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 https://gforge.inria.fr/projects/fmr 
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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