.. _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)