00:05:41,190 On to matrix number 3. 94 00:05:41,190 --> 00:05:53,990 C equals [1, 2, 3] [1, -4, 5]. 95 00:05:53,990 --> 00:05:58,010 How did you get the determinant for this one? 96 00:05:58,010 --> 00:06:02,820 Well remember, this is a rank one matrix 97 00:06:02,820 --> 00:06:05,900 because it's a column vector times a row vector. 98 00:06:05,900 --> 00:06:08,850 So if you write out what the matrix is it'll be a 3 99 00:06:08,850 --> 00:06:13,140 by 3 matrix where we can think about it this way. 100 00:06:13,140 --> 00:06:17,210 The first row will be 1 times [1, -4, 5]. 101 00:06:17,210 --> 00:06:20,260 The second row will be 2 times those numbers. 102 00:06:20,260 --> 00:06:23,870 And the third row will be three times those numbers. 103 00:06:23,870 --> 00:06:30,960 So all the rows are going to be linearly dependent, 104 00:06:30,960 --> 00:06:34,492 or another way of saying the matrix is singular. 105 00:06:34,492 --> 00:06:36,560 When the matrix is singular, the determinant 106 00:06:36,560 --> 00:06:37,750 is always equal to 0. 107 00:06:37,750 --> 00:06:40,580 That's also one of the properties. 108 00:06:40,580 --> 00:06:42,490 C is 0. 109 00:06:45,930 --> 00:06:48,240 Onto the next matrix. 110 00:06:48,240 --> 00:06:49,690 Last one. 111 00:06:49,690 --> 00:06:56,660 D is equal to 0, 0, 0. 112 00:06:56,660 --> 00:07:06,750 1, -1, 3, -3, 4, -4. 113 00:07:06,750 --> 00:07:10,800 Maybe you didn't get that from just looking at the matrix 114 00:07:10,800 --> 00:07:13,105 the first time, but did you see how I wrote it down? 115 00:07:13,105 --> 00:07:15,180 It has 0's down the diagonal. 116 00:07:15,180 --> 00:07:20,130 And then for each entry, I have minus that entry. 117 00:07:20,130 --> 00:07:22,660 That means that this matrix is skew symmetric. 118 00:07:22,660 --> 00:07:25,940 What that means is if you do D transpose, 119 00:07:25,940 --> 00:07:27,590 it will not be equal to D, but it 120 00:07:27,590 --> 00:07:37,430 will be equal to minus D. D transpose is equal to minus D. 121 00:07:37,430 --> 00:07:39,890 Now, what does this give us for a determinant? 122 00:07:39,890 --> 00:07:43,640 Well if these two matrices are the same matrix, 123 00:07:43,640 --> 00:07:46,830 this determinant is equal to that determinant. 124 00:07:46,830 --> 00:07:49,750 One of the properties is that the determinant 125 00:07:49,750 --> 00:07:53,780 of a transpose of the matrix is equal to the determinant 126 00:07:53,780 --> 00:07:56,100 of the original matrix. 127 00:07:56,100 --> 00:07:57,460 How about this side? 128 00:07:57,460 --> 00:08:07,350 Well, first temptation would be to just write that. 129 00:08:07,350 --> 00:08:09,330 Is that always true? 130 00:08:09,330 --> 00:08:09,870 No. 131 00:08:09,870 --> 00:08:14,120 The determinant is linear on each row separately. 132 00:08:14,120 --> 00:08:16,650 That means that you can't pull out the factor that 133 00:08:16,650 --> 00:08:18,010 is multiplying the matrix. 134 00:08:18,010 --> 00:08:21,330 You have to pull it out once for each row of the matrix. 135 00:08:21,330 --> 00:08:31,560 So what I should have written was 136 00:08:31,560 --> 00:08:34,179 -1, that's my factor, minus. 137 00:08:34,179 --> 00:08:35,370 How many rows do I have? 138 00:08:35,370 --> 00:08:37,490 One, two, three. 139 00:08:37,490 --> 00:08:39,690 Pull it out once for each row. 140 00:08:39,690 --> 00:08:44,990 Times the determinant of D. Well fortunately, -1 to the third 141 00:08:44,990 --> 00:08:48,370 is simply equal to -1. 142 00:08:48,370 --> 00:08:50,860 So here we go. 143 00:08:50,860 --> 00:08:54,490 It was correct, in fact. 144 00:08:54,490 --> 00:08:57,190 We have determinant of D is equal to minus determinant 145 00:08:57,190 --> 00:08:59,380 of D. What is the only number that 146 00:08:59,380 --> 00:09:01,970 is equal to minus that number? 147 00:09:01,970 --> 00:09:03,580 0. 148 00:09:03,580 --> 00:09:08,991 Determinant of D is equal to 0 again. 149 00:09:08,991 --> 00:09:11,830 Let me ask you one last question. 150 00:09:11,830 --> 00:09:14,330 Is it true that all skew symmetric matrices have 151 00:09:14,330 --> 00:09:16,355 the determinant equal to 0? 152 00:09:16,355 --> 00:09:18,190 It was true for this one. 153 00:09:18,190 --> 00:09:21,010 Is it true in every case? 154 00:09:21,010 --> 00:09:24,490 Well, the key factor here was that I 155 00:09:24,490 --> 00:09:29,260 had -1 to the third power and I got a minus sign here, 156 00:09:29,260 --> 00:09:32,215 determinant of D is equal to minus determinant of D. 157 00:09:32,215 --> 00:09:34,920 What if this number had been an even number? 158 00:09:34,920 --> 00:09:37,500 Then I would just have the determinant of D 159 00:09:37,500 --> 00:09:42,350 is equal minus 1 to an even number, D. That's 1. 160 00:09:42,350 --> 00:09:46,420 So I would have determinant of D is equal to determinant of D. 161 00:09:46,420 --> 00:09:48,380 There's nothing I can say about that number. 162 00:09:48,380 --> 00:09:51,505 It can be your favorite number, not necessarily 0. 163 00:09:51,505 --> 00:09:52,510 All right. 164 00:09:52,510 --> 00:09:53,500 We're done for today. 165 00:09:53,500 --> 00:09:54,848 Thank you.