The Choleski Decomposition

Description

Compute the Choleski factorization of a real symmetric positive-definite square matrix.

Usage

chol(x, pivot = FALSE,  LINPACK = pivot)

Arguments

Details

This is an interface to the LAPACK routine DPOTRF and the LINPACK routines DPOFA and DCHDC.

Note that only the upper triangular part of x is used, so that R'R = x when x is symmetric.

If pivot = FALSE and x is not non-negative definite an error occurs. If x is positive semi-definite (i.e., some zero eigenvalues) an error will also occur, as a numerical tolerance is used.

If pivot = TRUE, then the Choleski decomposition of a positive semi-definite x can be computed. The rank of x is returned as attr(Q, "rank"), subject to numerical errors. The pivot is returned as attr(Q, "pivot"). It is no longer the case that t(Q) %*% Q equals x. However, setting pivot <- attr(Q, "pivot") and oo <- order(pivot), it is true that t(Q[, oo]) %*% Q[, oo] equals x, or, alternatively, t(Q) %*% Q equals x[pivot, pivot]. See the examples.

Value

The upper triangular factor of the Choleski decomposition, i.e., the matrix R such that R'R = x (see example).
If pivoting is used, then two additional attributes "pivot" and "rank" are also returned.

Warning

The code does not check for symmetry.

If pivot = TRUE and x is not non-negative definite then there will be a warning message but a meaningless result will occur. So only use pivot = TRUE when x is non-negative definite by construction.

References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

Dongarra, J. J., Bunch, J. R., Moler, C. B. and Stewart, G. W. (1978) LINPACK Users Guide. Philadelphia: SIAM Publications.

Anderson. E. and ten others (1999) LAPACK Users' Guide. Third Edition. SIAM.
Available on-line at http://www.netlib.org/lapack/lug/lapack_lug.html.

See Also

chol2inv for its inverse (without pivoting), backsolve for solving linear systems with upper triangular left sides.

qr, svd for related matrix factorizations.

Examples

( m <- matrix(c(5,1,1,3),2,2) )
( cm <- chol(m) )
t(cm) %*% cm  #-- = 'm'
crossprod(cm)  #-- = 'm'

# now for something positive semi-definite
x <- matrix(c(1:5, (1:5)^2), 5, 2)
x <- cbind(x, x[, 1] + 3*x[, 2])
m <- crossprod(x)
qr(m)$rank # is 2, as it should be

# chol() may fail, depending on numerical rounding:
# chol() unlike qr() does not use a tolerance.
try(chol(m))

(Q <- chol(m, pivot = TRUE)) # NB wrong rank here ... see Warning section.
## we can use this by
pivot <- attr(Q, "pivot")
oo <- order(pivot)
t(Q[, oo]) %*% Q[, oo] # recover m

## now for a non-positive-definite matrix
( m <- matrix(c(5,-5,-5,3),2,2) )
try(chol(m))  # fails
try(chol(m, LINPACK=TRUE))   # fails
(Q <- chol(m, pivot = TRUE)) # warning
crossprod(Q)  # not equal to m