The hinge loss function (summed over $m$ examples):
$$ l(w)= \sum_{i=1}^{m} \max\{0 ,1-y_i(w^{\top} \cdot x_i)\} $$
My calculation of the subgradient for a single component and example is:
$$ l(z) = \max\{0, 1 - yz\} $$ $$ l^{\prime}(z) = \max\{0, - y\} $$ $$ z(w) = w \cdot x $$ $$ z^{\prime}(w) = x $$ $$ \frac{\partial l}{\partial z}\frac{\partial z}{\partial w} = \max\{0 \cdot x, - y \cdot x\} = \max\{0, - yx\} $$
For vectors:
$$ l^{\prime}(w) = \sum_{i=1}^{m} \max\{0 ,-(y_i \cdot x_i)\} $$
But the answer I have been given is:
I don't understand this notation. Have I arrived at the same solution, and can someone explain the notation?
$$\mathbb{I}_A(x)=\begin{cases} 1 & , x \in A \\ 0 & , x \notin A\end{cases}$$
is the indicator function
Hence for each $i$, it will first check if $y_i(w^Tx_i)<1$, if it is not, the corresponding value is $0$.
If it is $y_i(w^Tx_i)<1$ is satisfied, $-y_ix_i$ is added to the sum.
We can see that the two quantities are not the same as your result does not take $w$ into consideration.
Remark: Yes, the function is not differentiable, but it is convex. Subgradient is used here.