Dakota Reference Manual
Version 6.2
LargeScale Engineering Optimization and Uncertainty Analysis

Newton method based leastsquares 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 Newtonbased 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 GaussNewton algorithm is available as optpp_g_newton
and supports unconstrained, boundconstrained, and generallyconstrained problems. When interfaced with the unconstrained, boundconstrained, and nonlinear interior point fullNewton optimizers from the OPT++ library, it provides a GaussNewton least squares capability which – on zeroresidual 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.
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