Papers › Generalized Pseudospectral Shattering and Inverse-Free Matrix Pencil Diagonalization
Generalized Pseudospectral Shattering and Inverse-Free Matrix Pencil Diagonalization
James Demmel, Ioana Dumitriu, Ryan Schneider
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We present a randomized, inverse-free algorithm for producing an approximate diagonalization of any n ×n matrix pencil (A,B). The bulk of the algorithm rests on a randomized divide-and-conquer eigensolver for the generalized eigenvalue problem originally proposed by Ballard, Demmel, and Dumitriu [Technical Report 2010]. We demonstrate that this divide-and-conquer approach can be formulated to succeed with high probability provided the input pencil is sufficiently well-behaved, which is accomplished by generalizing the recent pseudospectral shattering work of Banks, Garza-Vargas, Kulkarni, and Srivastava [Foundations of Computational Mathematics 2022]. In particular, we show that perturbing and scaling (A,B) regularizes its pseudospectra, allowing divide-and-conquer to run over a simple random grid and in turn producing an accurate diagonalization of (A,B) in the backward error sense. The main result of the paper states the existence of a randomized algorithm that with high probability (and in exact arithmetic) produces invertible S,T and diagonal D such that ||A - SDT⁻¹||₂ ≤ε and ||B - ST⁻¹||₂ ≤ε in at most O (log² ( n/ε ) T_(MM)(n) ) operations, where T_(MM)(n) is the asymptotic complexity of matrix multiplication. This not only provides a new set of guarantees for highly parallel generalized eigenvalue solvers but also establishes nearly matrix multiplication time as an upper bound on the complexity of inverse-free, exact arithmetic matrix pencil diagonalization.
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