Matrix: TSOPF/TSOPF_RS_b9_c6

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

TSOPF/TSOPF_RS_b9_c6 graph TSOPF/TSOPF_RS_b9_c6 graph
(bipartite graph drawing) (graph drawing of A+A')


TSOPF/TSOPF_RS_b9_c6 dmperm of TSOPF/TSOPF_RS_b9_c6
scc of TSOPF/TSOPF_RS_b9_c6

  • 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: 81 KB. Use UFget(2245) or UFget('TSOPF/TSOPF_RS_b9_c6') in MATLAB.
  • download in Matrix Market format, file size: 183 KB.
  • download in Rutherford/Boeing format, file size: 105 KB.

    Matrix properties
    number of rows7,224
    number of columns7,224
    nonzeros54,082
    structural full rank?yes
    structural rank7,224
    # of blocks from dmperm1,204
    # strongly connected comp.1,204
    explicit zero entries0
    nonzero pattern symmetry 15%
    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 7224-by-5

    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 AMD80,388
    Cholesky flop count9.0e+05
    nnz(L+U), no partial pivoting, with AMD153,552
    nnz(V) for QR, upper bound nnz(L) for LU, with COLAMD15,765
    nnz(R) for QR, upper bound nnz(U) for LU, with COLAMD86,525

    SVD-based statistics:
    norm(A)55.4816
    min(svd(A))1.79163e-05
    cond(A)3.09671e+06
    rank(A)7,224
    sprank(A)-rank(A)0
    null space dimension0
    full numerical rank?yes

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

    TSOPF/TSOPF_RS_b9_c6 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.