Matrix norms

using FundamentalsNumericalComputation
A = [ 2  0; 1  -1 ]
2×2 Array{Int64,2}:
 2   0
 1  -1

In Julia one uses norm for vector norms and for the Frobenius norm of a matrix, which is like stacking the matrix into a single vector before taking the 2-norm.

Fronorm = norm(A)

Most of the time we really want to use opnorm for an induced matrix norm. The default is the 2-norm.

twonorm = opnorm(A)

You can get the 1-norm as well.

onenorm = opnorm(A,1)

The 1-norm is equivalent to

maximum( sum(abs.(A),dims=1) )   # sum down the rows (1st matrix dimension)

Similarly, we can get the \(\infty\)-norm and check our formula for it.

infnorm = opnorm(A,Inf)
maximum( sum(abs.(A),dims=2) )   # sum across columns (2nd matrix dimension)

Here we illustrate the geometric interpretation of the 2-norm. First, we will sample a lot of vectors on the unit circle in \(\mathbb{R}^2\).

theta = 2pi*(0:1/600:1)
x = [ fun(t) for fun in [cos,sin], t in theta ]    # 601 unit columns

plot(aspect_ratio=1, layout=(1,2), leg=:none, xlabel="\$x_1\$", ylabel="\$x_2\$")
plot!(x[1,:],x[2,:],subplot=1,title="Unit circle") 

We can apply A to every column of x simply by using a matrix multiplication.

Ax = A*x;

The image of the transformed vectors is an ellipse.

plot!(Ax[1,:],Ax[2,:],subplot=2,title="Image under map")

That ellipse just touches the circle of radius \(\|A\|_2\).