Matrix: TSOPF/TSOPF_RS_b162_c1

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

TSOPF/TSOPF_RS_b162_c1 graph TSOPF/TSOPF_RS_b162_c1 graph
(bipartite graph drawing) (graph drawing of A+A')


TSOPF/TSOPF_RS_b162_c1 dmperm of TSOPF/TSOPF_RS_b162_c1
scc of TSOPF/TSOPF_RS_b162_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: 692 KB. Use UFget(2232) or UFget('TSOPF/TSOPF_RS_b162_c1') in MATLAB.
  • download in Matrix Market format, file size: 1 MB.
  • download in Rutherford/Boeing format, file size: 293 KB.

    Matrix properties
    number of rows5,374
    number of columns5,374
    nonzeros205,399
    structural full rank?yes
    structural rank5,374
    # of blocks from dmperm126
    # strongly connected comp.126
    explicit zero entries0
    nonzero pattern symmetry 3%
    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 5374-by-49

    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 AMD467,856
    Cholesky flop count4.5e+07
    nnz(L+U), no partial pivoting, with AMD930,338
    nnz(V) for QR, upper bound nnz(L) for LU, with COLAMD71,180
    nnz(R) for QR, upper bound nnz(U) for LU, with COLAMD492,320

    SVD-based statistics:
    norm(A)2365.05
    min(svd(A))2.75184e-05
    cond(A)8.59445e+07
    rank(A)5,374
    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_b162_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.