.. _method-coliny_solis_wets: """"""""""""""""" coliny_solis_wets """"""""""""""""" Simple greedy local search method **Topics** package_scolib, package_coliny .. toctree:: :hidden: :maxdepth: 1 method-coliny_solis_wets-contract_after_failure method-coliny_solis_wets-no_expansion method-coliny_solis_wets-expand_after_success method-coliny_solis_wets-constant_penalty method-coliny_solis_wets-contraction_factor method-coliny_solis_wets-constraint_penalty method-coliny_solis_wets-initial_delta method-coliny_solis_wets-variable_tolerance method-coliny_solis_wets-solution_target method-coliny_solis_wets-seed method-coliny_solis_wets-show_misc_options method-coliny_solis_wets-misc_options method-coliny_solis_wets-max_iterations method-coliny_solis_wets-convergence_tolerance method-coliny_solis_wets-max_function_evaluations method-coliny_solis_wets-scaling method-coliny_solis_wets-model_pointer **Specification** - *Alias:* None - *Arguments:* None **Child Keywords:** +-------------------------+--------------------+------------------------------+---------------------------------------------+ | Required/Optional | Description of | Dakota Keyword | Dakota Keyword Description | | | Group | | | +=========================+====================+==============================+=============================================+ | Optional | `contract_after_failure`__ | The number of unsuccessful cycles prior to | | | | contraction. | +----------------------------------------------+------------------------------+---------------------------------------------+ | Optional | `no_expansion`__ | Don't allow expansion of the search pattern | +----------------------------------------------+------------------------------+---------------------------------------------+ | Optional | `expand_after_success`__ | Set the factor by which a search pattern | | | | can be expanded | +----------------------------------------------+------------------------------+---------------------------------------------+ | Optional | `constant_penalty`__ | Use a simple weighted penalty to manage | | | | feasibility | +----------------------------------------------+------------------------------+---------------------------------------------+ | Optional | `contraction_factor`__ | Amount by which step length is rescaled | +----------------------------------------------+------------------------------+---------------------------------------------+ | Optional | `constraint_penalty`__ | Multiplier for the penalty function | +----------------------------------------------+------------------------------+---------------------------------------------+ | Optional | `initial_delta`__ | Initial step size for derivative-free | | | | optimizers | +----------------------------------------------+------------------------------+---------------------------------------------+ | Optional | `variable_tolerance`__ | Step length-based stopping criteria for | | | | derivative-free optimizers | +----------------------------------------------+------------------------------+---------------------------------------------+ | Optional | `solution_target`__ | Stopping criteria based on objective | | | | function value | +----------------------------------------------+------------------------------+---------------------------------------------+ | Optional | `seed`__ | Seed of the random number generator | +----------------------------------------------+------------------------------+---------------------------------------------+ | Optional | `show_misc_options`__ | Show algorithm parameters not exposed in | | | | Dakota input | +----------------------------------------------+------------------------------+---------------------------------------------+ | Optional | `misc_options`__ | Set method options not available through | | | | Dakota spec | +----------------------------------------------+------------------------------+---------------------------------------------+ | 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 | `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 | +----------------------------------------------+------------------------------+---------------------------------------------+ .. __: method-coliny_solis_wets-contract_after_failure.html __ method-coliny_solis_wets-no_expansion.html __ method-coliny_solis_wets-expand_after_success.html __ method-coliny_solis_wets-constant_penalty.html __ method-coliny_solis_wets-contraction_factor.html __ method-coliny_solis_wets-constraint_penalty.html __ method-coliny_solis_wets-initial_delta.html __ method-coliny_solis_wets-variable_tolerance.html __ method-coliny_solis_wets-solution_target.html __ method-coliny_solis_wets-seed.html __ method-coliny_solis_wets-show_misc_options.html __ method-coliny_solis_wets-misc_options.html __ method-coliny_solis_wets-max_iterations.html __ method-coliny_solis_wets-convergence_tolerance.html __ method-coliny_solis_wets-max_function_evaluations.html __ method-coliny_solis_wets-scaling.html __ method-coliny_solis_wets-model_pointer.html **Description** The Solis-Wets method is a simple greedy local search heuristic for continuous parameter spaces. Solis-Wets generates trial points using a multivariate normal distribution, and unsuccessful trial points are reflected about the current point to find a descent direction. .. note:: See the page :ref:`topic-package_scolib` for important information regarding all SCOLIB methods. ``coliny_solis_wets`` is inherently serial, no concurrency is used. These specifications have the same meaning as corresponding specifications for :dakkw:`method-coliny_pattern_search`. Please see that page for specification details. In particular, ``coliny_solis_wets`` supports dynamic rescaling of the step length, and dynamic rescaling of the constraint penalty. The only new specification is ``contract_after_failure``, which specifies the number of unsuccessful cycles which must occur with a specific delta prior to contraction of the delta. *Expected HDF5 Output* If Dakota was built with HDF5 support and run with the :dakkw:`environment-results_output-hdf5` keyword, this method writes the following results to HDF5: - :ref:`hdf5_results-best_params` - :ref:`hdf5_results-best_obj_fncs` (when :dakkw:`responses-objective_functions`) are specified) - :ref:`hdf5_results-best_constraints` - :ref:`hdf5_results-calibration` (when :dakkw:`responses-calibration_terms` are specified)