A miscellaneous conclusion & some limitations for the hklam statistical model theory

There may be a numerous discussions about my HKLam Statistical Model theory. In practice, the theory is about turning any giving random matrix into a linear combination through some suitable linear transformation. Through the linear combination, we may then convert it into the linear regression (without reality engineering error term). In order to fill the gap between the pure theory and the apply engineering, one may then use the machine learning (e.g. gradient descent) method to find out the expected error term. At the same time, for the inverse of the HKLam Statistical Model Theory, there may be a 1/x → infinity situation, this author thus proposes a primal duality gap (delta > 0, = 0, < 0) cases to solve such inverse problem (or alternatively, in the mathematics & engineering sense, one may apply the inverse of the Laplace transform such that L-1(1/x) = a unit).  Hence, from the inverse HKLam theory, we may find some more hidden social discussing issues or implications from the final Nash Equilibrium matrix and thus we may further make those essential and necessary improvements, policy and contributions to our society.