NLP node implementation subclass for preprocessing and basis manipulation. More...

#include <NLPInterfacePack_NLPSerialPreprocess.hpp>

Inheritance diagram for NLPInterfacePack::NLPSerialPreprocess:

Classes
class	InconsistantBounds
	Thrown if xl(i) > xu(i) More...

struct	ObjGradInfoSerial
	Struct for serial gradient (objective), objective and constriants (pointers) More...

struct	ZeroOrderInfoSerial
	Struct for objective and constriants (pointer) as serial vectors. More...

Public Member Functions
	NLPSerialPreprocess ()
	Default Constructor. More...

Public Member Functions inherited from NLPInterfacePack::NLPObjGrad
	NLPObjGrad ()
	Initialize to no reference set to calculation quanities. More...

virtual bool	supports_Gf () const
	Determine if the objective gradient is supported or not. More...

virtual bool	supports_Gf_prod () const
	Determine if the objective gradient product is supported or not. More...

virtual void	set_Gf (VectorMutable *Gf)
	Set a pointer to a vector to be updated when `this->calc_Gf()` is called. More...

virtual VectorMutable *	get_Gf ()
	Return pointer passed to `this->set_Gf()`. More...

virtual VectorMutable &	Gf ()
	Returns non-`const` `*this->get_Gf()`. More...

virtual const Vector &	Gf () const
	Returns `const` `*this->get_Gf()`. More...

void	unset_quantities ()
	Call to unset all storage quantities (both in this class and all subclasses). More...

virtual void	calc_Gf (const Vector &x, bool newx=true) const
	Update the vector for `Gf` at the point `x` and put it in the stored reference. More...

virtual value_type	calc_Gf_prod (const Vector &x, const Vector &d, bool newx=true) const
	Calculate the inner product `Gf(x)'*d` at the point `x` and put it in the stored reference. More...

virtual size_type	num_Gf_evals () const
	Objective gradient evaluations count. More...

Public Member Functions inherited from NLPInterfacePack::NLP
const ZeroOrderInfo	zero_order_info () const
	Return pointer to set quantities. More...

const ZeroOrderInfo	zero_order_info_breve () const
	Return pointer to set `hat` quantities. More...

	NLP ()
	Initialize to no reference set to calculation quanities. More...

virtual	~NLP ()
	Destructor that cleans all the memory it owns. More...

virtual void	set_options (const options_ptr_t &options)
	Set the options that `this` NLP may be interested in. More...

virtual const options_ptr_t &	get_options () const
	Get the `OptionsFromStream` object being used to extract the options from. More...

virtual value_type	max_var_bounds_viol () const =0
	Set the maximum absolute value for which the variable bounds may be violated by when computing function and gradient values. More...

virtual void	set_f (value_type *f)
	Set a pointer to an value to be updated when `this->calc_f()` is called. More...

virtual value_type *	get_f ()
	Return pointer passed to `this->set_f()`. More...

virtual value_type &	f ()
	Returns non-`const` `*this->get_f()`. More...

virtual const value_type &	f () const
	Returns `const` `*this->get_f()`. More...

virtual void	set_c (VectorMutable *c)
	Set a pointer to a vector to be updated when `this->calc_c()` is called. More...

virtual VectorMutable *	get_c ()
	Return pointer passed to `this->set_c()`. More...

virtual VectorMutable &	c ()
	Returns non-`const` `*this->get_c()`. More...

virtual const Vector &	c () const
	Returns `const` `*this->get_c()`. More...

virtual void	calc_f (const Vector &x, bool newx=true) const
	Update the value for the objective `f` at the point `x` and put it in the stored reference. More...

virtual void	calc_c (const Vector &x, bool newx=true) const
	Update the constraint residual vector for `c` at the point `x` and put it in the stored reference. More...

virtual size_type	num_f_evals () const
	Gives the number of object function f(x) evaluations called by the solver since initialize() was called. More...

virtual size_type	num_c_evals () const
	Gives the number of constraint function c(x) evaluations called by the solver since initialize() was called. Throws exception if `this->m() == 0`. More...

