Rotations, Reflections, Scalings

I just wanted to pause briefly to showcase how some of the linear transformations we have been looking into can be represented in computerese (or at least one version of computerese). You can click on the pencil icon and then on the matrix_transform.js file in the trinket below and look for the word matrix. Change the numbers in those lines to check the effects on the transformations. You can get some fairly wild stuff.

By the way, trinket is an incredibly beautiful product if you like tinkering with all kinds of code. Grab a free account and share your work!

For this demo, I stuck with simple transformations centered at the origin of a coordinate system (so to speak). As you can imagine, there are much more elaborate things you can do when you combine transformations and move the center point around.

Zukei and Dot Products

Zukei puzzles that ask students to find right triangles seem to rely on an understanding of perpendicularity that is situated more comfortably in linear algebra than in Euclidean geometry. Consider the following, which has a hidden isosceles right triangle in it. Your job is to find the vertices of that isosceles right triangle.

zukei

High school students would be expected to look at perpendicularity either intuitively—searching for square corners—or using the slope criteria, that perpendicular lines have slopes which are negative reciprocals of each other. But it seems a bit much to start treating this puzzle as a coordinate plane and determining equations of lines.

The Dot Product

In all fairness, the dot product is a bit much too. Instead, we can operationalize slopes with negative reciprocals by, for example, starting from any point, counting 1, 2, . . . n to the left or right and then 1, 2, . . . n up or down to get to the next point. From that point, we have to count left-rights in the way we previously counted up-downs and up-downs in the way we counted left-rights, and we have to reverse one of those directions. For the puzzle above, we count 1, 2 to the right from a point and then 1, 2 up to the next point. From that second point, it’s 1, 2 right and then 1, 2 down. It’s a little harder to see our counting and direction-switching rule at work when the slopes are 1 and –1, but, in the Zukei context at least, the slopes have to be 1 and –1, I think, to get an isosceles right triangle if we’re not talking about square corners.

This kind of counting is really treating the possible triangle sides as vectors. And, with perpendicular vectors, we can see that we can get something like one of these two pairs (though perpendicular vectors don’t have to look like this): \[\mathtt{\begin{bmatrix} x_1\\x_2 \end{bmatrix} \textrm{and} \begin{bmatrix} -x_2\\ \,\,\,\,x_1 \end{bmatrix} \textrm{or} \begin{bmatrix} x_1\\x_2 \end{bmatrix} \textrm{and} \begin{bmatrix} \,\,\,\,x_2\\-x_1 \end{bmatrix}}\]

The dot product is defined as the sum of the element-wise products of the vector components. In the case of perpendicular vectors, the dot product is 0. Here is the dot product of our vectors: \[\mathtt{(x_1 \cdot -x_2) + (x_2 \cdot x_1) = 0}\]

Some Programming

One reason why this way of defining perpendicularity (with a single value) is helpful is that we avoid nasty zero denominators and, therefore, undefined slopes. With the two vectors at the right, we get \[\mathtt{\begin{bmatrix}0\\5\end{bmatrix} \cdot \begin{bmatrix}4\\0\end{bmatrix} = (0)(4) + (5)(0) = 0}\]

We can take all of the points and run them through a program to find all the connected perpendicular vectors. The result ((0, 1), (1, 0)), ((0, 1), (2, 3)) below means that the vector connecting (0, 1) and (1, 0) and the vector connecting (0, 1) and (2, 3) are perpendicular.

This gives us all the perpendicular vector pairs, though it doesn’t filter out those vectors with unequal magnitudes, which we wanted in order to identify the isosceles right triangle.

There are some Zukei solvers available, though I confess I haven’t looked at any of them. No doubt, one or all of them use linear algebra rather than ordinary coordinate plane geometry to do their magic. It’s about time, I think, we start weaving linear algebra into high school algebra and geometry standards.

Solving Zukei puzzles is not the best justification for bringing linear algebra down into high school, of course. But I hope it can be a salient example of how connected linear algebra can be to a lot of high school content standards.