Related: Why is this function, related to SVM derivation, non-convex?
I am studying notes which cover the derivation of SVM. The intuition is the geometric margin should be maximized in order to result in more confident predictions. The notes pose the following optimization problem:
\begin{equation*} \begin{aligned} & \max_{\gamma,w,b} & & \gamma \\ & \text{s.t.} & & y^{(i)}(w^Tx^{(i)} + b) \geq \gamma; i = 1, \ldots, m.\\ &&& \|w\| = 1. \end{aligned} \end{equation*}
$\gamma = y((\frac{w}{\|w\|})^Tx + \frac{b}{\|w\|})$
If $\|w\| = 1$ then the geometric margin equals the functional margin. However, it is stated that the constraint $\|w\| = 1$ is non-convex. I cannot intuitively see why this constraint is non-convex.
I tried graphing the function $\|w\| - 1 = 0, w \in \mathbb{R^1}$ and plugging in values to the definition of a convex function. I have not found any combination that would show this function to be non-convex.
Clearly I am lacking a fundamental understanding of this problem. Why is $\|w\| = 1$ a non-convex constraint?
This may be a hard question to answer, but do you have any suggestions for books relating to this topic that would bolster my understanding? That is very vague, but any recommendations would be great.
– Alex May 27 '15 at 20:46