How do I calculate the $p$-norm of a matrix?

I know that the $p$-norm for a matrix is:

$$\|A\| = \max_{x\neq 0} \frac{\|Ax\|_p}{\|x\|_p}$$

but I don't know what this really means.

So how would I compute the $2$-norm, $3$-norm, etc for the matrix.

$$A = \begin{bmatrix} 2 & 1 \\ 1 & 2 \end{bmatrix}$$

UPDATE Apparently, the above matrix is too easy :) Let's try something harder.

$$A = \begin{bmatrix} 2 & 1 & 4 \\ 3 & 0 & -1 \\ 1 & 1 & 2 \end{bmatrix}$$

Thanks,

Solution 1:

If you have a vector $x = [x_1,x_2,\ldots,x_n]^T$ then $\|x\|_p = \sqrt[p]{|x_1|^p + |x_2|^p + \cdots |x_n|^p}$

In your case, $x = [x_1, x_2]^T$ then $Ax = [2x_1 + x_2, x_1 + 2x_2]$

$\|Ax\|_p^p = (|2x_1 + x_2|)^p + (|x_1 + 2x_2|)^p$ and $\|x\|_p^p = |x_1|^p + |x_2|^p$

$$\left( \frac{\|Ax\|_p}{\|x\|_p} \right)^p = \frac{(|2x_1 + x_2|)^p + (|x_1 + 2x_2|)^p}{|x_1|^p + |x_2|^p}$$

By symmetry, the maximum occurs when $x_1 = x_2 = y$ and hence

$$\left( \frac{\|Ax\|_p}{\|x\|_p} \right)^p = \frac{(3y)^p + (3y)^p}{y^p + y^p} = \frac{2 \times 3^p y^p}{2y^p} = 3^p$$

Hence, the $p^{th}$ norm of $A$ is $3$

For any matrix, the $2$ norm is the largest singular value.

Solution 2:

As I've mentioned in this answer to an MO question, Nick Higham has made note of a numerical method he attributes to Boyd for estimating the $p$-norm of a matrix, which is essentially an approximate maximization scheme similar in flavor to the usual power method for computing the dominant eigenvalue; Higham's book has a few details, his article a few more, and then see this MATLAB implementation of the algorithm.

Code? Why sure! Here's a Mathematica translation of the MATLAB routine pnorm():

dualVector[vec_?VectorQ, p_] := 
 Module[{q = If[p == 1, Infinity, 1/(1 - 1/p)], n = Length[vec]}, 
   If[Norm[vec, Infinity] == 0, Return[vec]];
   Switch[p,
    1, 2 UnitStep[vec] - 1,
    Infinity, (Sign[Extract[vec, #]] UnitVector[n, #]) &[
     First[Flatten[Position[Abs[vec], Max[Abs[vec]]]]]],
    _, Normalize[(2 UnitStep[vec] - 1) Abs[
        Normalize[vec, Norm[#, Infinity] &]]^(p - 1), 
     Norm[#, q] &]]] /; p >= 1

Options[pnorm] = {"Samples" -> 9, Tolerance -> Automatic};

pnorm[mat_?MatrixQ, p_, opts___] := 
 Module[{q = If[p == 1, Infinity, 1/(1 - 1/p)], m, n, A, tol, sm, x, 
    y, c, s, W, fo, f, c1, s1, est, eo, z, k, th},
   {m, n} = Dimensions[mat];
   A = If[Precision[mat] === Infinity, N[mat], mat];
   {sm, tol} = {"Samples", Tolerance} /. {opts} /. Options[pnorm];
   If[tol === Automatic, tol = 10^(-Precision[A]/3)];
   y = Table[0, {m}]; x = Table[0, {n}];
   Do[
    If[k == 1, {c, s} = {1, 0},
     W = Transpose[{A[[All, k]], y}];
     fo = 0;
     Do[
      {c1, s1} = Normalize[Through[{Cos, Sin}[th]], Norm[#, p] &];
      f = Norm[W.{c1, s1}, p];
      If[f > fo,
       fo = f; {c, s} = {c1, s1}];
      , {th, 0, Pi, Pi/(sm - 1)}]
     ];
    x[[k]] = c;
    y = c A[[All, k]] + s y;
    If[k > 1, x = Join[s Take[x, k - 1], Drop[x, k - 1]]];
    , {k, n}];
   est = Norm[y, p];
   For[k = 1, True, k++,
    y = A.x;
    eo = est; est = Norm[y, p];
    z = Transpose[A].dualVector[y, p];
    If[(Norm[z, q] < z.x || Abs[est - eo] <= tol est) && k > 1, 
     Break[]];
    x = dualVector[z, q];];
   est] /; p >= 1

Now, let's use the OP's matrix as an example:

mat = N[{{2, 1, 4}, {3, 0, -1}, {1, 1, 2}}];

and check how good the estimator is in known cases:

(pnorm[ma, #] - Norm[ma, #]) & /@ {1, 2, Infinity} // InputForm
{0., -2.2045227554556845*^-6, 0.}

(i.e. the estimate for the 2-norm is good to ~ 5 digits; adjusting either Samples, Tolerance, or both would give better results).

Let's compare with Robert's example:

pnorm[ma, 4, Tolerance -> 10^-9] // InputForm
5.695759123950937

Pretty close!

