Dakota
Version 6.19
Explore and Predict with Confidence
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Wrapper class for NOMAD Optimizer. More...
Public Member Functions | |
NomadTraits () | |
default constructor | |
virtual | ~NomadTraits () |
destructor | |
virtual bool | is_derived () |
A temporary query used in the refactor. | |
bool | supports_continuous_variables () |
Return the flag indicating whether method supports continuous variables. | |
bool | supports_discrete_variables () |
Return the flag indicating whether method supports discrete variables. | |
bool | supports_nonlinear_equality () |
Return the flag indicating whether method supports nonlinear equalities. | |
NONLINEAR_EQUALITY_FORMAT | nonlinear_equality_format () |
Return the format used for nonlinear equality constraints. | |
bool | supports_nonlinear_inequality () |
Return the flag indicating whether method supports nonlinear inequalities. | |
NONLINEAR_INEQUALITY_FORMAT | nonlinear_inequality_format () |
Return the format used for nonlinear inequality constraints. | |
Public Member Functions inherited from TraitsBase | |
TraitsBase () | |
default constructor | |
virtual | ~TraitsBase () |
destructor | |
virtual bool | requires_bounds () |
Return the flag indicating whether method requires bounds. | |
virtual bool | supports_linear_equality () |
Return the flag indicating whether method supports linear equalities. | |
virtual bool | supports_linear_inequality () |
Return the flag indicating whether method supports linear inequalities. | |
virtual LINEAR_INEQUALITY_FORMAT | linear_inequality_format () |
Return the format used for linear inequality constraints. | |
virtual bool | expects_nonlinear_inequalities_first () |
Return the flag indicating whether method expects nonlinear inequality constraints followed by nonlinear equality constraints. | |
virtual bool | supports_scaling () |
Return the flag indicating whether method supports parameter scaling. | |
virtual bool | supports_least_squares () |
Return the flag indicating whether method supports least squares. | |
virtual bool | supports_multiobjectives () |
Return flag indicating whether method supports multiobjective optimization. | |
virtual bool | provides_best_objective () |
Return the flag indicating whether method provides best objective result. | |
virtual bool | provides_best_parameters () |
Return the flag indicating whether method provides best parameters result. | |
virtual bool | provides_best_constraint () |
Return the flag indicating whether method provides best constraint result. | |
virtual bool | provides_final_gradient () |
Return the flag indicating whether method provides final gradient result. | |
virtual bool | provides_final_hessian () |
Return the flag indicating whether method provides final hessian result. | |
Wrapper class for NOMAD Optimizer.
NOMAD (is a Nonlinear Optimization by Mesh Adaptive Direct search) is a simulation-based optimization package designed to efficiently explore a design space using Mesh Adaptive Search.
Mesh Adaptive Direct Search uses Meshes, discretizations of the domain space of variables. It generates multiple meshes, and as its name implies, it also adapts the refinement of the meshes in order to find the best solution of a problem.
The objective of each iteration is to find points in a mesh that improves the current solution. If a better solution is not found, the next iteration is done over a finer mesh.
Each iteration is composed of two steps: Search and Poll. The Search step finds any point in the mesh in an attempt to find an improvement; while the Poll step generates trial mesh points surrounding the current best current solution.
The NomadOptimizer is a wrapper for the NOMAD library. It features the following attributes: max_function_evaluations
, display_format
, display_all_evaluations
, function_precision
, max_iterations
.
A version of TraitsBase specialized for Nomad