virtual void	set_c_breve (VectorMutable *c_breve)
	Set a pointer to a vector to be updated when `this->calc_c_breve()` is called. More...

virtual VectorMutable *	get_c_breve ()
	Return pointer passed to `this->set_c_breve()`. More...

virtual VectorMutable &	c_breve ()
	Returns non-`const` `*this->get_c_breve()`. More...

virtual const Vector &	c_breve () const
	Returns `const` `*this->get_c_breve()`. More...

virtual void	set_h_breve (VectorMutable *h_breve)
	Set a pointer to a vector to be updated when `this->calc_h_breve()` is called. More...

virtual VectorMutable *	get_h_breve ()
	Return pointer passed to `this->set_h_breve()`. More...

virtual VectorMutable &	h_breve ()
	Returns non-`const` `*this->get_h_breve()`. More...

virtual const Vector &	h_breve () const
	Returns `const` `*this->get_h_breve()`. More...

virtual void	calc_c_breve (const Vector &x, bool newx=true) const
	Update the constraint residual vector for `c_breve` at the point `x` and put it in the stored reference. More...

virtual void	calc_h_breve (const Vector &x, bool newx=true) const
	Update the constraint residual vector for `h_breve` at the point `x` and put it in the stored reference. More...

Public Member Functions inherited from Teuchos::VerboseObject< NLP >
TEUCHOSPARAMETERLIST_LIB_DLL_EXPORT RCP< const ParameterList >	getValidVerboseObjectSublist ()

TEUCHOSPARAMETERLIST_LIB_DLL_EXPORT void	setupVerboseObjectSublist (ParameterList *paramList)

TEUCHOSPARAMETERLIST_LIB_DLL_EXPORT void	readVerboseObjectSublist (ParameterList paramList, RCP< FancyOStream > oStream, EVerbosityLevel *verbLevel)

void	readVerboseObjectSublist (ParameterList paramList, VerboseObject< NLP > verboseObject)

	VerboseObject (const EVerbosityLevel verbLevel=VERB_DEFAULT, const RCP< FancyOStream > &oStream=Teuchos::null)

virtual void	initializeVerboseObject (const EVerbosityLevel verbLevel=VERB_DEFAULT, const RCP< FancyOStream > &oStream=Teuchos::null)

virtual const VerboseObject &	setVerbLevel (const EVerbosityLevel verbLevel) const

virtual const VerboseObject &	setOverridingVerbLevel (const EVerbosityLevel verbLevel) const

virtual EVerbosityLevel	getVerbLevel () const

Public Member Functions inherited from NLPInterfacePack::NLPVarReductPerm

Static Public Member Functions
static value_type	fixed_var_mult ()
	Gives the value of a Lagrange multipler for a fixed variable bound .that has been preprocessed out of the problem. More...

Static Public Member Functions inherited from NLPInterfacePack::NLP
static value_type	infinite_bound ()
	Value for an infinite bound. More...

Static Public Member Functions inherited from Teuchos::VerboseObject< NLP >
static void	setDefaultVerbLevel (const EVerbosityLevel defaultVerbLevel)

static EVerbosityLevel	getDefaultVerbLevel ()

Private Member Functions
bool	get_next_basis_remove_fixed (IVector var_perm, IVector equ_perm, size_type *rank)

void	assert_and_set_basis (const IVector &var_perm, const IVector &equ_perm, size_type rank)

void	assert_bounds_on_variables () const

void	do_force_xinit_in_bounds ()

