It’s been over a decade since I’ve taught a class where I’ve felt the freedom to really explore transformations with a strong matrix thread. Whether due to curricular pressures, lack of time, or some other reason, I realized I had drifted away from some nice connections when I recently read Jonathan Dick’s and Maria Childrey’s *Enhancing Understanding of Transformation Matrices* in the April, 2012 *Mathematics Teacher* (abstract and complete article here).

Their approach was okay, but I was struck by the absence of a beautiful idea I believe I learned at a UCSMP conference in the early 1990s. Further, today’s Common Core State Standards for Mathematics explicitly call for students to “*Work with 2×2 matrices as transformations of the plane, and interpret the absolute value of the determinant in terms of area*” (see Standard NV-M 12 on page 61 of the CCSSM here). I’m going to take a couple posts to unpack this standard and describe the pretty connection I’ve unfortunately let slip out of my teaching.

**What they almost said**

At the end of the *MT* article, the authors performed a double transformation equivalent to reflecting the points (2,0), (3,-4), and (9,-7) over the line via matrices using = giving image points (0,2), (-4,3), and (-7,9). That this matrix multiplication reversed all of the points’ coordinates is compelling evidence that might be a reflection matrix.

**Going much deeper**

Here’s how this works. Assume a set of pre-image points, P, undergoes some transformation T to become image points, P’. For this procedure, T can be almost any transformation except a translation–reflections, dilations, scale changes, rotations, etc. Translations can be handled using augmentations of these transformation matrices, but that is another story. Assuming P is a set of *n* two-dimensional points, then it can be written as a 2×*n* pre-image matrix, [P], with all of the *x*-coordinates in the top row and the corresponding *y*-coordinates in the second row. Likewise, [P’] is a 2×*n* matrix of the image points, while [T] is a 2×2 matrix unique to the transformation. In matrix form, this relationship is written .

So what would do as a transformation matrix? To see, transform (2,0), (3,-4), and (9,-7) using this new [T].

=

The result might be more easily seen graphically with the points connected to form pre-image and image triangles.

After studying the graphic, hopefully you can see that rotated the pre-image points 90 degrees around the origin.

**Generalizing**

Now you know the effects of two different transformation matrices, but what if you wanted to perform a specific transformation and didn’t know the matrix to use. If you’re new to transformations via matrices, you may be hoping for something much easier than the experimental approach used thus far. If you can generalize for a moment, the result will be a stunningly simple way to determine the matrix for any transformation quickly and easily.

Assume you need to find a transformation matrix, . Pick (1,0) and (0,1) as your pre-image points.

=

On the surface, this says the image of (1,0) is (a,b) and the image of (0,1) is (c,d), but there is *so much more here*!

Because the pre-image matrix for (1,0) and (0,1) is the 2×2 identity matrix, will always be BOTH the transformation matrix AND (much more importantly), the image matrix. This is a major find. It means that **if you know the images of (1,0) and (0,1) under some transformation T, then you automatically know the components of [T]**!

For example, when reflecting over the *x*-axis, (1,0) is unchanged and (0,1) becomes (0,-1), making . Remember, coordinates of points are always listed vertically.

Similarly, a scale change that doubles *x*-coordinates and triples the *y*s transforms (1,0) to (2,0) and (0,1) to (0,3), making .

In a generic rotation of around the origin, (1,0) becomes and (0,1) becomes .

Therefore, . Substituting into this [T] confirms the matrix from earlier.

As nice as this is, there is even more beautiful meaning hidden within transformation matrices. I’ll tackle some of that in my next post.