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, NNLS2369RAB, QRM02QU2.