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# Example using orthogonal change-of-basis matrix to find transformation matrix

Example using orthogonal change-of-basis matrix to find transformation matrix. Created by Sal Khan.

## Want to join the conversation?

• at , i dont understand why T(v3) is -v3. Since it is perpendicular to the plane spanned by v1 and v2..shouldnt the projection of v3 on the plane be 0?
• He mixed projection with reflection. If it were the case for reflection, then T(v3) has to be equal to -v3 which he mentions at .
• Can we always consider that v1 equals v1, v2 equals v2 and v3 equals -v3 or is it just a convention of this example?
• How v1, v2, and v3 change describes the result of a transformation. In this case, v3 => -v3, which means that a transformed vector will be flipped around the plane formed by v1 and v2. If Sal used v3 => 2 * v3, then the transformation would double the distance between a vector and a plane formed by v1 and v2.
• At , why is it that v1 = 1*v1 + 0*v2 + 0*v3 as a vector in the new basis B={v1,v2,v3}?
• All vectors in that R^3 can be represented as linear combinations of v1, v2, and v3. That is, all vectors in R^3 can be expressed as x = c1v1 + c2v2 + c3v3, where c1, c2, and c3 are scaling factors. If x = v1, what are the values for c1, c2, and c3?
• Around he is doing the transformations of the vectors. What exactly are the transformations of? The mirror image perpendicular to the plane?
(1 vote)
• Yes, it is the mirror image of the vector through the plane.
• In the earlier videos we established that if C is the change of basis matrix, Xb is a vector X with respect to the basis B and X is a vector with respect to the standard coordinates (our basis), then C * Xb = X.
inv(C) is then our basis' coordinates in basis B's coordinate system. Thus, inv(C) * X = Xb

What I can't understand here is how we have assumed inv(C) to be our basis ( v1, v2, v3's) coordinates with respect to the plane's coordinate system (or plane's basis)? Why does inv(C) * X give us a vector in the plane's basis and when exactly did we establish that?
(1 vote)
• We established that at the beginning when drawing the 3 orthonormal vectors (2 orthogonal ones on the plane and a third blue one orthogonal to both).

Also, inv(C) isn't our basis. (C) is our basis.
(1 vote)
• In order for him to be able to deduce that Inverse(C) = Transpose(C), the matrix property A * Inverse(A) = Inverse(A) * A = I, must hold, right?
(1 vote)
• At Sal takes for granted that [-v3]_B is the same as -[v3]_B.
But have we proved that?
Does a scaled up vector in one basis S always scale up by the same amount in another basis B?
(1 vote)
• I think sal made a mistake in the final multiplication

## Video transcript

We learned in the last video that if I have some matrix-- let's call it C, Let's say it's an n by k matrix. And all of matrix C's columns-- let's say this is column 1, column 2, all the way through column k-- if each of these columns vectors form an orthonormal set-- so, let me write this-- columns are orthonormal set, or the set of columns are an orthonormal set, we showed in the last video that if I multiply C transpose times C, I get the identity matrix. I get the k by k identity matrix. I get the identity matrix of k, because it's going to be k by n, times n by k. And we saw how everything else cancels out, except right along the diagonal. And then we were just multiplying the vectors times themselves, and they're all unit vectors. So you get the square of their lengths and you get 1's down the diagonal. We saw that in the last video. Now what happens if k is equal to n? So what happens if k is equal to n? Well, then this guy is going to be a square matrix. And if he's a square matrix, what else do we know about it? Well, we know that these guys form an orthonormal set. We saw, I think it was two or three videos ago, that that means that the columns are linearly independent. So we also know that we have linearly independent columns. And we've seen this many, many times before. If we have a square matrix with linearly independent columns, it means that C is invertible. It also means, if we have n of these, it also means that these columns, or these column vectors, if they were in a set, then that set would be a basis for Rn. So I'll just write that on the side, just because it, you know, it also would mean that, you know, c1, c2, all the way through cn, because we're saying n is equal to k, would be a basis for Rn. But we'll leave that on the side for now. It's just worth pointing out. Now if C is invertible, what does that mean? That means that there's some C inverse that exists, such that C inverse times C is equal to the n by n identity matrix. In this case k and n are equal to the same thing, so let's just replace