  In this work, we investigate the optimal map-making technique for the linear
system $d=Ax+n$ while carefully taking into account singularities that may come
from either the covariance matrix $C = \langle nn^t \rangle$ or the main matrix
$A$. We first describe the general optimal solution, which is quite complex,
and then use the modified pseudo inverse to create a near-optimal solution,
which is simple, robust, and can significantly alleviate the unwanted noise
amplification during map-making. The effectiveness of the nearly optimal
solution is then compared to that of the naive co-adding solution and the
standard pseudo inverse solution, showing noticeable improvements.
Interestingly, all one needs to get the near-optimal solution with singularity
is just a tiny change to the classical solution, which is designed for the case
without singularity.
