Matrices as Transformations
Matrices as Transformations
In linear algebra, matrices are not just grids of numbers—they are actions. A 2×2 matrix can represent a linear transformation of the 2D plane, mapping any point (x,y) to a new point (x′,y′).
How It Works
To apply a transformation, we multiply the transformation matrix by a column vector representing our point:
[x′y′]=[acbd][xy]=[ax+bycx+dy]
Common Transformations
Scaling
A scaling matrix stretches or compresses the plane along the x and y axes.
[kx00ky]
If kx=2, the x-coordinates are doubled. If ky=0.5, the y-coordinates are halved.
Reflections
Matrices can reflect points across specific lines.
- Over the x-axis: [100−1]
- Over the y-axis: [−1001]
Rotations
To rotate a point counterclockwise around the origin by an angle θ, use the rotation matrix:
[cosθsinθ−sinθcosθ]
Composing Transformations
If you want to apply transformation A and then transformation B, you can simply multiply their matrices. The combined transformation matrix is BA. Note that matrix multiplication is not commutative, so the order matters: BA means transformation A happens first, followed by B.
Example Problems
Example 1: Find the matrix that rotates the plane by 90∘ counterclockwise.
Using the rotation matrix formula with θ=90∘:
cos(90∘)=0,sin(90∘)=1
Substituting these into the rotation matrix gives:
[01−10]
Example 2: Apply the matrix [2003] to the triangle with vertices (1,1), (2,0), (0,2).
We multiply the matrix by each vertex (written as column vectors):
[2003][11]=[23]
[2003][20]=[40]
[2003][02]=[06]
The new vertices of the transformed triangle are (2,3), (4,0), and (0,6).