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I'm trying to generate a list of random submatrices with matrix norm 0.1, however I'm getting stuck; currently my code looks like this, where I run over a while loop.

Randnum:= RandomReal[NormalDistribution[0, 1]];                             
Randz := Randnum + I*Randnum;                                                        
Randmat[n_] := Table[Randz, {n}, {n}];                                          
Randuni[n_] := Orthogonalize[Randmat[n]];    

fbfmatricestest={};
tbtmatricestest={};
epsilontest={};
normepsilontest={};
While[Length[normepsilontest]<=1,
  (
 (*Generate a random matrix*)
 a=Randuni[4];
(*Take the 3x3 submatrix out*)
b=Take[a,{1,3},{1,3}];
(*generate the epsilon matrix*)
\[Epsilon]=Simplify[ConjugateTranspose[b].b -{{1,0,0},{0,1,0},{0,0,1}}];
(*n\[Epsilon] measures gives the norm of each submatrix*)
n\[Epsilon]=Norm[\[Epsilon]];   
If[
Abs[n\[Epsilon]-0.1]<0.01,
(
AppendTo[fbfmatricestest,a];
AppendTo[tbtmatricestest,b];
AppendTo[epsilontest,\[Epsilon]];
AppendTo[normepsilontest, n\[Epsilon]]
)
]
)
]

How can I efficiently generate this random list? I read that Reap and Sow might be able to generate a bunch and then collect those which satisfy a condition, but I feel this may end up taking longer than the procedure above I am trying to get working. Many thanks!

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  • $\begingroup$ Ah I see, thanks! $\endgroup$ – MKF Nov 27 '18 at 21:45
  • $\begingroup$ I edited the question, sorry! $\endgroup$ – MKF Nov 28 '18 at 1:46
  • $\begingroup$ Actually, I thought about it, it is the norm of b we worry about; a is unitary so has norm 1, but this is not true for b. I disagree that is the reason... $\endgroup$ – MKF Nov 28 '18 at 6:23
  • $\begingroup$ I'd like to point out that originally, the code read as follows: \[Epsilon]=Simplify[ConjugateTranspose[b].b];. After your edit, it's a different story. $\endgroup$ – Henrik Schumacher Nov 28 '18 at 6:26
  • $\begingroup$ Even so, ConjugateTranspose[b].b still isn't norm 1 right? $\endgroup$ – MKF Nov 28 '18 at 6:27
4
+25
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Explanations

I will present a method which relies on p-form calculation of matrix norms, a method that is already implemented in Mathematica.

We generate a random $n_1\times n_2$ complex matrix as follows:

ClearAll[randomMatrix];
randomMatrix[n1_, n2_] := RandomComplex[{-1 - I, 1 + I}, {n1, n2}];
randomMatrix[n_] := RandomComplex[{-1 - I, 1 + I}, {n, n}];

We calculate all possible rectangular submatrices by considering all possible positions for upperleft corner of submatrix and all possible row and column lengths:

ClearAll[possibleSubMatrices];
possibleSubMatrices[n1_, n2_] := possibleSubMatrices[n1, n2] = Flatten[Table[{leftTop1, leftTop2, rowLength, columnLength}, {leftTop1, n1 - 1}, {leftTop2, n2 - 1}, {columnLength, 1, n1 - leftTop1}, {rowLength, 1, n2 - leftTop2}], 3];

Then, given any matrix, following code gives all possible submatrices:

ClearAll[listSubMatrices];
listSubMatrices = Function[input, With[{n1 = Dimensions[input][[1]], n2 = Dimensions[input][[2]]},
     Function[input[[#[[1]] ;; #[[1]] + #[[4]], #[[2]] ;; #[[2]] + #[[3]]]]] /@ possibleSubMatrices[n1, n2] ]];

