  We introduce a fast and easy-to-implement simulation algorithm for a
multivariate normal distribution truncated on the intersection of a set of
hyperplanes, and further generalize it to efficiently simulate random variables
from a multivariate normal distribution whose covariance (precision) matrix can
be decomposed as a positive-definite matrix minus (plus) a low-rank symmetric
matrix. Example results illustrate the correctness and efficiency of the
proposed simulation algorithms.
