I'm trying to follow Andrew Ng's notes on Support Vector Machines and had the following question.
In his notes, Ng, transforms the following optimization problem [using the notion of geometric margin] of the SVM
into the following equivalent problem [using the notion of functional margin]
My question is this: how are the conditions the same? I understand how $\gamma = \frac{\hat{\gamma}}{\Vert w\Vert}$, but what is the proof of the equivalence of the "s.t." conditions?