For example,

With[{matrix = Array[a, {4, 3}]},
  {MatrixForm[matrix], MatrixForm /@ listSubMatrices[matrix]}
] // TeXForm

$$ \left\{\left( \begin{array}{ccc} a(1,1) & a(1,2) & a(1,3) \\ a(2,1) & a(2,2) & a(2,3) \\ a(3,1) & a(3,2) & a(3,3) \\ a(4,1) & a(4,2) & a(4,3) \\ \end{array} \right),\left\{\left( \begin{array}{cc} a(1,1) & a(1,2) \\ a(2,1) & a(2,2) \\ \end{array} \right),\left( \begin{array}{ccc} a(1,1) & a(1,2) & a(1,3) \\ a(2,1) & a(2,2) & a(2,3) \\ \end{array} \right),\left( \begin{array}{cc} a(1,1) & a(1,2) \\ a(2,1) & a(2,2) \\ a(3,1) & a(3,2) \\ \end{array} \right),\left( \begin{array}{ccc} a(1,1) & a(1,2) & a(1,3) \\ a(2,1) & a(2,2) & a(2,3) \\ a(3,1) & a(3,2) & a(3,3) \\ \end{array} \right),\left( \begin{array}{cc} a(1,1) & a(1,2) \\ a(2,1) & a(2,2) \\ a(3,1) & a(3,2) \\ a(4,1) & a(4,2) \\ \end{array} \right),\left( \begin{array}{ccc} a(1,1) & a(1,2) & a(1,3) \\ a(2,1) & a(2,2) & a(2,3) \\ a(3,1) & a(3,2) & a(3,3) \\ a(4,1) & a(4,2) & a(4,3) \\ \end{array} \right),\left( \begin{array}{cc} a(1,2) & a(1,3) \\ a(2,2) & a(2,3) \\ \end{array} \right),\left( \begin{array}{cc} a(1,2) & a(1,3) \\ a(2,2) & a(2,3) \\ a(3,2) & a(3,3) \\ \end{array} \right),\left( \begin{array}{cc} a(1,2) & a(1,3) \\ a(2,2) & a(2,3) \\ a(3,2) & a(3,3) \\ a(4,2) & a(4,3) \\ \end{array} \right),\left( \begin{array}{cc} a(2,1) & a(2,2) \\ a(3,1) & a(3,2) \\ \end{array} \right),\left( \begin{array}{ccc} a(2,1) & a(2,2) & a(2,3) \\ a(3,1) & a(3,2) & a(3,3) \\ \end{array} \right),\left( \begin{array}{cc} a(2,1) & a(2,2) \\ a(3,1) & a(3,2) \\ a(4,1) & a(4,2) \\ \end{array} \right),\left( \begin{array}{ccc} a(2,1) & a(2,2) & a(2,3) \\ a(3,1) & a(3,2) & a(3,3) \\ a(4,1) & a(4,2) & a(4,3) \\ \end{array} \right),\left( \begin{array}{cc} a(2,2) & a(2,3) \\ a(3,2) & a(3,3) \\ \end{array} \right),\left( \begin{array}{cc} a(2,2) & a(2,3) \\ a(3,2) & a(3,3) \\ a(4,2) & a(4,3) \\ \end{array} \right),\left( \begin{array}{cc} a(3,1) & a(3,2) \\ a(4,1) & a(4,2) \\ \end{array} \right),\left( \begin{array}{ccc} a(3,1) & a(3,2) & a(3,3) \\ a(4,1) & a(4,2) & a(4,3) \\ \end{array} \right),\left( \begin{array}{cc} a(3,2) & a(3,3) \\ a(4,2) & a(4,3) \\ \end{array} \right)\right\}\right\} $$

Note that we ignore $1x1$ submatrices.

As the next step, we define

ClearAll[chooseCorrectNorms];
chooseCorrectNorms[list_List, normAverage_, error_] := With[{norms = Norm[#, 1] & /@ list}, Pick[list, 
UnitStep[(norms - normAverage - error) (normAverage - error - norms)], 1]];

which eliminates matrices of undesired norms. See that we are using Norm[list,p] command to calculate matrix norm. Also, note that we are using vectorized commands Pick and UnitStep instead of conditional If and friends for better performance.

Now, our last command is this:

ClearAll[try];
try[normAverage_, error_] := With[{matrix = randomMatrix[4]}, {matrix, chooseCorrectNorms[listSubMatrices[matrix], normAverage, error]}];

It generates a random $4x4$ matrix, find all submatrices, and eliminate those with undesired norms.

