Matrix: TSOPF/TSOPF_FS_b300_c1

Description: transient optimal power flow, Full-Space. Guangchao Geng, Zhejiang Univ

TSOPF/TSOPF_FS_b300_c1 graph
(undirected graph drawing)


TSOPF/TSOPF_FS_b300_c1 dmperm of TSOPF/TSOPF_FS_b300_c1
scc of TSOPF/TSOPF_FS_b300_c1

  • Home page of the UF Sparse Matrix Collection
  • Matrix group: TSOPF
  • Click here for a description of the TSOPF group.
  • Click here for a list of all matrices
  • Click here for a list of all matrix groups
  • download as a MATLAB mat-file, file size: 19 MB. Use UFget(2224) or UFget('TSOPF/TSOPF_FS_b300_c1') in MATLAB.
  • download in Matrix Market format, file size: 15 MB.
  • download in Rutherford/Boeing format, file size: 10 MB.

    Matrix properties
    number of rows29,214
    number of columns29,214
    nonzeros4,400,122
    structural full rank?yes
    structural rank29,214
    # of blocks from dmperm2
    # strongly connected comp.2
    explicit zero entries0
    nonzero pattern symmetrysymmetric
    numeric value symmetrysymmetric
    typereal
    structuresymmetric
    Cholesky candidate?no
    positive definite?no

    authorG. Geng
    editorT. Davis
    date2009
    kindpower network problem
    2D/3D problem?no

    Additional fieldssize and type
    bsparse 29214-by-1

    Notes:

    Transient stability-constrained optimal power flow (TSOPF) problems from     
    Guangchao Geng, Institute of Power System, College of Electrical Engineering,
    Zhejiang University, Hangzhou, 310027, China.  (genggc AT gmail DOT com).    
    Matrices in the  Full-Space (FS) group are symmetric indefinite, and are best
    solved with MA57.  Matrices in the the Reduced-Space (RS) group are best     
    solved with KLU, which for these matrices can be 10 times faster than UMFPACK
    or SuperLU.                                                                  
    

    Ordering statistics:result
    nnz(chol(P*(A+A'+s*I)*P')) with AMD66,816,930
    Cholesky flop count4.2e+11
    nnz(L+U), no partial pivoting, with AMD133,604,646
    nnz(V) for QR, upper bound nnz(L) for LU, with COLAMD53,080,815
    nnz(R) for QR, upper bound nnz(U) for LU, with COLAMD120,027,713

    SVD-based statistics:
    norm(A)1.5e+06
    min(svd(A))1.24283e-10
    cond(A)1.20692e+16
    rank(A)28,025
    sprank(A)-rank(A)1,189
    null space dimension1,189
    full numerical rank?no
    singular value gap1.02198

    singular values (MAT file):click here
    SVD method used:s = svd (full (A))
    status:ok

    TSOPF/TSOPF_FS_b300_c1 svd

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

    Maintained by Tim Davis, last updated 12-Mar-2014.
    Matrix pictures by cspy, a MATLAB function in the CSparse package.
    Matrix graphs by Yifan Hu, AT&T Labs Visualization Group.