SNLLOptimizer Class Reference

Wrapper class for the OPT++ optimization library. More...

Inheritance diagram for SNLLOptimizer:

List of all members.

Public Member Functions
	SNLLOptimizer (Model &model)
	standard constructor
	SNLLOptimizer (const String &method_name, Model &model)
	alternate constructor for instantiations "on the fly"
	SNLLOptimizer (const RealVector &initial_point, const RealVector &var_lower_bnds, const RealVector &var_upper_bnds, const RealMatrix &lin_ineq_coeffs, const RealVector &lin_ineq_lower_bnds, const RealVector &lin_ineq_upper_bnds, const RealMatrix &lin_eq_coeffs, const RealVector &lin_eq_targets, const RealVector &nonlin_ineq_lower_bnds, const RealVector &nonlin_ineq_upper_bnds, const RealVector &nonlin_eq_targets, void(*user_obj_eval)(int mode, int n, const NEWMAT::ColumnVector &x, NEWMAT::Real &f, NEWMAT::ColumnVector &grad_f, int &result_mode), void(*user_con_eval)(int mode, int n, const NEWMAT::ColumnVector &x, NEWMAT::ColumnVector &g, NEWMAT::Matrix &grad_g, int &result_mode))
	alternate constructor for instantiations "on the fly"
	~SNLLOptimizer ()
	destructor
void	find_optimum ()
	Performs the iterations to determine the optimal solution.
Protected Member Functions
void	derived_pre_run ()
	and performs other set-up
void	derived_post_run ()
	and performs other data recovery
Static Private Member Functions
static void	nlf0_evaluator (int n, const NEWMAT::ColumnVector &x, NEWMAT::Real &f, int &result_mode)
	require only function values.
static void	nlf1_evaluator (int mode, int n, const NEWMAT::ColumnVector &x, NEWMAT::Real &f, NEWMAT::ColumnVector &grad_f, int &result_mode)
	values and gradients to OPT++ methods.
static void	nlf2_evaluator (int mode, int n, const NEWMAT::ColumnVector &x, NEWMAT::Real &f, NEWMAT::ColumnVector &grad_f, NEWMAT::SymmetricMatrix &hess_f, int &result_mode)
	values, gradients, and Hessians to OPT++ methods.
static void	constraint0_evaluator (int n, const NEWMAT::ColumnVector &x, NEWMAT::ColumnVector &g, int &result_mode)
	only constraint values.
static void	constraint1_evaluator (int mode, int n, const NEWMAT::ColumnVector &x, NEWMAT::ColumnVector &g, NEWMAT::Matrix &grad_g, int &result_mode)
	values and gradients to OPT++ methods.
static void	constraint2_evaluator (int mode, int n, const NEWMAT::ColumnVector &x, NEWMAT::ColumnVector &g, NEWMAT::Matrix &grad_g, OPTPP::OptppArray< NEWMAT::SymmetricMatrix > &hess_g, int &result_mode)
	values, gradients, and Hessians to OPT++ methods.
Private Attributes
SNLLOptimizer *	prevSnllOptInstance
	restoration in the case of iterator/model recursion
OPTPP::NLP0 *	nlfObjective
	objective NLF base class pointer
OPTPP::NLP0 *	nlfConstraint
	constraint NLF base class pointer
OPTPP::NLP *	nlpConstraint
	constraint NLP pointer
OPTPP::NLF0 *	nlf0
	pointer to objective NLF for nongradient optimizers
OPTPP::NLF1 *	nlf1
	pointer to objective NLF for (analytic) gradient-based optimizers
OPTPP::NLF1 *	nlf1Con
	pointer to constraint NLF for (analytic) gradient-based optimizers
OPTPP::FDNLF1 *	fdnlf1
	pointer to objective NLF for (finite diff) gradient-based optimizers
OPTPP::FDNLF1 *	fdnlf1Con
	pointer to constraint NLF for (finite diff) gradient-based optimizers
OPTPP::NLF2 *	nlf2
	pointer to objective NLF for full Newton optimizers
OPTPP::NLF2 *	nlf2Con
	pointer to constraint NLF for full Newton optimizers
OPTPP::OptimizeClass *	theOptimizer
	optimizer base class pointer
OPTPP::OptPDS *	optpds
	PDS optimizer pointer.
OPTPP::OptCG *	optcg
	CG optimizer pointer.
OPTPP::OptLBFGS *	optlbfgs
	L-BFGS optimizer pointer.
OPTPP::OptNewton *	optnewton
	Newton optimizer pointer.
OPTPP::OptQNewton *	optqnewton
	Quasi-Newton optimizer pointer.
OPTPP::OptFDNewton *	optfdnewton
	Finite Difference Newton opt pointer.
OPTPP::OptBCNewton *	optbcnewton
	Bound constrained Newton opt pointer.
OPTPP::OptBCQNewton *	optbcqnewton
	Bnd constrained Quasi-Newton opt ptr.
OPTPP::OptBCFDNewton *	optbcfdnewton
	Bnd constrained FD-Newton opt ptr.
OPTPP::OptNIPS *	optnips
	NIPS optimizer pointer.
OPTPP::OptQNIPS *	optqnips
	Quasi-Newton NIPS optimizer pointer.
OPTPP::OptFDNIPS *	optfdnips
	Finite Difference NIPS opt pointer.
String	setUpType
	NonDReliability currently uses the user_functions mode.
RealVector	initialPoint
	holds initial point passed in for "user_functions" mode.
RealVector	lowerBounds
	holds variable lower bounds passed in for "user_functions" mode.
RealVector	upperBounds
	holds variable upper bounds passed in for "user_functions" mode.
Static Private Attributes
static SNLLOptimizer *	snllOptInstance
	functions in order to avoid the need for static data

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Detailed Description

Wrapper class for the OPT++ optimization library.

