NomadTraits Class Reference

Wrapper class for NOMAD Optimizer. More...

Inheritance diagram for NomadTraits:

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.

Detailed Description

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

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The documentation for this class was generated from the following file:

• NomadOptimizer.hpp