Private Attributes
value_type	f_orig_

DVector	c_orig_

DVector	h_orig_

DVector	Gf_full_

bool	initialized_

bool	force_xinit_in_bounds_

value_type	scale_f_

IVector	var_full_to_fixed_

IVector	inv_var_full_to_fixed_

IVector	var_perm_

IVector	equ_perm_

IVector	inv_equ_perm_

DVector	x_full_

DVector	xinit_full_

DVector	xl_full_

DVector	xu_full_

perm_fcty_ptr_t	factory_P_var_

perm_fcty_ptr_t	factory_P_equ_

VectorSpaceSerial	space_x_

VectorSpaceSerial	space_c_

VectorSpaceSerial	space_c_breve_

VectorSpaceSerial	space_h_breve_

size_type	num_bounded_x_

VectorMutableDense	xinit_

VectorMutableDense	xl_

VectorMutableDense	xu_

VectorMutableDense	hl_breve_

VectorMutableDense	hu_breve_

PermutationSerial	P_var_

PermutationSerial	P_equ_

size_type	n_orig_

size_type	m_orig_

size_type	mI_orig_

size_type	n_full_

size_type	m_full_

size_type	n_

size_type	r_

int	basis_selection_num_

Overridden public members from NLP
void	force_xinit_in_bounds (bool force_xinit_in_bounds)

bool	force_xinit_in_bounds () const

void	initialize (bool test_setup)

bool	is_initialized () const

size_type	n () const

size_type	m () const

vec_space_ptr_t	space_x () const

vec_space_ptr_t	space_c () const

size_type	num_bounded_x () const

const Vector &	xl () const

const Vector &	xu () const

const Vector &	xinit () const

void	get_init_lagrange_mult (VectorMutable lambda, VectorMutable nu) const

void	scale_f (value_type scale_f)

value_type	scale_f () const

void	report_final_solution (const Vector &x, const Vector lambda, const Vector nu, bool is_optimal)
	Overridden to permute the variables back into an order that is natural to the subclass. More...

virtual size_type	ns () const

vec_space_ptr_t	space_c_breve () const

vec_space_ptr_t	space_h_breve () const

const Vector &	hl_breve () const

const Vector &	hu_breve () const

const Permutation &	P_var () const

const Permutation &	P_equ () const

Overridden public members from NLPVarReductPerm
const perm_fcty_ptr_t	factory_P_var () const

const perm_fcty_ptr_t	factory_P_equ () const

Range1D	var_dep () const

Range1D	var_indep () const

Range1D	equ_decomp () const

Range1D	equ_undecomp () const

bool	nlp_selects_basis () const

bool	get_next_basis (Permutation P_var, Range1D var_dep, Permutation P_equ, Range1D equ_decomp)

void	set_basis (const Permutation &P_var, const Range1D &var_dep, const Permutation P_equ, const Range1D equ_decomp)

void	get_basis (Permutation P_var, Range1D var_dep, Permutation P_equ, Range1D equ_decomp) const

Overridden protected members from NLP
void	imp_calc_f (const Vector &x, bool newx, const ZeroOrderInfo &zero_order_info) const

void	imp_calc_c (const Vector &x, bool newx, const ZeroOrderInfo &zero_order_info) const

void	imp_calc_c_breve (const Vector &x, bool newx, const ZeroOrderInfo &zero_order_info_breve) const

void	imp_calc_h_breve (const Vector &x, bool newx, const ZeroOrderInfo &zero_order_info_breve) const

Overridden protected members from NLPObjGrad
void	imp_calc_Gf (const Vector &x, bool newx, const ObjGradInfo &obj_grad_info) const

Pure virtual methods to be defined by subclasses
virtual bool	imp_nlp_has_changed () const
	Return if the definition of the NLP has changed since the last call to `initialize()` More...

virtual size_type	imp_n_orig () const =0
	Return the number of variables in the original problem (including those fixed by bounds) More...

virtual size_type	imp_m_orig () const =0
	Return the number of general equality constraints in the original problem. More...

virtual size_type	imp_mI_orig () const =0
	Return the number of general inequality constraints in the original problem. More...

virtual const DVectorSlice	imp_xinit_orig () const =0
	Return the original initial point (size `imp_n_orig()`). More...

virtual bool	imp_has_var_bounds () const =0
	Return if the NLP has bounds. More...

virtual const DVectorSlice	imp_xl_orig () const =0
	Return the original lower variable bounds (size `imp_n_orig()`). More...