that n, that k with an n. Now these two statements look very similar. If the columns are an orthonormal set, and it's an n by n matrix, then we learned in the last video that C transpose times C is equal to the n by n identity matrix. And we also know, since it's a square matrix with linearly independent columns, that C is invertible. So, in this situation where you have n by n matrix whose columns form an orthonormal set, then, well, C inverse must be equal to C transpose. I mean, you can just say, look, something times C is equal to the identity matrix. Something times C is the identity matrix. These somethings must be the same thing. Then C inverse is equal to C transpose. And that is a huge time saver. If we can assume that these, this is a square matrix with an orthonormal set. Because finding an inverse of a matrix is painful, especially if you get, you know, beyond, you know, n equals 3. If you have to find the inverse of a, I don't know, 10 by 10 matrix, that would take you forever. But to find the transpose of a 10 by 10 matrix, a lot easier. You just swap the rows and the columns. So let's see if we can apply this newly found insight to simplify some problems that we've tackled in the past. So I have up here some vectors, v1, v2, v3. And let's say that we-- let me copy and paste these so I can use them below, because I think that might be more useful. Let me copy them and then let me paste them and put them right down here. We have these vectors here. I'll leave it for you to verify that they all are unit vectors, and that taken together they form an orthonormal set, or that they would be an orthonormal basis for R3. And however you want to view it, you could verify that on your own time. But what I want to do in this video is construct an interesting linear transformation in R3. So let's say I have the plane formed by v1 and v2. v1 and v2 are going to be orthogonal to each other as well. This is an orthonormal set. So v1 and v2 are orthogonal. Let's say that this is v1-- and I'm not drawing what they actually are. I'm just kind of doing a abstract visual representation of it. v1, v2, they're going to span some plane. So let me draw the plane as best as I can. So if you take all of the linear combinations of v1 and v2, it's going to be a plane in R3. So that's my plane, and we know it goes off in every direction infinitely. But this is what it looks like. We're going to have the 0 vector in it. And let me just call my plane, this plane, let me call it, it's going to be some subspace V in R3, which is equal to the span of v1 and v2. Now v3 is orthogonal to both of these guys. So let me just draw v3 here for fun. So maybe v3 looks something like this. It's orthogonal to both of these vectors, so it's going to be orthogonal to all of the linear combinations of these guys. So if you view that the plane is V, then the line spanned by v3, if you imagine it that way, would be the orthogonal complement of your plane. But let me just draw v3 here. v3 would be a member of the orthogonal complement, or the orthogonal complement would be the line spanned by v3. That's all a little bit of review. Now let's talk about the linear transformation that I want to construct in this video. I want to construct a linear transformation in R3-- remember, we're dealing with R3 right here-- that essentially reflects any vector over this plane. So let me draw some indicative examples. And I hope we can visualize this well. So let's say I have a vector that looks like that. So let's call that vector x. And let's say, you know, let's say it looks something like this. It's above the plane. It kind of jumps out. It's not in my subspace. But I want the transformation of x to be the mirror image below this. So, you know, if you imagine that this plane is somewhat transluscent, it would be down here. My vector x would look something like this. This would be the transformation of x that I want to generate. If I have a vector-- let me pick a color that I haven't used before-- if I have a vector that looks like this. Then its transformation is going to go below the plane and just be the mirror image. This is going to be the mirror image, just like that. You get the idea. Now, what would be, just to understand our transformation a little bit better, what would be the transformation of v1? Well v1 is in the plane, so its mirror image isn't going to change. So the transformation of v1 is just going to be v1. What's the transformation of v2? Well that's also in the plane. So the transformation of v2 is just going to be v2. And what's the transformation of v3? Well v3 is directly orthogonal to the plane. It kind of just pops straight up out of the plane. So if you want to get its mirror image, you would literally take the negative of v3. It would be the negative of