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arXiv · 1412.5071

Probabilistic analysis of Wiedemann's algorithm for minimal polynomial computation

Gavin Harrison·Jeremy Johnson·B. David Saunders
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Abstract

Blackbox algorithms for linear algebra problems start with projection of the sequence of powers of a matrix to a sequence of vectors (Lanczos), a sequence of scalars (Wiedemann) or a sequence of smaller matrices (block methods). Such algorithms usually depend on the minimal polynomial of the resulting sequence being that of the given matrix. Here exact formulas are given for the probability that this occurs. They are based on the generalized Jordan normal form (direct sum of companion matrices of the elementary divisors) of the matrix. Sharp bounds follow from this for matrices of unknown elementary divisors. The bounds are valid for all finite field sizes and show that a small blocking factor can give high probability of success for all cardinalities and matrix dimensions.

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BibTeXRIS

Gavin Harrison, Jeremy Johnson, B. David Saunders. 2014-12-16. Probabilistic analysis of Wiedemann's algorithm for minimal polynomial computation. https://doi.org/10.1016/j.jsc.2015.06.005

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