Cholesky {Matrix} | R Documentation |
Computes the Cholesky decomposition of a sparse, symmetric, positive-definite matrix.
Cholesky(A, perm, LDL, super, ...)
A |
sparse symmetric matrix. No missing values or IEEE special values are allowed. |
perm |
logical scalar indicating if a fill-reducing permutation
should be computed and applied to the rows and columns of A .
Default is TRUE . |
LDL |
logical scalar indicating if the decomposition should be
computed as LDL' where L is a unit lower triangular matrix.
The alternative is LL' where L is lower triangular with
arbitrary diagonal elements. Default is TRUE . |
super |
logical scalar indicating is a supernodal decomposition
should be created. The alternative is a simplicial decomposition.
Default is FALSE . |
... |
further arguments passed to or from other methods. |
This is a generic function with special methods for different types
of matrices. Use showMethods("Cholesky")
to list all
the methods for the Cholesky
generic.
The method for class dsCMatrix
of sparse matrices
is based on functions from the CHOLMOD library.
an object of class "CHMfactor"
.
Tim Davis (2005) {CHOLMOD}: sparse supernodal {Cholesky} factorization and update/downdate http://www.cise.ufl.edu/research/sparse/cholmod/
Timothy A. Davis (2006) Direct Methods for Sparse Linear Systems, SIAM Series “Fundamentals of Algorithms”.
Class definitions CHMfactor
and dsCMatrix
and function expand
.
Note that chol()
returns matrices (inheriting from
"Matrix"
) whereas Cholesky()
returns a
"CHMfactor"
object.
data(KNex) mtm <- with(KNex, crossprod(mm)) Cholesky(mtm) # uses show(<MatrixFactorization>) Cholesky(mtm, super = TRUE)