v3. And I do it straight up. We don't know necessarily whether-- v3 clearly does not go straight up. I just drew it, you know this plane, I just drew it relative to this plane. This plane might be more tilted than I've drawn it. But anyway, so the transformation of v3 will be equal to minus v3. Now this seems like a fairly tough transformation to construct. You know, we could try to apply the transformation to our standard basis vectors in R3 like we've done in the past. But that seems really complicated, a lot of trigonometry. We would have to figure out the inclination of this plane, and what we're doing to it. It would just be hard to visualize. So you probably have a sense that maybe this transformation will be much easier to describe if we change our basis. Let's say this is our standard basis. And our standard basis, our transformation, we would multiply it times some matrix x-- sorry, by some matrix A-- to get your transformation of x. Now, we're saying that this is hard to find, because it's hard to figure out what the transformation is when you apply to just your standard basis vectors. So what I'm going to do is try to construct some basis, so that when I represent x in that new basis, if I multiply x times the inverse of our change of basis matrix, then I'm going to get x in the new basis coordinates. And maybe it'll be easier to find the D in this new basis, and then we'll get the B representation of the transformation of x. And then we can multiply that times C. So if we're able to figure out D, what we can do is, look, we know that the transformation of x is going to be able to A times x. We saw that a long time ago, that any linear transformation could be represented as a matrix vector product. But let's say that this is hard to find. This is hard. Then we can go the other way around this. We could say that the transformation of x is equal to you take x, first you multiply it by C inverse to get the B version of x. So first you multiply it by C inverse. Then you multiply it by D, to get the B version of the transformation of x. So then you multiply it by D. And then you multiply that by C to go back to our standard basis for the transformation. So then you go multiply it by C. And we've seen this formula before. So if we can find a nice basis where D is easy to figure out, then we can multiply it this way, times the change in basis matrix and its inverse, and we'll get our A. Because this thing has to be the same thing as that thing right there. And even more, if we pick an orthonormal basis, if B is an orthonormal basis, with three vectors, right, then C will be invertible. Well, we already know if it's an orthonormal basis, we know that C transpose C is equal to the identity matrix. And we also know that C is invertible, if, you know, C is essentially a 3 by 3 matrix. C inverse exists. And in the beginning of this video, we said, well that means that C transpose is going to be equal to C Inverse, because this is just acting like an inverse right there. And then, if C is an n by n matrix, or in this case we're dealing with a 3 by 3 matrix, we're dealing with R3, then it simplifies to A will be equal to C times D, times the C transpose, which is much easier to find, much easier to find than trying to invert a 3 by 3 matrix. So let's see if we can do this effectively. So what would be a good basis? Well I think maybe a natural one would be to use v1, v2, and v3, because these basis vectors, it's very easy to figure out what their transformations are. So let's write that down. So I'm going to make my basis that I'm going to change to is going to be v1, v2, and v3. So just to make sure we understand what we're doing, each of these vectors, what do they look like in my new basis? Well v1 is equal to 1 times v1, plus 0 times v2, plus 0 times v3. So v1, in my new basis, where v1 is the first basis vector, is just going to be equal to 1, 0, 0. Same argument. v2, what is it going to be equal to in my new basis? I don't even have to, I think you get the idea. Let me just write it over here. v2 in my new basis is just going to be 0 times v1-- remember, these numbers, these coordinates are just the coefficients on my basis vectors-- it's going to be 0 times v1, plus 1 times v2, plus 0, times v3. And then finally, v3 in my new basis is just going to be 0 times v1, plus 0 times v2, plus 1 times v3, This is almost trivially easy. Now, what is my change of basis matrix going to be? Well I'm going to have, well, I'll save that for later. But my change of basis matrix is just going to be a matrix with these guys as the columns. And of course its inverse is going to be the transpose of that. But we'll save that for a little bit. Now, how can we figure out D? Let's write D. D is going to have three columns. So d1, d2, d3. It's still a