Global Convergence of Hessenberg Shifted QR III: Approximate Ritz Values via Shifted Inverse Iteration

Abstract

We give a self-contained randomized algorithm based on shifted inverse iteration which provably computes the eigenvalues of an arbitrary matrix $M\in {\mathbb{C}}^{n\times n}$ up to backward error $\delta \Vert M\Vert$ in $O(n^4+n^3{\log }^2(n/\delta )+\log (n/\delta {)}^2\log \log (n/\delta ))$ floating point operations using $O({\log }^2(n/\delta ))$ bits of precision. While the $O(n^4)$ complexity is prohibitive for large matrices, the algorithm is simple and may be useful for provably computing the eigenvalues of small matrices using controlled precision, in particular for computing Ritz values in shifted QR algorithms as in (Banks, Garza-Vargas, Srivastava, 2022).

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