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KonradZuseBest ResponseYou've already chosen the best response.0
Trying to figure out how this works, wolfram is giving me a different setup than my book... http://www.wolframalpha.com/input/?i=diagonalize+%7B%7B1%2C0%7D%2C%7B6%2C1%7D%7D The book says p = [1/3][0] [1 ][1] and P^1AP = [1][0] [0][1] Which is the inverse of J...
 one year ago

KonradZuseBest ResponseYou've already chosen the best response.0
@UnkleRhaukus any idea sir?
 one year ago

KonradZuseBest ResponseYou've already chosen the best response.0
crap I forgot to post what the book said h/o.
 one year ago

UnkleRhaukusBest ResponseYou've already chosen the best response.0
whats your question?
 one year ago

KonradZuseBest ResponseYou've already chosen the best response.0
I'm trying to do #14.
 one year ago

KonradZuseBest ResponseYou've already chosen the best response.0
I'm confused on how to do this... Wolfram gives a different definition than my book, and I'm not really sure what my book is trying to show...
 one year ago

KonradZuseBest ResponseYou've already chosen the best response.0
mhm yeah I entered that in wolfram, but I'm not sure what SJ and M are... They are different from P and A....
 one year ago

lgbasalloteBest ResponseYou've already chosen the best response.0
@KonradZuse not my habit to reply to questions already being answered
 one year ago

UnkleRhaukusBest ResponseYou've already chosen the best response.0
i think that S and P are inverses of each other
 one year ago

KonradZuseBest ResponseYou've already chosen the best response.0
that's what I was looking at. @iggy we are in the process yo...
 one year ago

KonradZuseBest ResponseYou've already chosen the best response.0
but according to wolfram and the example 7 I showed above, there is no 1/3 in wolfram.... Is it just a different way to do it....?
 one year ago

KonradZuseBest ResponseYou've already chosen the best response.0
yeah Idk about S and P...
 one year ago

KonradZuseBest ResponseYou've already chosen the best response.0
it look slike J = P^1AP just flipped around... Same thing happened when I did #15.
 one year ago

phiBest ResponseYou've already chosen the best response.1
diagonalizing a matrix M uses eigenvectors x and eigenvalues \(\lambda\) the "big equation" is \( M x = \lambda x\) if we put all the eigenvectors x into the columns of a matrix P and the eigenvalues on the diagonal of matrix \( \Lambda \) we can say \( MP = P\Lambda \) note the order we must multiply P and \( \Lambda \) to get the right result multiplying by the inverse of P we have \(\Lambda = P^{1}MP \) This says we can diagonalize matrix M by multiplying by the eigenvector matrix P It returns a diagonal matrix with entries being the eigenvalues of M
 one year ago

phiBest ResponseYou've already chosen the best response.1
When we do this diagonalizing, note that there is flexibility in defining an eigenvector. Eigenvectors represent a "direction", and if we scale them, they are still an eigenvector. Example: if eigenvector x= [ 1/3 1] corresponds to eigenvalue λ and we scale x by 3 to get x' = [1 3] , it is still an eigenvector associated with λ M(3x)=λ(3x) still works. We would scale x to get rid of fractions. Also, when diagonalizing, there is no forced order on the eigenvalues in matrix \(\Lambda\) The only requirement is that the eigenvalue in position n,n of matrix \(\Lambda\) corresponds to the eigenvector in column n of matrix P Often, though, people put the eigenvalues in ascending (or descending ) order. The point is we can get different variations of P and Λ, and all are correct, unless the order of the eigenvalues in Λ are stipulated to be in some order.
 one year ago
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