Dakota
Version 6.20
Explore and Predict with Confidence
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Wrapper class for the NPSOL optimization library. More...
Public Member Functions | |
NPSOLTraits () | |
default constructor | |
virtual | ~NPSOLTraits () |
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_linear_equality () |
Return the flag indicating whether method supports linear equalities. | |
bool | supports_linear_inequality () |
Return the flag indicating whether method supports linear inequalities. | |
bool | supports_nonlinear_equality () |
Return the flag indicating whether method supports nonlinear equalities. | |
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 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 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. | |
Wrapper class for the NPSOL optimization library.
The NPSOLOptimizer class provides a wrapper for NPSOL, a Fortran 77 sequential quadratic programming library from Stanford University marketed by Stanford Business Associates. It uses a function pointer approach for which passed functions must be either global functions or static member functions. Any attribute used within static member functions must be either local to that function or accessed through a static pointer.
The user input mappings are as follows: max_function_evaluations
is implemented directly in NPSOLOptimizer's evaluator functions since there is no NPSOL parameter equivalent, and max_iterations
, convergence_tolerance
, output
verbosity, verify_level
, function_precision
, and linesearch_tolerance
are mapped into NPSOL's "Major Iteration Limit", "Optimality Tolerance", "Major
Print Level" (verbose:
Major Print Level = 20; quiet:
Major Print Level = 10), "Verify Level", "Function Precision", and "Linesearch Tolerance" parameters, respectively, using NPSOL's npoptn() subroutine (as wrapped by npoptn2() from the sol_optn_wrapper.f file). Refer to [Gill, P.E., Murray, W., Saunders, M.A., and Wright, M.H., 1986] for information on NPSOL's optional input parameters and the npoptn() subroutine.
A version of TraitsBase specialized for NPSOL optimizers