Dakota Reference Manual
Version 6.10
Explore and Predict with Confidence

Dakota's least squares branch currently contains three methods for solving nonlinear least squares problems:
The important difference of these algorithms from generalpurpose 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.