Determining Matrix Powers without Eigenvectors ? (Worked out but inelegant soln)

In summary, the conversation revolves around using the Cayley-Hamilton Theorem to calculate the 7th power of a matrix without matrix multiplication, by substituting occurrences of M^n with M^(n-1), and the implications of diagonalisability and distinct eigenvalues in this process.
  • #1
sid9221
111
0
http://dl.dropbox.com/u/33103477/Untitled.png


My solution:
[tex]
M=\begin{bmatrix}
t+15 & -12 \\
24 & t-19
\end{bmatrix}
[/tex]

The eigen values are 1,3.

Hence as the matrix has real and distinct eigenvalues it is diagonalisable.

Now the characteristic equation is [tex] t^2 - 4t +3 =0 [/tex]

So [tex] M^2 - 4M +3I = 0 [/tex]

[tex] M^2 = 4M-3I [/tex]

Hence [tex] M^7 = (4M-3I)(4M-3I)(4M-3I)(M) [/tex]

This gives the correct answer but is a very inelegant solution. Is there a better way to determine the answer without Eingevectors ?
 
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  • #2
Why do you think that is inelegant? You used the Cayley Hamilton Theorem, right? I like it.
 
  • #3
I think that is exactly what you were supposed to do. The only thing left is to finish the calculation by substituting in the given matrix and doing the multiplications.
 
  • #4
Mark44 said:
I think that is exactly what you were supposed to do. The only thing left is to finish the calculation by substituting in the given matrix and doing the multiplications.

Whats the point in having a theorem that just saves you 1 multiplication ?
 
  • #5
If you had a much larger matrix M, and it was being raised to a higher power, this could save you some work.

For this problem, the real goal is to get you comfortable working with the characteristic equation of a matrix.
 
  • #6
Mark44 said:
If you had a much larger matrix M, and it was being raised to a higher power, this could save you some work.

For this problem, the real goal is to get you comfortable working with the characteristic equation of a matrix.

I see. Thank you.
 
  • #7
No, no, you're not done yet!

Work out the brackets, you'll see terms like M4 and M3. Substitute them also by using Caley-Hamilton.

The eventual goal is to write

[tex]M^7=aM^2+bM+cI[/tex]

This only needs one matrix multiplication.
 
  • #8
micromass said:
No, no, you're not done yet!

Work out the brackets, you'll see terms like M4 and M3. Substitute them also by using Caley-Hamilton.

The eventual goal is to write

[tex]M^7=aM^2+bM+cI[/tex]

This only needs one matrix multiplication.

[tex] 27 IM-108 M^2-144 IM^3+64 M^4 [/tex]

Than sub in value for M^2 and multiply ? I don't see how I can get the answer with 1 multiplication ?
 
  • #9
sid9221 said:
Hence as the matrix has real and distinct eigenvalues it is diagonalisable.

I think that's incorrect. The proper explanation should be:
Since the matrix does not have zero as one of its eigenvalues, it is invertible, hence it is diagonalisable.

micromass said:
No, no, you're not done yet!

Work out the brackets, you'll see terms like M4 and M3. Substitute them also by using Caley-Hamilton.

The eventual goal is to write

[tex]M^7=aM^2+bM+cI[/tex]

This only needs one matrix multiplication.

But, replacing M^2 again would give M^7 in terms of M and I only, right?
 
  • #10
sharks said:
I think that's incorrect. The proper explanation should be:
Since the matrix does not have zero as one of its eigenvalues, it is invertible, hence it is diagonalisable.

No, that is totally incorrect. Invertibility and diagonalisable have nothing to do with each other.

OP:

[itex]M^2=4M-3I[/itex]

So

[itex]M^3=4M^2-3M[/itex]

So you can replace all occurences of [itex]M^3[/itex] with occurences of [/itex]M^2[/itex].

Same way: you can replace [itex]M^4[/itex] with something and [itex]M^2[/itex] with something.
 
  • #11
sharks said:
But, replacing M^2 again would give M^7 in terms of M and I only, right?

Right! So this problem requires no matrix multiplications at all!
 
  • #12
sid9221 said:
Hence as the matrix has real and distinct eigenvalues it is diagonalisable.

But, do the eigenvalues need to be distinct in order for the matrix to be diagonalisable? What if the eigenvalues are real but equal?
 
  • #13
sharks said:
But, do the eigenvalues need to be distinct in order for the matrix to be diagonalisable? What if the eigenvalues are real but equal?

Then it might or might not be diagonalisable.

A matrix is diagonalisable if and only if it has a basis of eigenvectors. So if the eigenvalues are distinct, then all eigenspaces are distinct and thus a basis of eigenvectors exist.

Another equivalence is: a matrix is diagonalisable if and only if the algebraic multiplicity is the geometric multiplicity.

However, if not all the eigenvalues are distinct, then it might be harder to check diagonalisability. Typically, one has to know the eigenvectors at that point.

Yet another equivalence states that a matrix is diagonalisable iff the minimal polynomial has distinct factors. But then we have to know the minimal polynomial.
 
  • #14
Apparently works out to:

[tex] M^7 = 27 - 108[4M-3I] - 144[16M-15I] + 64[64M-63I] [/tex]
 
  • #15
sid9221 said:
Apparently works out to:

[tex] M^7 = 27 - 108[4M-3I] - 144[16M-15I] + 64[64M-63I] [/tex]

That first term would have to be 27I.
 
  • #16
micromass said:
Then it might or might not be diagonalisable.

A matrix is diagonalisable if and only if it has a basis of eigenvectors. So if the eigenvalues are distinct, then all eigenspaces are distinct and thus a basis of eigenvectors exist.

Another equivalence is: a matrix is diagonalisable if and only if the algebraic multiplicity is the geometric multiplicity.

However, if not all the eigenvalues are distinct, then it might be harder to check diagonalisability. Typically, one has to know the eigenvectors at that point.

Yet another equivalence states that a matrix is diagonalisable iff the minimal polynomial has distinct factors. But then we have to know the minimal polynomial.
I'll need to look into that a bit more. Thank you for the clarification, micromass.
 
  • #17
Mark44 said:
That first term would have to be 27I.

Yeah of course !

So extending this, you could potentially calculate the 27th power of a 6x6 matrix without a single multiplication(or a few) wow !

Cayley and Hamilton were pretty smart dude's eh !
 

FAQ: Determining Matrix Powers without Eigenvectors ? (Worked out but inelegant soln)

How can matrix powers be determined without using eigenvectors?

Matrix powers can be determined using the Cayley-Hamilton theorem, which states that every square matrix satisfies its own characteristic polynomial. This means that the matrix can be expressed as a polynomial of its own powers, making it possible to calculate any power of the matrix without using eigenvectors.

Why is the method of determining matrix powers without eigenvectors considered inelegant?

This method is considered inelegant because it involves a lot of manual calculations and can be time-consuming for larger matrices. It also requires a good understanding of matrix algebra and the Cayley-Hamilton theorem, which can be challenging for some individuals.

Is this method applicable to all types of matrices?

Yes, this method can be used for all types of matrices as long as they are square matrices. It does not matter if the matrix is invertible or not, or if it has repeated eigenvalues.

Can this method be used to calculate higher powers of the matrix?

Yes, this method can be used to calculate any power of the matrix. However, as the power increases, the calculations become more complex and time-consuming.

Are there any other methods to determine matrix powers without using eigenvectors?

Yes, there are other methods such as diagonalization and Jordan decomposition. However, these methods also require a good understanding of linear algebra and can be difficult to apply in certain cases. The Cayley-Hamilton theorem method is considered the most straightforward approach.

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