chol {base} | R Documentation |

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

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

`x` |
a real symmetric, positive-definite matrix |

`pivot` |
Should pivoting be used? |

`LINPACK` |
logical. Should LINPACK be used in the non-pivoting
case (for compatibility with R < 1.7.0)? |

`chol(pivot = TRUE)`

provides an interface to the LINPACK routine DCHDC.
`La.chol`

provides an interface to the LAPACK routine DPOTRF.

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.

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.

The code does not check for symmetry.

If `pivot = TRUE`

and `x`

is not non-negative
definite then there will be no error message but a meaningless
result will occur. So only use `pivot = TRUE`

when `x`

is
non-negative definite by construction.

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.

`chol2inv`

for its *inverse* (without pivoting),
`backsolve`

for solving linear systems with upper
triangular left sides.

`qr`

, `svd`

for related matrix factorizations.

( 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

[Package *base* version 2.2.1 Index]