Now, we simply need to run try command again and again until we hit a nonempty list.

Example

DeleteCases[Table[try[.1,.5],10^3],{_,{}}]
(* {{{{0.670769 - 0.179352 I, 
0.583875 - 0.0771007 I, -0.0528683 + 0.221524 I, 
0.312784 - 0.899022 I}, {-0.201839 + 0.0572823 I, 
0.0601396 + 0.102979 I, 
0.572871 + 0.221715 I, -0.352286 + 0.510435 I}, {0.150507 - 
 0.124843 I, -0.379535 - 0.142305 I, 
0.0565353 - 0.22504 I, -0.390374 + 0.823681 I}, {0.664221 + 
 0.375659 I, 0.913889 - 0.853969 I, -0.923382 + 0.855335 I, 
0.110999 + 0.149893 I}}, {{{-0.201839 + 0.0572823 I, 
 0.0601396 + 0.102979 I}, {0.150507 - 0.124843 I, -0.379535 - 
  0.142305 I}}}}} *)

which means the random matrix

$$\left( \begin{array}{cccc} 0.670769\, -0.179352 i & 0.583875\, -0.0771007 i & -0.0528683+0.221524 i & 0.312784\, -0.899022 i \\ -0.201839+0.0572823 i & 0.0601396\, +0.102979 i & 0.572871\, +0.221715 i & -0.352286+0.510435 i \\ 0.150507\, -0.124843 i & -0.379535-0.142305 i & 0.0565353\, -0.22504 i & -0.390374+0.823681 i \\ 0.664221\, +0.375659 i & 0.913889\, -0.853969 i & -0.923382+0.855335 i & 0.110999\, +0.149893 i \\ \end{array} \right)$$

has the submatrix

$$\left( \begin{array}{cc} -0.201839+0.0572823 i & 0.0601396\, +0.102979 i \\ 0.150507\, -0.124843 i & -0.379535-0.142305 i \\ \end{array} \right)$$

which has the matrix norm $0.52459$.

Performance considerations

I wrote above example to show that the code works and finds solutions for high enough error margins. However, it needs more trials for lower error margins.

The code works relatively fast in the sense that it generates $10,000$ $4\times 4$ random complex matrices and checks all their submatrices under 3 seconds on may laptop. However, the low error margin means we need a lot more trials and the required time increases as we can see:

DeleteCases[Table[try[.1,.01],10^3],{_,{}}]//Timing
(* {0.274255,{}} *)

DeleteCases[Table[try[.1,.01],10^4],{_,{}}]//Timing
(* {2.70209,{}} *)

DeleteCases[Table[try[.1,.01],10^5],{_,{}}]//Timing
(* {27.9623,{}} *)

One immediate thing that we can do is to parallelize the computations. The simplest thing to do is to use ParallelTable instead of Table. On my laptop which has 2 cores, this seems to be a nice performance improvement:

DeleteCases[ParallelTable[try[.1,.01],10^5],{_,{}}]//Timing
(* {2.21016,{}} *)

DeleteCases[ParallelTable[try[.1,.01],10^6],{_,{}}]//Timing
(* {30.9441,{}} *)

With higher number of cores, it will surely be more manageable.

Final Comments

The way we are doing the calculation here is quite ugly: We brute force generate random matrices and see if they satisfy a very selective condition. As the probability that they satisfy that condition is low, we need to generate lots of matrices to find one, which is simply a waste of resources.

A more proper way to do this is to generate $2\times 2$, $2\times 3$, or $3\times 3$ matries with required form and then embed it into a random $4\times 4$ matrix. For example,

Timing[chooseCorrectNorms[ParallelTable[randomMatrix[2],10^3],.1,0.01]]
(*{0.02439,{}}*)

generates random $2\times 2$ matrices and check their norm directly. This approach is clearly faster, though I still fail to find a solution:

Timing[chooseCorrectNorms[ParallelTable[randomMatrix[2], 10^4], .1, 0.01]]

(* {0.119413, {}} *)

Timing[chooseCorrectNorms[ParallelTable[randomMatrix[2], 10^5], .1, 0.01]]

(*{1.0971, {}}*)
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