The known algorithms for linear systems of equations perform significantly slower where the input matrix is ill conditioned, that is lies near a matrix of a smaller rank. The known methods counter this problem only for some important but special input classes, but our novel randomized augmentation techniques serve as a remedy for a typical ill conditioned input and similarly facilitates computations with rank deficient input matrices. The resulting acceleration is dramatic, both in terms of the proved bit-operation cost bounds and the actual CPU time observed in our tests. Our methods can be effectively applied to various other fundamental matrix and polynomial computations as well. © 2010 Springer-Verlag.
CITATION STYLE
Pan, V. Y., Qian, G., & Zheng, A. L. (2010). Advancing matrix computations with randomized preprocessing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6072 LNCS, pp. 303–314). https://doi.org/10.1007/978-3-642-13182-0_28
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