then so what's happening here is that if you're able to change the predictions f of X by an order one quantity while the Jacobian the gradient stay constant in a relative sense then the second order term is essentially vanishing the second order term is how fast this changes so if I can move f of X by order one which is all I need to move it because I need to move it to match Y well this stays constant then I'm done I've found that I've constructed a global minimizer that is very close to my initialization close