Matrix: TSOPF/TSOPF_RS_b39_c30

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

TSOPF/TSOPF_RS_b39_c30 graph TSOPF/TSOPF_RS_b39_c30 graph
(bipartite graph drawing) (graph drawing of A+A')

TSOPF/TSOPF_RS_b39_c30 dmperm of TSOPF/TSOPF_RS_b39_c30
scc of TSOPF/TSOPF_RS_b39_c30

  • 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: 2 MB. Use UFget(2241) or UFget('TSOPF/TSOPF_RS_b39_c30') in MATLAB.
  • download in Matrix Market format, file size: 3 MB.
  • download in Rutherford/Boeing format, file size: 2 MB.

    Matrix properties
    number of rows60,098
    number of columns60,098
    nonzeros1,079,986
    structural full rank?yes
    structural rank60,098
    # of blocks from dmperm3,011
    # strongly connected comp.3,011
    explicit zero entries0
    nonzero pattern symmetry 6%
    numeric value symmetry 0%
    typereal
    structureunsymmetric
    Cholesky candidate?no
    positive definite?no

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

    Additional fieldssize and type
    bsparse 60098-by-19

    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 AMD2,230,846
    Cholesky flop count8.7e+07
    nnz(L+U), no partial pivoting, with AMD4,401,594
    nnz(V) for QR, upper bound nnz(L) for LU, with COLAMD317,062
    nnz(R) for QR, upper bound nnz(U) for LU, with COLAMD2,319,252

    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.