For a SVM with soft margin, we want to minimize the following: $$ \lambda||\hat w||^2 +(1/n)\sum max(0,1-y_i(\hat w \hat x_i -b)) $$
we know that $2/||\hat w||$ is the width of the margin.
The second term penalizes a misclassified point for how far away it is from the margin relative to the width of the margin. E.g., suppose there is a misclassified point $x_0$: $$ 1-y_0(\hat w \hat x_0 -b)=3 $$ That means $x_0$ is $3/||\hat w||$ away from $1-y_i(\hat w \hat x_i -b)=0$ and is penalized for $3$.
The first term penalizes for the inverse of the width of the margin squared. I find it hard to reconcile with the second term, they seem to be of different scales. Is there any reason (intuitively) why $||\hat w||^2$ is used instead of just $||\hat w||$?
PS: Perhaps one reason is that $||\hat w||^2 $ is easier computation-wise (quadratic programming)? Or perhaps norm squared assumes sample noise to be Gaussian? I am not sure. Has anyone seen the use of $||\hat w||$ instead of $||\hat w||^2$?