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Theory (and some practice) of Randomized Algorithms for Matrices and Data

Theory (and some practice) of Randomized Algorithms for Matrices and Data

Theory (and some practice) of Randomized Algorithms for Matrices and Data. Michael W. Mahoney Stanford University October 2012 (For more info, see: http://cs.stanford.edu/people/mmahoney). Matrix computations. Eigendecompositions, QR, SVD, least-squares, etc. Traditional algorithms:

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878 views • 79 slides
Randomized Numerical Linear Algebra ( RandNLA) : Past, Present, and Future, cont.

Randomized Numerical Linear Algebra ( RandNLA) : Past, Present, and Future, cont.

Randomized Numerical Linear Algebra ( RandNLA) : Past, Present, and Future, cont. Petros Drineas 1 & Michael W. Mahoney 2 1 Department of Computer Science, Rensselaer Polytechnic Institute 2 Department of Mathematics, Stanford University. To access our web pages:. Drineas.

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901 views • 72 slides
Randomized algorithms for matrices and data

Randomized algorithms for matrices and data

Randomized algorithms for matrices and data. Michael W. Mahoney Stanford University November 2011 (For more info, see: http://cs.stanford.edu/people/mmahoney). Matrix computations. Eigendecompositions, QR, SVD, least-squares, etc. Traditional algorithms:

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531 views • 50 slides
Randomized matrix algorithms and their applications

Randomized matrix algorithms and their applications

Randomized matrix algorithms and their applications. Petros Drineas Rensselaer Polytechnic Institute Computer Science Department. To access my web page:. drineas. Randomized algorithms. Randomization and sampling allow us to design provably accurate algorithms for problems that are:

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564 views • 38 slides

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