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enA common class of nonlinear
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<div class="field field-name-comment-body field-type-text-long field-label-hidden"><div class="field-items"><div class="field-item even" property="content:encoded"><p>A common class of nonlinear least squares problems have the attribute that the final value of the sum of the squares of the functions is "small" in some relevant sense. That is, the data being "fitted" are correctly modelled by the set of functions being used.</p>
<p> For nonlinear optimization where the final function norm is not 'small', the requirement of second-order differentiability is not absolute, because the computational algorithms gradually build up approximations to the derivatives.</p>
<p>See Prof. Mittelmann's Web page at <a href="http://plato.asu.edu/sub/nlounres.html">http://plato.asu.edu/sub/nlounres.html</a> for useful links.</p>
</div></div></div>Fri, 11 Oct 2013 13:55:00 +0000Imecej4comment 1759711 at https://software.intel.com