The QR algorithm computes eigenvalues through repeated QR decompositions and similarity updates. Shifted and unshifted forms have different convergence rates; the unshifted QR algorithm uses a zero shift at every step.
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The QR algorithm is a numerical procedure used to find the eigenvalues and eigenvectors of a matrix. It is based on the QR decomposition of a matrix, which factors a matrix \( A \) into a product of an orthogonal matrix \( Q \) and an upper triangular matrix \( R \). The algorithm is particularly effective for real and complex matrices and is widely used in computational linear algebra.