Wednesday, June 29, 2016

FMR: Fast randomized algorithms for covariance matrix computations - implementation -

The full poster is here.

from the poster: 

Sources are available online as part of the open-source package FMR. They can be downloaded for free at the following address 

Dependencies FMR relies on 
  • ScalFMM [1] for performing fast multipole matrix multiplication in parallel (in shared and distributed memory) 
  • MKL for dense linear algebra and FFT 
  • Scotch or CClusteringLib for partitionning Features The package provides: 
  • routines for generating Gaussian Random Fields based on
  • standard LRA: Cholesky Decomposition, SVD or FFT for regular grids. 
  • randomized LRA: RandSVD and Nystrom method with uniform or leverage score-based sampling.
    • a variety of correlation kernels: Mat´ern, Spherical model, Oseen-Gauss.
    • a Python interface for MDS using Randomized SVD or Nystrom
    • a Matlab interface for Ensemble Kalman Filtering

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