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## Algebra (all content)

### Course: Algebra (all content)>Unit 20

Lesson 10: Matrices as transformations

# Matrix from visual representation of transformation

Learn how to determine the transformation matrix that has a given effect that is described visually.

## Warmup example

Let's practice encoding linear transformations as matrices, as described in the previous article. For instance, suppose we want to find a matrix which corresponds with a 90${}^{\circ }$ rotation.
The first column of the matrix tells us where the vector $\left[\begin{array}{c}1\\ 0\end{array}\right]$ goes, and—looking at the animation—we see that this vector lands on $\left[\begin{array}{c}0\\ 1\end{array}\right]$. Based on this knowledge, we start filling in our matrix like this:
$\left[\begin{array}{cc}0& ?\\ 1& ?\end{array}\right]$
For the second column, we ask where the vector $\left[\begin{array}{c}0\\ 1\end{array}\right]$ lands. Rotating this upward facing vector 90${}^{\circ }$ yields a leftward facing arrow—i.e., the vector $\left[\begin{array}{c}-1\\ 0\end{array}\right]$—so we can finish writing our matrix as $\left[\begin{array}{cc}0& -1\\ 1& 0\end{array}\right]$.
Now you try!

## Practice problems

Problem 1
What matrix corresponds with the following transformation?

Problem 2
What matrix corresponds with the following transformation?

Problem 3
What matrix corresponds with the following transformation?

Problem 4
What matrix corresponds with the following transformation?

Problem 5
What matrix corresponds with the following transformation?

Problem 6
What matrix corresponds with the following transformation?

## Want to join the conversation?

• so because you are identifying the transformation that occurs for 1 0 and 0 1 you are also identifying the vector that transforms all other points?
• That's right. For a linear transformation, you only need to specify what it does to a set of points that represents a basis of the space (like (1,0) and (0,1) is a basis of two-dimensional space). You can calculate any of the other values from that.
• This topic seems super interesting.
• Yep. I'm also exited
• You lost me on this one.
• The answer to each question can be easily found by looking at where the green and red basis vectors end up after each transformation, and encoding where they do in a 2X2 matrix as follows:
_ _
| green-x-coord red-x-coord |
| |
| green-y-coord red-y-coord |
|_ _|

It is really that simple. The green vector is the vector that defines the x-axis and has length 1, labeled "i" with a little caret on top, pronounced "eye-hat," while the red arrow in the vector that defines the y-axis and has length 1 as well, labeled "j" with a little caret on top, pronounced "jay-hat." The 2x2 matrix has two columns: the left column says where i-hat ends up after the transformation, and the right column says where j-hat does.

I hope that helps a bit. :-)
• Hi everyone,

after seeing the videos, I understand how to do a transformation that corresponds to a 90° rotation or a rotation multiple of 90°.

Now I am wondering what might be a rule to translate a transformation expressed as a matrix into a rotaion expressed in degree.

My intuition says that
1.) I would need to rotate the x and the y component by the same degree if I do not wish to somehow squeez my vector/figure/coordinate system.
2.) I would probably able to calculate the matrix that corresponds to a rotation of given degrees using the unity circle and the sine etc functions.

So I would need to calculate the angle phi for any given point P in the coordinate system, add the angle I would like to rotate it, and then calculate the respective x,y coordinates.

Is this reasoning correct?

And if I want to resize the object (as in the tasks earlier), I assume, I could alter the magnitude of the vectors pointing to each corner of the object. But what would I need to do to alter the size of object and altering the distance to the origin at the same time, e.g. enlarge a triangle and at the same time get it closer to the origin? Would that still be a linear transformation?

• Your assumption is correct but you don't need to retrieve the original angle of the rotated point first. The trigonometric identities tell you that x'=x⋅cos(θ)-y⋅sin(θ) and y'=x⋅sin(θ)+y⋅cos(θ). And since matrices are all about forming i values each formed from j others values in different proportions, the matrix come by itself :

+cos θ   -sin θ+sin θ   +cos θ

Since θ is a given constant, sin θ and cos θ are also constants, so you can replace each every expression in the matrix by its numerical equivalent if you wish to do so.

As regards your last question, to translate your figure, you indeed need to ADD a matrix to your result, which is no longer linear. However, it remains possible to perform an affine transformation in a single operation (multiplication of a square matrix), by adding one more dimension : https://en.wikipedia.org/wiki/Transformation_matrix#Affine_transformations
• I honestly think 3b1b's Essence of Linear Algebra is one of the most enlightening things you can find on the internet.
• You are right, but to be honest, combining both Khacacademy's problem sets and 3b1b's video helped me a lot to grasp visualization more deeply.
• Maybe I don't quite catch it, but why in every sample the vector start point is [0,0]?
And what will be if we will operate with more complicated vectors, what the transformation matrix will look like?
• Vectors don't have a starting point. Vectors only have a magnitude and a direction. We just draw them as arrows, but the starting point of the arrow does not matter.
• So we are saying that the result of every linear transformation of the vector zero is the vector zero? In order to be a Linear Transformation?
• Yes, taking the transformation of the zero vector for any linear transformation always gives back the zero vector in the other space.
• Are matrix transformations always represented as a 2x2 matrix? The textbook I'm using suggests that but I want to be sure.
• No. Any matrix of any size mxn is a matrix transformation.
If you are specifically referring to transformations that apply rotation, projection, reflection, etc. in the xy plane, then yes, those only come from 2x2 matrices.