mapping from a three-dimensional, I guess we could call it a three-dimensional matrix vector to a three-dimensional vector. Each of these are members of R3. So what is the transformation of v1 represented in B coordinates? That's going to be equal to D times the representation of v1 in B coordinates, which is equal to d1, d2, d3 times this guy, right? This is the representation of v1 in B coordinates. So times 1, 0, 0. Well that just equals d1. We've seen this in the past. So if we want to figure out what d1 is, it's just the transformation of v1 in B coordinates. Now we could use that exact same argument-- let me shift to the left a little bit more. I was running out of space on the right. So the transformation of v2 in B coordinates is equal to D, which is d1, d2, d3 times the B version of v2, or v2 represented in B coordinates. So that is v2 represented in B coordinates is 0, 1, 0. So that's going to be equal to d2. And finally, let's just complete it. The transformation of v3, represented in B coordinates is going to be equal to d1, d2, d3 times v3 represented in B coordinates. So times 0, 0, 1. So it's going to be 0 times d1, plus 0 times d2, plus 1 times d3, is equal to d3. And this is kind of a re-proving that we can find the columns of D, by essentially finding the B versions of these transformations. So D, we can rewrite D is going to be equal to, with the first column it's just going to be this. So it's the transformation of v1 in B coordinates. The second column is this. All right, d2 is that. So it's the transformation of v2 in B coordinates. And third column is that, the transformation of v3 in B coordinates. Just like that. So let's see if we can find these. This, hopefully, shouldn't be too difficult. So we saw it up here. We wrote what the transformation of v1, v2, and v3 are. Now we just have to find out what they are in B coordinates. So let me rewrite, let me copy and paste this down here, because this might be nice to look at. So we already found that. I'll paste it down here. So we have that. We've already found that. So we just have to find the B coordinate representation of them. So the B coordinate representation of the transformation of v1-- actually, I think we, well I'll write it out. This becomes a little bit tedious. That's equivalent to the B coordinate representation of v1, which is just 1, 0, 0. So let me write D here. D is equal to, the first column is 1, 0, 0. Now what the second column? Well the transformation of v2, B representation is going to be just v2's B representation, or B coordinates, which is equal to 0, 1, 0 zero, 0, 1, 0, and then one left. This one's a little interesting one. The B representation of the transformation of v3-- and remember, v3 was the one that's not in the plane-- that is equal to-- remember, the transformation is minus v3. Flip that, because we're taking the mirror image with respect to that plane. So it would be the B representation of minus v3. Well minus v3 is just 0 times v1, plus 0 times v2, minus 1 times v3. Those are out basis vectors, so our third column is going to be 0, 0, minus 1. So we figured out our D transformation. That was pretty straightforward. D right here is this matrix that we just figured out. So to figure out A, we can apply this formula right there with our change of basis matrix. Actually let me copy all that stuff. This'll be useful. just let me copy all of this. Copy, then we paste it down here, now that we have our D. All right, we have all of this stuff here. And so let's figure out what A. Is So to do that, first we have to figure out our change of basis. So I'll just write it all out like this. Let me clear this out a little bit, clean things up. All I need is this. I don't need any of this really. So let me clean all of that up. So let's figure out what A is. A is equal to our change of basis matrix. Well our change of basis matrix is just the matrix with these guys as the columns. Well let me just take out the 1/3. So C is 1/3, times 2 minus 2, 1, 2, 1 minus 2, and then we have 1, 2, 2, right? That right there is A, and then we're going to multiply that times-- or this right here is our change of basis matrix, that is C-- and we're going to multiply that times D, which is 1, 0, 0, 0, 1, 0, and then 0, 0, minus 1. So it almost looks like the identity matrix, but we flipped our third vector, and that's why we got a minus 1 there. And then we have C inverse, but because C was a square matrix with orthonormal columns, we know that C inverse is the same thing is C transpose. So let me write that here. So let me write this here. So it's just going to be the transpose of this thing. So I'll write another 1/3 out here, just to simplify things. So times 1/3, times the transpose of this character, so we have 2, 2, 1, so we're going to have 2, 2, 