Matrix: Janna/PFlow_742

Description: 3D pressure-temp evoluation in porous media

Janna/PFlow_742 graph
(undirected graph drawing)


Janna/PFlow_742 dmperm of Janna/PFlow_742

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  • Matrix group: Janna
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  • download as a MATLAB mat-file, file size: 266 MB. Use UFget(2661) or UFget('Janna/PFlow_742') in MATLAB.
  • download in Matrix Market format, file size: 232 MB.
  • download in Rutherford/Boeing format, file size: 196 MB.

    Matrix properties
    number of rows742,793
    number of columns742,793
    nonzeros37,138,461
    structural full rank?yes
    structural rank742,793
    # of blocks from dmperm57,652
    # strongly connected comp.57,652
    explicit zero entries0
    nonzero pattern symmetrysymmetric
    numeric value symmetrysymmetric
    typereal
    structuresymmetric
    Cholesky candidate?yes
    positive definite?yes

    authorC. Janna, M. Ferronato
    editorT. Davis
    date2014
    kind2D/3D problem
    2D/3D problem?yes

    Notes:

    Janna/PFlow_742: 3D pressure-temperature evolution in porous media
                                                                      
    Authors: Carlo Janna and Massimiliano Ferronato                   
    Symmetric Positive Definite Matrix                                
    # equations:     742,793                                          
    # non-zeroes: 37,138,461                                          
                                                                      
    The matrix PFlow_742 is obtained from a 3D simulation of the      
    pressure-temperature field in a multilayered porous media         
    discretized by hexahedral Finite Elements. The ill-conditioning   
    of the matrix is due to the strong contrasts in the material      
    properties fo different layers.                                   
                                                                      
    Further information may be found in the following paper:          
                                                                      
    1) C. Janna, M. Ferronato, G. Gambolati. "The use of supernodes   
    in factored sparse approximate inverse preconditioning". SIAM     
    Journal on Scientific Computing, submitted.                       
    

    Ordering statistics:result
    nnz(chol(P*(A+A'+s*I)*P')) with AMD1,124,528,164
    Cholesky flop count5.4e+12
    nnz(L+U), no partial pivoting, with AMD2,248,313,535
    nnz(V) for QR, upper bound nnz(L) for LU, with COLAMD1,851,385,449
    nnz(R) for QR, upper bound nnz(U) for LU, with COLAMD4,000,348,915

    For a description of the statistics displayed above, click here.

    Maintained by Tim Davis, last updated 10-Jun-2015.
    Matrix pictures by cspy, a MATLAB function in the CSparse package.
    Matrix graphs by Yifan Hu, AT&T Labs Visualization Group.