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Dakota Reference Manual
Version 6.15
Explore and Predict with Confidence
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Quasi-Newton optimization method
This keyword is related to the topics:
Alias: none
Argument(s): none
Child Keywords:
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 | max_iterations | Number of iterations allowed for optimizers and adaptive UQ methods | ||
Optional | convergence_tolerance | Stopping criterion based on objective function or statistics convergence | ||
Optional | speculative | Compute speculative gradients | ||
Optional | max_function_evaluations | Number of function evaluations allowed for optimizers | ||
Optional | scaling | Turn on scaling for variables, responses, and constraints | ||
Optional | model_pointer | Identifier for model block to be used by a method |
This is a Newton method that expects a gradient and computes a low-rank approximation to the Hessian. Each of the Newton-based methods are automatically bound to the appropriate OPT++ algorithm based on the user constraint specification (unconstrained, bound-constrained, or generally-constrained). In the generally-constrained case, the Newton methods use a nonlinear interior-point approach to manage the constraints.
See package_optpp for info related to all optpp
methods.
Expected HDF5 Output
If Dakota was built with HDF5 support and run with the hdf5 keyword, this method writes the following results to HDF5: