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Dakota Reference Manual
Version 6.2
Large-Scale Engineering Optimization and Uncertainty Analysis
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Dakota Reference Manual
Version 6.2
Large-Scale Engineering Optimization and Uncertainty Analysis
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Newton method based least-squares calbration
This keyword is related to the topics:
Alias: none
Argument(s): none
| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description | |
|---|---|---|---|---|
| Optional | search_method | Select a search method for Newton-based optimizers | ||
| Optional | merit_function | Balance goals of reducing objective function and satisfying constraints | ||
| Optional | steplength_to_boundary | Controls how close to the boundary of the feasible region the algorithm is allowed to move | ||
| Optional | centering_parameter | Controls how closely the algorithm should follow the "central path" | ||
| Optional | max_step | Max change in design point | ||
| Optional | gradient_tolerance | Stopping critiera based on L2 norm of gradient | ||
| Optional | linear_inequality_constraint_matrix | Define coefficients of the linear inequality constraints | ||
| Optional | linear_inequality_lower_bounds | Define lower bounds for the linear inequality constraint | ||
| Optional | linear_inequality_upper_bounds | Define upper bounds for the linear inequality constraint | ||
| Optional | linear_inequality_scale_types | Specify how each linear inequality constraint is scaled | ||
| Optional | linear_inequality_scales | Define the characteristic values to scale linear inequalities | ||
| Optional | linear_equality_constraint_matrix | Define coefficients of the linear equalities | ||
| Optional | linear_equality_targets | Define target values for the linear equality constraints | ||
| Optional | linear_equality_scale_types | Specify how each linear equality constraint is scaled | ||
| Optional | linear_equality_scales | Define the characteristic values to scale linear equalities | ||
| Optional | model_pointer | Identifier for model block to be used by a method | ||
The Gauss-Newton algorithm is available as optpp_g_newton and supports unconstrained, bound-constrained, and generally-constrained problems. When interfaced with the unconstrained, bound-constrained, and nonlinear interior point full-Newton optimizers from the OPT++ library, it provides a Gauss-Newton least squares capability which – on zero-residual test problems – can exhibit quadratic convergence rates near the solution. (Real problems almost never have zero residuals, i.e., perfect fits.)
See package_optpp for info related to all optpp methods.
These keywords may also be of interest:
1.8.4 
