** 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). **

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** The classical linear algebra solution to this problem is: **

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** x = inv(A'*inv(V)*A)*A'*inv(V)*b **

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** See also SLASH , NNLS , QR . **

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