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I am working on an eigensolver in PLAPACK, which has built-in functionality to find the QR decomposition of a general matrix of double precision floating point numbers. I am familiar with the concept of iterating QR to find the eigenvalues of a matrix $A$, and at every iteration, I will have access to the contents of $A$, $Q_i$, and $R_i$ for $i\geq 1$.

I was wondering if there's a convenient way to find the eigenvectors, rather than solving $(A-\lambda_i I)v = 0$ for each $\lambda_i$?

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Try posting this on SciComp –  Inquest Mar 12 '12 at 14:22

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