# Is the QR algorithm for computing eigenvalues efficient for today's standards?

I was looking at the QR factorization algorithm of a matrix to approach eigenvalues. At the Wikipedia page they state that it was developed in the 50's and took over the LR algorithm. They also state a few variants. However I couldn't find what the state of this algorithm is today.

Is it still efficient for today's standards? Is it used in modern mathematical software to compute eigenvalues? If not, are there any variants that are considered more efficient?

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I'm no expert, but I believe QR is alive and kicking. To expand on lalala's answer, last I checked, MATLAB's general-purpose eig() function invokes LAPACK's QR routines to do its work for general matrices. For matrices that have special structure, e.g. symmetric matrices, there are alternatives that may be better, but for arbitrary matrices, QR is still the standard as far as I know.