The SNLLOptimizer class provides a wrapper for OPT++, a C++ optimization library of nonlinear programming and pattern search techniques from the Computational Sciences and Mathematics Research (CSMR) department at Sandia's Livermore CA site. 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, a static member, or accessed by static pointer.

The user input mappings are as follows: max_iterations, max_function_evaluations, convergence_tolerance, max_step, gradient_tolerance, search_method, and search_scheme_size are set using OPT++'s setMaxIter(), setMaxFeval(), setFcnTol(), setMaxStep(), setGradTol(), setSearchStrategy(), and setSSS() member functions, respectively; output verbosity is used to toggle OPT++'s debug mode using the setDebug() member function. Internal to OPT++, there are 3 search strategies, while the DAKOTA search_method specification supports 4 (value_based_line_search, gradient_based_line_search, trust_region, or tr_pds). The difference stems from the "is_expensive" flag in OPT++. If the search strategy is LineSearch and "is_expensive" is turned on, then the value_based_line_search is used. Otherwise (the "is_expensive" default is off), the algorithm will use the gradient_based_line_search. Refer to [Meza, J.C., 1994] and to the OPT++ source in the Dakota/methods/OPTPP directory for information on OPT++ class member functions.

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Constructor & Destructor Documentation

SNLLOptimizer

(

Model &

model

)

standard constructor

This constructor is used for normal instantiations using data from the ProblemDescDB.

SNLLOptimizer	(	const String &	method_name,
		Model &	model
	)

alternate constructor for instantiations "on the fly"

This is an alternate constructor for instantiations on the fly using a Model but no ProblemDescDB.

SNLLOptimizer	(	const RealVector &	initial_point,
		const RealVector &	var_lower_bnds,
		const RealVector &	var_upper_bnds,
		const RealMatrix &	lin_ineq_coeffs,
		const RealVector &	lin_ineq_lower_bnds,
		const RealVector &	lin_ineq_upper_bnds,
		const RealMatrix &	lin_eq_coeffs,
		const RealVector &	lin_eq_targets,
		const RealVector &	nonlin_ineq_lower_bnds,
		const RealVector &	nonlin_ineq_upper_bnds,
		const RealVector &	nonlin_eq_targets,
		void(*)(int mode, int n, const NEWMAT::ColumnVector &x, NEWMAT::Real &f, NEWMAT::ColumnVector &grad_f, int &result_mode)	user_obj_eval,
		void(*)(int mode, int n, const NEWMAT::ColumnVector &x, NEWMAT::ColumnVector &g, NEWMAT::Matrix &grad_g, int &result_mode)	user_con_eval
	)

alternate constructor for instantiations "on the fly"

This is an alternate constructor for performing an optimization using the passed in objective function and constraint function pointers.

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Member Function Documentation

void nlf0_evaluator	(	int	n,
		const NEWMAT::ColumnVector &	x,
		NEWMAT::Real &	f,
		int &	result_mode
	)			[static, private]

require only function values.

For use when DAKOTA computes f and gradients are not directly available. This is used by nongradient-based optimizers such as PDS and by gradient-based optimizers in vendor numerical gradient mode (opt++'s internal finite difference routine is used).

void nlf1_evaluator	(	int	mode,
		int	n,
		const NEWMAT::ColumnVector &	x,
		NEWMAT::Real &	f,
		NEWMAT::ColumnVector &	grad_f,
		int &	result_mode
	)			[static, private]

values and gradients to OPT++ methods.

For use when DAKOTA computes f and df/dX (regardless of gradientType). Vendor numerical gradient case is handled by nlf0_evaluator.

void nlf2_evaluator	(	int	mode,
		int	n,
		const NEWMAT::ColumnVector &	x,
		NEWMAT::Real &	f,
		NEWMAT::ColumnVector &	grad_f,
		NEWMAT::SymmetricMatrix &	hess_f,
		int &	result_mode
	)			[static, private]

values, gradients, and Hessians to OPT++ methods.

For use when DAKOTA receives f, df/dX, & d^2f/dx^2 from the ApplicationInterface (analytic only). Finite differencing does not make sense for a full Newton approach, since lack of analytic gradients & Hessian should dictate the use of quasi-newton or fd-newton. Thus, there is no fdnlf2_evaluator for use with full Newton approaches, since it is preferable to use quasi-newton or fd-newton with nlf1. Gauss-Newton does not fit this model; it uses nlf2_evaluator_gn instead of nlf2_evaluator.

void constraint0_evaluator	(	int	n,
		const NEWMAT::ColumnVector &	x,
		NEWMAT::ColumnVector &	g,
		int &	result_mode
	)			[static, private]

only constraint values.

For use when DAKOTA computes g and gradients are not directly available. This is used by nongradient-based optimizers and by gradient-based optimizers in vendor numerical gradient mode (opt++'s internal finite difference routine is used).

void constraint1_evaluator	(	int	mode,
		int	n,
		const NEWMAT::ColumnVector &	x,
		NEWMAT::ColumnVector &	g,
		NEWMAT::Matrix &	grad_g,
		int &	result_mode
	)			[static, private]

values and gradients to OPT++ methods.

For use when DAKOTA computes g and dg/dX (regardless of gradientType). Vendor numerical gradient case is handled by constraint0_evaluator.

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

• SNLLOptimizer.H
• SNLLOptimizer.C

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