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Dakota Reference Manual
Version 6.15
Explore and Predict with Confidence
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Dakota's least squares branch currently contains three methods for solving nonlinear least squares problems:
The important difference of these algorithms from general-purpose optimization methods is that the response set is defined by calibration terms (e.g. separate terms for each residual), rather than an objective function. Thus, a finer granularity of data is used by least squares solvers as compared to that used by optimizers. This allows the exploitation of the special structure provided by a sum of squares objective function.