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A fast randomized algorithm for overdetermined linear least-squares regression

We introduce a randomized algorithm for overdetermined linear least-squares regression. Given an arbitrary full-rank m × n matrix A with m ≥ n, any m × 1 vector b, and any positive real number ε, the procedure computes an n × 1 vector x such that x minimizes the Euclidean norm ‖Ax − b‖ to relative p...

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Detalles Bibliográficos
Main Authors: Rokhlin, Vladimir, Tygert, Mark
Formato: Artigo
Idioma:Inglês
Publicado: National Academy of Sciences 2008
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Acceso en liña:https://ncbi.nlm.nih.gov/pmc/articles/PMC2734343/
https://ncbi.nlm.nih.gov/pubmed/18779559
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1073/pnas.0804869105
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