  In a recent paper, de Forcrand has pointed out that matrix inversions using
Gaussian noise need not be iterated to full convergence, but instead may be
solved approximately and treated as a heatbath. Gaussian noise however is not
optimal for minimizing variance. It shown here how his algorithm may be
generalized to a mixture of Gaussian and Z(N) noise, resulting in a smaller
effective variance for some operators.
