sparseinv (version 0.1.3)

Takahashi_Davis: Takahashi equations

Description

Computes the sparse inverse subset of a sparse matrix Q using the Takahashi equations.

Usage

Takahashi_Davis(Q = NULL, cholQp = NULL, return_perm_chol = 0, P = 0,
  gc = 0)

Arguments

Q

precision matrix of class matrix, Matrix (column-compressed, i.e., dgCMatrix or dsCMatrix), or spam

cholQp

the Cholesky factor of class dtCMatrix of the permuted Q (if known already). If both Q and cholQp are specified, Q is ignored

return_perm_chol

if 1, the Cholesky factor of the permuted Q is returned

P

the permutation matrix of class dgCMatrix (if known already)

gc

do garbage collection throughout (may increase computational time but useful for small memory machines)

Value

if return_perm_chol == 0, the sparse inverse subset of Q is returned, where the non-zero elements correspond to those in the Cholesky factor of its permutation. If !(return_perm_chol == 0), a list with three elements is returned: S (the sparse inverse subset), Lp (the Cholesky factor of the permuted matrix) and P (the permutation matrix)

Details

This function first computes the Cholesky factor of Q. The fill-in reduction permutation is the approximate minimum degree permutation (amd) of Timothy Davis' SuiteSparse package configured to be slightly more aggressive than that in the Matrix package. The function then uses the Takahashi equations to compute the variances at the non-zero locations in the Cholesky factor from the factor itself. The equations themselves are implemented in C using the SparseSuite package of Timothy Davis.

References

Takahashi, K., Fagan, J., Chin, M.-S., 1973. Formation of a sparse bus impedance matrix and its application to short circuit study. 8th PICA Conf. Proc. June 4--6, Minneapolis, Minn.

Davis, T. A., 2014. sparseinv: Sparse Inverse Subset. URL https://au.mathworks.com/matlabcentral/fileexchange/33966-sparseinv--sparse-inverse-subset Davis, T. A., 2006. Direct Methods for Sparse Linear Systems. SIAM, Philadelphia, PA.

Examples

# NOT RUN {
require(Matrix)
Q = sparseMatrix(i = c(1, 1, 2, 2),
                 j = c(1, 2, 1, 2),
                 x = c(0.1, 0.2, 0.2, 1))
X <- cholPermute(Q)
S_partial = Takahashi_Davis(Q, cholQp = X$Qpermchol, P = X$P)
# }