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steffan09.doc
steffan09.doc

The Eigenvalue Problem: Power Iterations
The Eigenvalue Problem: Power Iterations

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... transformation matrix S has been found, the eigenvectors of A are contained in the columns of the transformation matrix on the right in Eq. 7 and in the rows of its inverse in Eq. 7, S-1. The eigenvalues A are the diagonal elements of A' and the eigenvalue in the nth diagonal element corresponds to ...
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... and x = C●,k is a column vector. On the other hand by the first definition of matrix multiplication ((AB)C)ik = ((AB)i,●)(C●,k). By the third definition of matrix multiplication (AB)i,● = (Ai,●)B. So ((AB)C)ik = ((Ai,●)B)(C●,k) = (pB)x. However, in the previous section we proved that if p is a row v ...
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... initial conditions y1 (0) and y2 (0). It makes sense to multiply by this parameter because when we have an eigenvector, we actually have an entire line of eigenvectors. And this line of eigenvectors gives us a line of solutions. This is what we’re looking for. Note that this is the general solution ...
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Perron–Frobenius theorem

In linear algebra, the Perron–Frobenius theorem, proved by Oskar Perron (1907) and Georg Frobenius (1912), asserts that a real square matrix with positive entries has a unique largest real eigenvalue and that the corresponding eigenvector can be chosen to have strictly positive components, and also asserts a similar statement for certain classes of nonnegative matrices. This theorem has important applications to probability theory (ergodicity of Markov chains); to the theory of dynamical systems (subshifts of finite type); to economics (Okishio's theorem, Leontief's input-output model); to demography (Leslie population age distribution model), to Internet search engines and even ranking of football teams.
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