if A is not a covariance matrix
Ax=b; (1)
x=pseudo(A).b (2)
I can understand eq 1 and 2 that is uses least square to find x since A may not be invertible.
But if R is the covariance matrix of A
then what is the significance of the equation (3) and (4)
Rx=transpose(A)b; (3)
x=pseudo(R). transpose(A)b (4)
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