1. We have minus 2, 1, 2, so we have minus 2, 1, 2. We have 1 minus 2, 2, so we have 1 minus 2 and 2. And this right here, this is C inverse, which is equal to C transpose, because C is an invertible matrix, or a square matrix, with orthonormal columns. So what's this going to be equal to? Well let's just take, I don't know, first let's just take-- let me write this, this was D-- so let's just take this product first. We'll worry about the 1/3's later. And now I can erase this. I can use this real estate. Let me erase that. So let me turn my pen tool back on. All right. So let's take this product right there. So A is equal to, let's take the 1/3's, it's going to be equal to 1/9 times-- now this is going to be another 3 by 3 matrix. So, we're going to have 2, 2, 1, times 1, 0 0. Or we're going to dot 2, 2, 1 with 1, 0, 0. So the only term that's going to be non-zero is the 2 times the 1. So that's going to be 2. Then we're going to dot 2, 2, 1 with 0, 1, 0, right, for the second column. The only non-zero term is going to be the 2, the middle 2. Then you get 2, 2, 1 dot 0, 0, minus 1. So that's going to be, the only non-zero term is this last one which is going to be negated, so it's going to be minus 1. This isn't too bad. I simplified it, but this is almost the identity matrix. Then we have this guy. We have minus 2, 1, 2 dotted this guy, only this guy kind of survived that dot product, minus 2, then you take this guy dot this guy, only this guy survives. And then you take this guy dot that guy. This guy gets negative. And he's the only survivor. I think you see what's happening. These rows stay the same, but the third term becomes negative. Let's do it again. So you take this guy. You take the dot product of this column, this row vector with this column vector. Only this guy survives. Then this row with this column, you have the minus 2. And then this row with that column, only the 2 survives, but it's times minus 1, so it becomes a minus 2. And then we have this vector out here. Remember, we took 1/3's, we multiplied them and we got 1/9. And then we have 2 minus 2, 1, 2, 1, minus 2, and then 1, 2, 2. And this'll be a little bit more painful to multiply, but I think we can pull it off. Because this is the home stretch. So A, our transformation matrix for that, you know, strange transformation where we're reflecting over the plane in R3 is going to be equal to 1/9 times-- it's going to be a 3 by 3 matrix. So it's going to be 2 times 2, which is 4. Let me write this down. Oh, that's going to be too onerous. So 2 times 2, which is 4, plus 2 times 2, which is 4, and then plus minus 1, right? So it's going to be 4 plus 4, minus 1, which is 7. 4 plus 4 minus 1 is 7. So we're going to multiply this guy times this column. So 2 times minus 2, is minus 4, plus 2, is minus 2. Minus 2 is minus 4. Then we're going to have 2 times 1 is 2, minus 4, which is minus 2, and then minus 2, which is minus 4. Not so painful. OK, now in the second row, we're going to have minus 2 times minus 2, is minus 4, plus 2, which is minus 2, minus 2, which is minus 4. Then we're going to have minus 2 times minus 2, which is 4, plus 1, which is 5, minus 4. Let me make sure I got that right. We're going to have minus 2 times minus 2, which is positive 4, plus 1, times 1, which is minus 3, plus minus 2 times minus 2, which is which is minus 4. And so we are going to get-- and I'm confusing myself-- I'm going to get-- oh no, this thing is going to get very confusing. It's straightforward. Minus 2 times minus 2 is 4, plus 1 is 5, minus 4 is 1. And then we get minus 2 minus 2, which is minus 4, minus 4, which is minus 8. Home stretch. This is the painful part. OK, then you have 2 minus 4, which is minus 2, minus 2, which is minus 4, minus 4-- all right, now-- minus 2 minus 2, minus 4, and that's minus 8. And then finally you have 1 plus 4, minus 4, which is 1. And if I haven't made any careless mistakes, we're done. We now know that our transformation that reflects-- now that did all this fancy stuff-- our transformation that reflects across this plane that's spanned by these characters right there, it can be represented by this matrix. So our transformation, applied to x in standard coordinates, is just equal to A times x, where A is equal to this matrix. We can multiply the 1/9 out, but that will just make it look more confusing. But this would've been really hard to figure out on our own, to figure out that we had to put a 7 and a minus 4 or to even apply the transformation to the standard basis vectors, which you normally do to figure out this matrix. Instead, we changed our basis to kind of a very natural orthonormal basis. And the fact that it was orthonormal made it very easy to find the inverse of our change-of-basis matrix. Anyway, hopefully you found that useful.