virtual const DVectorSlice	imp_xu_orig () const =0
	Return the original upper variable bounds (size `imp_n_orig()`). More...

virtual const DVectorSlice	imp_hl_orig () const =0
	Return the original lower general inequality bounds (size `imp_mI_orig()`). More...

virtual const DVectorSlice	imp_hu_orig () const =0
	Return the original upper general inequality bounds (size `imp_mI_orig()`). More...

virtual void	imp_calc_f_orig (const DVectorSlice &x_full, bool newx, const ZeroOrderInfoSerial &zero_order_info) const =0
	Calculate the objective function for the original NLP. More...

virtual void	imp_calc_c_orig (const DVectorSlice &x_full, bool newx, const ZeroOrderInfoSerial &zero_order_info) const =0
	Calculate the vector for all of the general equality constaints in the original NLP. More...

virtual void	imp_calc_h_orig (const DVectorSlice &x_full, bool newx, const ZeroOrderInfoSerial &zero_order_info) const =0
	Calculate the vector for all of the general inequality constaints in the original NLP. More...

virtual void	imp_calc_Gf_orig (const DVectorSlice &x_full, bool newx, const ObjGradInfoSerial &obj_grad_info) const =0
	Calculate the vector for the gradient of the objective in the original NLP. More...

virtual bool	imp_get_next_basis (IVector var_perm_full, IVector equ_perm_full, size_type rank_full, size_type rank)
	Return the next basis selection (default returns `false`). More...

virtual void	imp_report_orig_final_solution (const DVectorSlice &x_full, const DVectorSlice lambda_orig, const DVectorSlice lambdaI_orig, const DVectorSlice *nu_orig, bool optimal)
	To be overridden by subclasses to report the final solution in the original ordering natural to the subclass. More...

Other protected implementation functions for subclasses to call
void	set_not_initialized ()
	Used by subclasses to set the state of the NLP to not initialized. More...

void	assert_initialized () const
	Assert if we have been initizlized (throws UnInitialized) More...

void	set_x_full (const DVectorSlice &x, bool newx, DVectorSlice *x_full) const
	Set the full x vector if `newx == true` More...

DVectorSlice	x_full () const
	Give reference to current x_full. More...

const ZeroOrderInfoSerial	zero_order_orig_info () const

const ObjGradInfoSerial	obj_grad_orig_info () const

const IVector &	var_remove_fixed_to_full () const
	Permutation vector for partitioning free and fixed variables. More...

const IVector &	var_full_to_remove_fixed () const
	Inverse permutation vector of `var_remove_fixed_to_full()`. More...

const IVector &	var_perm () const
	Permutes from the compated variable vector (removing fixed variables) to the current basis selection. More...

const IVector &	equ_perm () const
	Permutes from the original constriant ordering to the current basis selection. More...

const IVector &	inv_equ_perm () const
	Inverse of `equ_perm()` More...

void	var_from_full (DVectorSlice::const_iterator vec_full, DVectorSlice::iterator vec) const

void	var_to_full (DVectorSlice::const_iterator vec, DVectorSlice::iterator vec_full) const

void	equ_from_full (const DVectorSlice &c_orig, const DVectorSlice &h_orig, const DVectorSlice &s_orig, DVectorSlice *c_full) const

Additional Inherited Members
Public Types inherited from NLPInterfacePack::NLP
typedef AbstractLinAlgPack::Vector	Vector

typedef AbstractLinAlgPack::VectorMutable	VectorMutable

typedef Teuchos::RCP< const VectorSpace >	vec_space_ptr_t

typedef Teuchos::RCP< const OptionsFromStreamPack::OptionsFromStream >	options_ptr_t

Public Types inherited from NLPInterfacePack::NLPVarReductPerm
typedef Teuchos::RCP< const Teuchos::AbstractFactory < Permutation > >	perm_fcty_ptr_t

Protected Member Functions inherited from NLPInterfacePack::NLPObjGrad
const ObjGradInfo	obj_grad_info () const
	Return objective gradient and zero order information. More...

