Eigenvalues of 12*12 symbolic matrix

In summary, the conversation discusses the calculation of eigenvalues for a symbolic matrix in Mathematica and MATLAB. The matrix is in the form of a 12*12 matrix and both software programs are unable to calculate its eigenvalues. There is a suggestion to try using a computer with 64 gigabytes of memory. The conversation also discusses the use of Root objects and ToRadicals in finding the roots of cubic and quartic equations. The conversation ends with the suggestion to carefully check for any errors and to try to understand the twelve roots of the huge polynomial.
  • #1
quin
50
0
Hi dear friends
I have a 12*12 symbolic matrix in terms of x y z d that I want its eigenvalues
but not mathematica nor MATLAB can do it for me.My mathematica is "7" so If you have a newer version or even in MATLAB , would you mind checking my matrix in your software?
this is my matrix in mathematica and matlab:
 

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  • #2
In[1]:= LeafCount[Eigenvalues[{{a,b},{c,d}}]]
Out[1]= 55

In[2]:= LeafCount[Eigenvalues[{{a,b,c},{d,e,f},{g,h,i}}]]
Out[2]= 268

In[3]:= LeafCount[Eigenvalues[{{a,b,c,d},{e,f,g,h}, {i,j,k,l},{m,n,o,p}}]]
Out[3]= 1657

In[4]:= LeafCount[Eigenvalues[{{a,b,c,d,e},{f,g,h,i,j}, {k,l,m,n,o},{p,q,r,s,t},{u,v,w,x,y}}]]
Out[4]= 11916

In[5]:= LeafCount[Eigenvalues[{{a,b,c,d,e,f}, {g,h,i,j,k,l},{m,n,o,p,q,r},{s,t,u,v,w,x}, {y,z,a1,b1,c1,d1},{e1,f1,g1,h1,i1,j1}}]]
Out[5]= 97411

In[6]:= Fit[Map[Log,{55,268,1657,11916}],{1,x},x]
Out[6]= 2.1100 + 1.7957*x

In[7]:= Show[Plot[2.1100+1.7957 x,{x,1,5}],ListPlot[Map[ Log,{55,268,1657,11916,97411}], Joined->True]]
Out[7]= ...plotSnipped...

In[8]:= E^(2.1100+1.7957 x)/.x->12
Out[8]= 1.8817*10^10

So for this somewhat simpler approximation of an n by n symbolic matrix the number of elements making up the eigenvalues grows just a bit faster than E^(2.11+1.8x) and thus you would have about 18 billion elements making up the result.

Now your matrix is "banded" and has some zeros, which will help, but probably not enough. You could try replacing all your 2d/3 by d and each of your unique -2 Cos functions by other simple fabricated variables. That might reduce the size of your task by perhaps a factor of 3 or 4. But even if your zeros and banding help more than I expect, I doubt your result will have less than a billion elements. Maybe with 64 gigabytes of memory and unlimited time you might get an answer to the simplified problem, but that is just a guess and I would not be surprised if I were wrong.
 
  • #3
Bill Simpson said:
Bill Simpson;

thanks for your help But I made it more simplify by renaming identical elements and matrix became in the form of below:
but still mathematica cannot calculate its eigenvalues.please help me !
 

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  • #4
Did you try this on a computer with 64 gigabytes of memory?

Did you try solving this problem for 8x8 and 9x9 and 10x10 matricies and use the Mathematica function MaxMemoryUsed[] with a fresh start for each of your smaller problems. See how much memory is needed for the smaller problems and how this is growing and whether you can then estimate how much memory will be needed for the 12x12 matrix. With the banding of your matrix I am not sure how to use smaller matricies and still get memory measurements that will correctly estimate what your 12x12 matrix will require.
 
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  • #5
Sorry but what do you mean by computer with 64 gigabytes of memory?

and also generally in calculating eigenvalues forexample for 4*4 symbolic matrix, mathematica gives "ROOT[...]" so what should I do for solving these roots and arrive to my eigenvalues?
 
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  • #6
If you have an old or inexpensive computer you may only have 256 megabytes of memory. If you have a newer or better computer you may have one gigabyte or two gigabytes of memory. Your eigenvalue calculation will have billions of terms in the result. So I asked if you were able to use a computer that does not have one or two gigabytes of memory, but instead to use one with 64 or 128 gigabytes of memory. Perhaps that will be enough to be able to calculate your result.

Now Root[]. Cubic and quartic equations, those with x^3 or x^4 in them and you want to find all the roots, are sometimes very large. Mathematica will sometimes give you a smaller way of displaying the result. It will give Root[equation, 1] for the "first" root of the equation and Root[equation, 2] for the "second" root, etc. These are usually much smaller and sometimes you can see the information you need from this.

If you want to see all the details of what each root is you can try ToRadicals[Root[equation,1]] and see if it can display the solution you wish. Here is the documentation on this.

http://reference.wolfram.com/mathematica/ref/ToRadicals.html

It is possible that there is no way for ToRadicals to find what you want.
 
  • #7
thanks but my pc is new with core i 7 and 8gig ram and 500gig memory but still it cannot give its eigenvalues! I really don't Know how can I solve my problem anymore !
 
