t = \frac{X \balpha_t}{\lambda n}, \quad \bw_{t+1} = \bw_t + \frac{1}{\lambda n }\bx_q \be_q \Delta\alpha_q. \]
</p>
<p>For example, consider the hinge loss as given in <a class="el" href="svm-advanced.html">Advanced SVM topics</a> : </p><p class="formulaDsp">
\[ \ell_q^*(u) = \begin{cases} y_q u, &amp; -1 \leq y_q u \leq 0, \\ +\infty, &amp; \text{otherwise}. \end{cases} \]
</p>
<p> The maximizer \(\Delta\alpha_q\) of the update objective must be in the range where the conjugate loss is not infinite. Ignor