Least squares solution in the presence of known covariance.
X = LSCOV(A,b,V) returns the vector X that minimizes
(A*X-b)'*inv(V)*(A*X-b) for the case in which length(b) > length(X).
This is the over-determined least squares problem with covariance V.
The solution is found without needing to invert V which is a square
symmetric matrix with dimensions equal to length(b).
The classical linear algebra solution to this problem is:
x = inv(A'*inv(V)*A)*A'*inv(V)*b
See also SLASH1MJV463, NNLS , QR .