Finally, here is a plot of the $p$-norm of the OP's matrix with varying $p$:

Solution 3:

For other values of $p$, you can use Lagrange multiplier methods. Here, for example, is your $3 \times 3$ example with $p=4$ using Maple.

> M:= <<2|1|4>,<3|0|-1>,<1|1|2>>;
  X:= <x1,x2,x3>;
  MX:= M.X;
  F:= add(MX[i]^4,i=1..3)-lambda*(add(X[i]^4,i=1..3)-1);
  eqs:= {diff(F,x1),diff(F,x2),diff(F,x3),diff(F,lambda)};
  S:=RootFinding[Isolate](eqs,[x1,x2,x3,lambda]);
  pnorm:= max(map(t -> eval(lambda,t),S))^(1/4);

pnorm := 5.695759124

Solution 4:

EXTRA CREDIT: find the eigenvalues, eigenvectors, and the $1,2,\infty$ norms of the matrix $$B = \begin{pmatrix} 1 & 10 \\ -16 & 9 \end{pmatrix},$$ using the methods illustrated below. I have arranged that everything works out nicely.

ORIGINAL: I've never seen these calculated except for $p=1,2,\infty.$ Note that the 1 norm of a vector is the sum of the absolute values of the entries, while the $\infty$ norm of a vector is the largest absolute value of any entry.

As you can see from the link given by Jonas, the $\infty$ norm is the largest row sum(of absolute values), which for your 3 by 3 example is 2 + 1 + 4 = 7. This is achieved for a column vector consisting of all $\pm 1,$ where the choice of $\pm$ is made so that the product with the $2,1,4$ are all positive, so in this case all $+1. $ Take your matrix $$A = \left[\begin{matrix} 2 & 1 & 4 \\ 3 & 0 & -1 \\ 1 & 1 & 2 \end{matrix}\right],$$

$$A = \left(\begin{matrix} 2 & 1 & 4 \\ 3 & 0 & -1 \\ 1 & 1 & 2 \end{matrix}\right) \cdot \left(\begin{matrix} 1 \\ 1 \\ 1 \end{matrix}\right) = \left(\begin{matrix} 7 \\ 2 \\ 4 \end{matrix}\right), $$ so with $$ x = \left(\begin{matrix} 1 \\ 1 \\ 1 \end{matrix}\right), $$ we have $$ \|x\|_\infty = 1, \; \; \|Ax\|_\infty = 7, \; \; \frac{ \|Ax\|_\infty}{\|x\|_\infty} = 7, $$ and $$ \|A\|_\infty = 7. $$

The $1$ norm is the largest column sum(of absolute values), which for your 3 by 3 example is 4 + 1 + 2 = 7. This is achieved for a column vector consisting of almost all 0's and a single 1, where the choice of position for the 1 is made so that the most important column is kept. Take your matrix $$A = \left[\begin{matrix} 2 & 1 & 4 \\ 3 & 0 & -1 \\ 1 & 1 & 2 \end{matrix}\right],$$

$$A = \left(\begin{matrix} 2 & 1 & 4 \\ 3 & 0 & -1 \\ 1 & 1 & 2 \end{matrix}\right) \cdot \left(\begin{matrix} 0 \\ 0 \\ 1 \end{matrix}\right) = \left(\begin{matrix} 4 \\ -1 \\ 2 \end{matrix}\right), $$ so with $$ x = \left(\begin{matrix} 0 \\ 0 \\ 1 \end{matrix}\right), $$ we have $$ \|x\|_1 = 1, \; \; \|Ax\|_1 = 7, \; \; \frac{ \|Ax\|_1}{\|x\|_1} = 7, $$ and $$ \|A\|_1 = 7. $$

These give upper bounds for the norms of eigenvalues, working on that. From GP-Pari, largest eigenvalue is about 4.7,

? mat = [2,1,4; 3,0,-1; 1,1,2]
%1 = 
[2 1 4]

[3 0 -1]

[1 1 2]

? charpoly(mat)
%2 = x^3 - 4*x^2 - 2*x - 7
? 
? matdet(mat)
%3 = 7
? 
? polroots(  charpoly(mat)  )
%4 = [4.734676178725887352610775374 ,
 -0.3673380893629436763053876869 - 1.159101604948420124760141070*I,
 -0.3673380893629436763053876869 + 1.159101604948420124760141070*I]~
? 

I get it, the eigenvector for the real eigenvalue is given numerically as $$ x = \left(\begin{matrix} 1.803282495304177184832508716 \\ 0.9313936834217101677782666577 \\ 1 \end{matrix}\right), $$

? vec = [  1.803282495304177184832508716 , 0.9313936834217101677782666577, 1   ]
%7 = [1.803282495304177184832508716, 0.9313936834217101677782666577, 1]

? columnvec = mattranspose( vec)
%8 = [1.803282495304177184832508716, 0.9313936834217101677782666577, 1]~
? mat * columnvec
%9 = [8.537958674030064537443284089, 4.409847485912531554497526148, 4.734676178725887352610775374]~
? mat * columnvec  -   4.734676178725887352610775374  * columnvec
%10 = [4.038967835 E-28, -7.068193710 E-28, -4.038967835 E-28]~
?