i.e., require more gradient evaluations) before the line search finds a good step length.</p>
<p>In addition to the above, the L-BFGS algorithm has argument <code>history_size</code> which controls the size of the history it uses to approximate the Hessian. The value should be less than the dimensionality of the parameter space and, in general, relatively small values (<span class="math inline">\(5\)</span>-<span class="math inline">\(10\)</span>) are sufficient; the default value is <span class="math inlin