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Dakota
Version 6.22
Explore and Predict with Confidence
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Dakota
Version 6.22
Explore and Predict with Confidence
|
Class for provably-convergent local surrogate-based optimization and nonlinear least squares. More...
Public Member Functions | |
| DataFitSurrBasedLocalTraits () | |
| default constructor | |
| ~DataFitSurrBasedLocalTraits () override | |
| destructor | |
| bool | is_derived () override |
| A temporary query used in the refactor. | |
| bool | supports_continuous_variables () override |
| Return the flag indicating whether method supports continuous variables. | |
| bool | supports_linear_equality () override |
| Return the flag indicating whether method supports linear equalities. | |
| bool | supports_linear_inequality () override |
| Return the flag indicating whether method supports linear inequalities. | |
| bool | supports_nonlinear_equality () override |
| Return the flag indicating whether method supports nonlinear equalities. | |
| bool | supports_nonlinear_inequality () override |
| Return the flag indicating whether method supports nonlinear inequalities. | |
 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 LINEAR_INEQUALITY_FORMAT | linear_inequality_format () |
| Return the format used for linear inequality constraints. | |
| virtual NONLINEAR_EQUALITY_FORMAT | nonlinear_equality_format () |
| Return the format used for nonlinear equality constraints. | |
| virtual NONLINEAR_INEQUALITY_FORMAT | nonlinear_inequality_format () |
| Return the format used for nonlinear 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 | supports_discrete_variables () |
| Return the flag indicating whether method supports continuous variables. | |
| 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. | |
Class for provably-convergent local surrogate-based optimization and nonlinear least squares.
This minimizer uses a SurrogateModel to perform minimization based on local, global, or hierarchical surrogates. It achieves provable convergence through the use of a sequence of trust regions and the application of surrogate corrections at the trust region centers.
A version of TraitsBase specialized for local surrogate-based minimizer
1.8.20