1. If the matrix is diagonal, solve directly and goto 8
2. If the matrix is a permuted diagonal, solve directly taking into
account the permutations. Goto 8
3. If the matrix is square, banded and if the band density is less
than that given by `spparms ("bandden")' continue, else goto 4.
a. If the matrix is tridiagonal and the right-hand side is not
sparse continue, else goto 3b.
1. If the matrix is hermitian, with a positive real
diagonal, attempt Cholesky factorization using
LAPACK xPTSV.
2. If the above failed or the matrix is not hermitian with
a positive real diagonal use Gaussian elimination
with pivoting using LAPACK xGTSV, and goto 8.
b. If the matrix is hermitian with a positive real diagonal,
attempt Cholesky factorization using LAPACK xPBTRF.
c. if the above failed or the matrix is not hermitian with a
positive real diagonal use Gaussian elimination with
pivoting using LAPACK xGBTRF, and goto 8.
4. If the matrix is upper or lower triangular perform a sparse forward
or backward substitution, and goto 8
5. If the matrix is a upper triangular matrix with column permutations
or lower triangular matrix with row permutations, perform a sparse
forward or backward substitution, and goto 8
6. If the matrix is square, hermitian with a real positive diagonal,
attempt sparse Cholesky factorization using CHOLMOD.
7. If the sparse Cholesky factorization failed or the matrix is not
hermitian with a real positive diagonal, and the matrix is square,
factorize using UMFPACK.
8. If the matrix is not square, or any of the previous solvers flags
a singular or near singular matrix, find a minimum norm solution
using CXSPARSE(1).
You are probably having problems with SuperLU with 2.1 and they will go
away with 2.9. Note that you need to install UFSparse to get the full
benefits of the above, otherwise large parts are deactivated