The normal equation for a linear inverse problem is
It has a solution exactly when
When solutions exist they form
They are unique exactly when , while is always the unique solution in and the solution of minimum norm. Here is the Moore-Penrose inverse.
Use
The adjoint operator is consequently
With a unitary Fourier normalization, this is simply .
At fixed incident direction and wavenumber, define
Taking the complex conjugate of the kernel gives
The least-squares minimizer of satisfies the normal equation for a linear inverse problem . Fourier inversion on the measured transfer-vector set is exactly the corresponding Moore--Penrose reconstruction ; hence the formal solution in part (i) is the minimum-norm least-squares solution when the data are incomplete or inconsistent.