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mido_h_

Beginner

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10-11-2013
01:58 AM

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optimisation routines

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mecej4

Black Belt

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10-11-2013
06:55 AM

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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.

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.

See Prof. Mittelmann's Web page at http://plato.asu.edu/sub/nlounres.html for useful links.

For more complete information about compiler optimizations, see our Optimization Notice.