A Dakota plugin for ropt
The ropt-dakota package extends the ropt
module by providing a plugin that integrates optimization algorithms from the
Dakota toolkit. ropt itself is a robust
optimization framework designed for both continuous and discrete optimization
workflows and is extensible through its plugin architecture. Installing
ropt-dakota makes these Dakota algorithms directly available within ropt.
Reference
Bases: Optimizer
Dakota optimization backend for ropt.
This class provides an interface to several optimization algorithms from
Dakota, enabling their use within ropt.
To select an optimizer, set the method field within the
optimizer section of the
EnOptConfig configuration object to the
desired algorithm's name. Most methods support the general options defined
in the EnOptConfig object. For
algorithm-specific options, use the options dictionary within the
optimizer section.
The table below lists the included methods together with the method-specific
options that are supported. Click on the method name to consult the
corresponding Dakota documentation:
| Method | Method Options |
|---|---|
| optpp_q_newton | search_method, merit_function, steplength_to_boundary, centering_parameter, max_step, gradient_tolerance, max_iterations, convergence_tolerance, max_function_evaluations |
| conmin_mfd | max_iterations, convergence_tolerance, constraint_tolerance, max_function_evaluations |
| conmin_frcg | max_iterations, convergence_tolerance, constraint_tolerance, max_function_evaluations |
| mesh_adaptive_search | initial_delta, variable_tolerance, function_precision, seed, history_file, display_format, variable_neighborhood_search, neighbor_order, display_all_evaluations, use_surrogate, max_iterations, max_function_evaluations |
| coliny_ea | population_size, initialization_type, fitness_type, replacement_type, crossover_rate, crossover_type, mutation_rate, mutation_type, constraint_penalty, solution_target, seed, show_misc_options, misc_options, max_iterations, convergence_tolerance, max_function_evaluations |
| soga | fitness_type, replacement_type, convergence_type, max_iterations, max_function_evaluations, population_size, print_each_pop, initialization_type, crossover_type, crossover_rate, mutation_type, mutation_rate, seed, convergence_tolerance |
| moga | fitness_type, replacement_type, niching_type, convergence_type, postprocessor_type, max_iterations, max_function_evaluations, population_size, print_each_pop, initialization_type, crossover_type, crossover_rate, mutation_type, mutation_rate, seed, convergence_tolerance |
| asynch_pattern_search | initial_delta, contraction_factor, variable_tolerance, solution_target, merit_function, constraint_penalty, smoothing_factor, constraint_tolerance, max_function_evaluations |