Using Cayley-Hamilton Theorem to Calculate Matrix Powers

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SUMMARY

The Cayley-Hamilton theorem allows for the calculation of matrix powers by expressing A^k as a linear combination of the matrix A itself, specifically A^k = a_0 + a_1 A for k ≥ 2. To determine the coefficients a_0 and a_1, one can utilize the eigenvalues of the matrix A. For matrices with two distinct eigenvalues, two equations can be derived, while for matrices with a repeated eigenvalue, differentiation of the characteristic polynomial provides the necessary additional equation to solve for the coefficients. This method is validated through the properties of the characteristic polynomial.

PREREQUISITES
  • Understanding of the Cayley-Hamilton theorem
  • Familiarity with eigenvalues and eigenvectors
  • Knowledge of matrix algebra, specifically 2x2 matrices
  • Basic calculus concepts, particularly differentiation
NEXT STEPS
  • Study the derivation and applications of the Cayley-Hamilton theorem in linear algebra
  • Learn how to compute eigenvalues and eigenvectors for various matrix types
  • Explore the characteristic polynomial and its significance in matrix theory
  • Practice differentiating polynomials and applying those techniques to matrix equations
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Mathematicians, students of linear algebra, and anyone interested in advanced matrix computations and the application of the Cayley-Hamilton theorem.

AcidRainLiTE
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Given a matrix A (for simplicity assume 2x2) we can use the Cayley-Hamilton theorem to write:

A^k = a_0 + a_1 A

for k>= 2.

So suppose we have a given k and want to find the coefficients a_0,a_1. We can use the fact that the same equation is satisfied by the eigenvalues. That is, for any eigenvalue \lambda we have

\text{(1) } \quad \lambda^k=a_0+a_1\lambda.

If A has 2 distinct eigenvalues, we can plug them into this equation and get 2 equations which allow us to solve for a_0, a_1. And we're done.

If on, the other hand, A has a repeated eigenvalue \lambda_1, then we only get 1 equation from the above procedure. To get another equation we can differentiate (1) to get
\text{(2) } \quad k \lambda^{k-1} = a_1.
We then plug \lambda_1 into (2) to get our second equation. Now we can solve for a_0, a_1.

My question is how we know we can differentiate (1) and still get a valid equation. In order to differentiate, (1) must hold for all \lambda (or at least over some interval). But we only know that it holds for particular discrete values of \lambda (i.e. for the eigenvalues of A).
 
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Ugh... First, huge thanks for your question. I solved my problem via this method.
I'm not sure that this is still valid question since you posted almost 3yrs ago.
Think about characteristic polynomial. f(t).
We differentiate f(t), than substitute eigenvalue after. Than it's a little bit tricky but still no problem .. I guess..
 

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