Given positive constants $C$ and $\epsilon$ and points $\{ (x^i,b_i)\} _{i=1}^I \subset \mathbb{R}^{n+1}$, how can we rewrite the following optimization problem as minimizing a convex quadratic objective subject to linear constraints?
$$\min _{w\in \mathbb{R}^n} \Big[ \frac{C}{2}w^Tw+ \sum_{i=1}^I \max \{ |w^Tx^i-b_i|-\epsilon,0 \} \Big]$$