A * coordinate transformation* of the plane is a
function . I'll usually assume that f has
continuous partial derivatives, and that f is "essentially"
one-to-one in the region of interest. (A function is * one-to-one* if different inputs produce different
outputs.)

A coordinate transformation will usually be given by an equation . You can think of it as deforming or moving things in the u-v plane and placing them in the x-y plane.

I'm going to look at some important special cases.

1. * Linear transformations.* You know that a * linear transformation* has
the form

a, b, c, and d are numbers. In equation form, this is

"Linear transformation" is long to write and say, so I'll
often use * linear map* for short.

Linear maps always leave the origin fixed, since , gives , . (This is another way of saying that linear maps take the zero vector to the zero vector.) And as you might expect, linear maps carry lines to lines.

Moreover, a linear map takes squares to parallelograms:

For example, look at what happens to the unit square , :

The vectors , determine the unit square. They are mapped to the vectors , , two adjacent sides of the parallelogram.

2. * Translations.* A *
translation* has the form

e and f are numbers. This translation just translates everything by the vector .

A translation by a nonzero vector is *not* a linear map,
because linear maps must send the zero vector to the zero vector.
However, translations are very useful in performing coordinate
transformations. I'll introduce the following terminology for the
composite of a linear transformation and a translation.

* Definition.* Let A be a real matrix. An * affine map* is a
function of the form

* Example.* Find an affine map which carries
the unit square , to the
parallelogram in the x-y plane with vertices , , , .

I'm going to make a rough sketch of the parallelogram first. I want to know the orientation of the vertices --- e.g. that B and C are next to A, and that D is opposite A.

I'm going to do the transformation in two steps. First, I'll take the square to a parallelogram which is the right size and shape, but which has a corner at the origin. Next, I'll move the parallelogram so it's at the right place.

The vectors from A to B and to C are and . I saw above that if I construct a matrix with as the first column and as the second column, then it will multiply to and to :

So the following transformation takes the unit square to a parallelogram of the right shape:

This transformation takes to ; I want to go to the point A (which I used as the base point for my two vectors). To fix this, just translate by A:

3. * Rotations.* A *
rotation* counterclockwise through an angle is given by

To see this, just check that the unit vectors , go to the right places:

You can see from the picture that and have both been rotated by an angle . Other vectors can be built out of these two vectors, so other vectors are rotated by as well.

4. * Reflections.* This is how to do a * reflection* across the x-axis:

It is clear that this reflects things across the x-axis, because it simply negates the second component.

What about reflection across a line L making an angle with the origin? It's messy to do using analytic geometry, but very easy using matrices. Simply do a rotation through which carries L to the . Next, reflect across the x-axis. Finally, do a rotation through which carries the x-axis back to L. Each of these transformations can be accomplished by matrix multiplication; just multiply the three matrices to do reflection across L.

Translations, rotations, and reflections are examples of * rigid motions*. They preserve distances between
points, as well as areas.

* Example.* Find a real matrix A such that

reflects points across the line .

The line makes an angle of with the positive x-axis. I can accomplish this transformation as follows:

Thus,

Copyright 2008 by Bruce Ikenaga