  • #8
In[2]:= aa = a //. {(2*d)/3 -> d, (-2*d)/3 -> -d, -2*Cos[(x + z)/4] -> -e, -2*Cos[(y + z)/4] -> -f, -2*Cos[(x + y)/4] -> -g, -2*Cos[(x - y)/4] -> -h, -2*Cos[(y - z)/4] -> -i, -2*Cos[(x - z)/4] -> -j}

Out[2]= {
{d, d, d, -e, 0, 0, -f, 0, 0, -g, 0, 0},
{d, d, d, 0, -e, 0, 0, -f, 0, 0, -g, 0},
{d, d, d, 0, 0, -e, 0, 0, -f, 0, 0, -g},
{-e, 0, 0, d, -d, -d, -h, 0, 0, -i, 0, 0},
{0, -e, 0, -d, d, d, 0, -h, 0, 0, -i, 0},
{0, 0, -e, -d, d, d, 0, 0, -h, 0, 0, -i},
{-f, 0, 0, -h, 0, 0, d, -d, d, -j, 0, 0},
{0, -f, 0, 0, -h, 0, -d, d, -d, 0, -j, 0},
{0, 0, -f, 0, 0, -h, d, -d, d, 0, 0, -j},
{-g, 0, 0, -i, 0, 0, -j, 0, 0, d, d, -d},
{0, -g, 0, 0, -i, 0,0, -j, 0, d, d, -d},
{0, 0, -g, 0, 0, -i, 0, 0, -j, -d, -d, d}}

In[3]:= aaa=Eigenvalues[aa];

There you have it, the eigenvalues, but not printed out. How big is it?

In[4]:= LeafCount[aaa]
Out[4]= 424921

In[5]:= aaa[[1]]
Out[5] = stunninglyHugeRootObject and there are 11 more of those

I am astonished it was able to complete. I did not believe this could be done.
Please check all this very carefully to make certain I have made no errors.
With your computer you should easily be able to reproduce this.
Then you have to understand the twelve roots of this huge polynomial.
There may or may not be a solution in radicals for these.
 
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  • #9
Really thanks for your complete guidence
But how can understand the twelve roots of this huge polynomial? by ToRadicals of aaa[[1]] ?
thank you alot
 
  • #10
I do not believe ToRadicals[aaa[[1]]] will be able to find a solution for you, but you can try it and see what happens. Or perhaps there is some other method to at least partly factor the result. Or perhaps there are common subexpressions that appear several times in the large polynomial and you can see some meaning in those. Perhaps there is something about your original problem that will tell something about the polynomial. But it is so large that I believe it will be very difficult for you to make any sense of.
 
  • #11
Assuming Out[2] in post #8 is correct, this has a huge amount of internal structure. You only need to diagonalize the 3x3 diagonal blocks to make it effectively a 4x4 symmetric matrix where each "element" is a 3x3 diagonal matrix.

Doing something along those lines will probably give you a lot more understanding than getting "the answer" by brute force.
 
  • #12
AlephZero said:
Assuming Out[2] in post #8 is correct, this has a huge amount of internal structure. You only need to diagonalize the 3x3 diagonal blocks to make it effectively a 4x4 symmetric matrix where each "element" is a 3x3 diagonal matrix.

Doing something along those lines will probably give you a lot more understanding than getting "the answer" by brute force.
yes you are right my matrix is infact a 4*4 matrix where each "element" is a 3x3 diagonal matrix
But I have one question
If I diagonalize each of 16 blocks seperately and then put them in the matrix and after obtaining a symmetric matrix,find its eigenvalues, does this procces preserve the form of first matrix eigenvalues? I mean the finall symmetric matrix will have the same eigenvalues of the first matrix?

thanks
 
  • #13
http://math.fullerton.edu/mathews/n2003/QRMethodMod.html
 
Last edited by a moderator:

1. What is the definition of eigenvalues?

Eigenvalues are the set of numbers that satisfy the equation Ax = λx, where A is a square matrix, λ is a scalar value, and x is a non-zero vector. In simpler terms, eigenvalues are the values which, when multiplied by a vector, result in the same vector but with a different magnitude.

2. How do you calculate eigenvalues of a 12*12 symbolic matrix?

The calculation of eigenvalues for a 12*12 symbolic matrix can be done using computer software or by hand using techniques such as Gaussian elimination or the power method. The process involves finding the roots of the characteristic polynomial of the matrix, which is a polynomial equation of degree 12.

3. What is the significance of eigenvalues in matrix algebra?

Eigenvalues are important in matrix algebra as they can provide valuable information about the properties of a matrix. They are used in various applications such as solving systems of linear equations, determining the stability of dynamical systems, and finding the principal components in data analysis.

4. Can a 12*12 symbolic matrix have complex eigenvalues?

Yes, a 12*12 symbolic matrix can have complex eigenvalues. In fact, any square matrix can have complex eigenvalues. This is because the characteristic polynomial of a matrix can have complex roots.

5. How do eigenvalues relate to eigenvectors?

Eigenvalues and eigenvectors are closely related. Eigenvectors are the corresponding vectors to the eigenvalues of a matrix. Each eigenvalue has a corresponding eigenvector, and the eigenvectors form a basis for the vector space of the matrix. The eigenvectors can be found by solving the equation Av = λv, where A is the matrix, v is the eigenvector, and λ is the eigenvalue.

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