Protected Member Functions inherited from NLPInterfacePack::NLP
template<class T >
void	assert_ref_set (T *p, std::string info) const
	Assert referece has been set for a quanity. More...

Detailed Description

NLP node implementation subclass for preprocessing and basis manipulation.

This is an implementation node class that takes an original NLP and transforms it by:

Converting general inequalities to equalities with slack variables
Removing variables fixed by bounds
Converting general inequalities with cramped bounds into general equalities
Reordering the quantities according to the current basis selection (by implementing the NLPVarReductPerm interface).

Original NLP formulation

The original NLP (as specified by the subclass) takes the form:

    min    f_orig(x_orig)
    s.t.   c_orig(x_orig) = 0
           hl_orig <= h(x_orig) <= hu_orig
           xl_orig <= x_orig <= xu_orig
 where:
         x_orig         <: REAL^n_orig
         f_orig(x_orig) <: REAL^n_orig -> REAL
         c_orig(x_orig) <: REAL^n_orig -> REAL^m_orig
         h_orig(x_orig) <: REAL^n_orig -> REAL^mI_orig

Conversion of general inequalities to equalities using slack variables

The original NLP formulation above is transformed by adding slack variables s_orig <: REAL^mI_orig, defining a new x_full = [ x_orig; s_orig ] and forming the new NLP:

    min    f_full(x_full)
    s.t.   c_full(x_full) = 0
           xl_full <= x_full <= xu_full

 where:

         x_full           = [ x_orig ]  n_orig
                            [ s_orig ]  mI_orig

         f_full(x_full)   = f_orig(x_orig)

         c_full(x_full)   = [ c_orig(x_orig)          ]  m_orig
                            [ h_orig(x_orig) - s_orig ]  mI_orig

         xl_full          = [ xl_orig ]  n_orig
                            [ hl_orig ]  mI_orig

         xu_full          = [ xu_orig ]  n_orig
                            [ hu_orig ]  mI_orig

Note that in this case, the Jacobian of the new equality constraints and the gradient of the new objective become:

   Gc_full = [  Gc_orig    Gh_orig   ]  n_orig
             [     0          -I     ]  mI_orig

                 m_orig     mI_orig

   Gf_full = [ Gf_orig ]  n_orig
             [    0    ]  mI_orig

It is up to the subclass to implement imp_calc_Gc() and imp_calc_Gh() in a way that is consistent with the above transformation while also considering basis permutations (see NLPSerialPreprocessExplJac). As for the gradient Gc_full, the subclass can actually include terms for the slack variables in the objective function but the most common behavior will be to just ignore slack variables in the subclass.

Preprocessing and basis manipulation

The initial basis selection is the original order (x_full = [ x_orig; s_orig ]) with the variables fixed by bounds being removed, and assumes there are no dependent equations (r == m).

The implementations of the Jacobian matrices Gc and Gh are not determined here and must be defined by an NLP subclass (see NLPSerialPreprocessExplJac for example).

This class stores the variable permutations and processing information in two parts. In the first state, the fixed variables are removed as:

   var_remove_fixed_to_full =  [ not fixed by bounds |  fixed by bounds  ]
                               [1 ..                n|n+1 ..        n_full]

The mapping i_full = var_remove_fixed_to_full()(i_free_fixed) gives the index of the original variable (i_full) for the sets of variables not fixed and fixed by bounds.

The inverse mapping i_free_fixed = var_full_to_remove_fixed()(i_full) can be used to determine if a variable is fixed by bounds or not..

On top of this partitioning of free and fixed variables, there is a second stage which is a permutation of the free variables into dependent and independent sets that is needed by the client.

   var_perm = [ dependent variables | independent variables ]
              [1..               n-r|n-r+1...              n]

The mapping i_free_fixed = var_perm()(i_perm) is used to determine the index of a free variable in var_remove_fixed_to_full() given its index (i_perm) for the current basis selection.

For example, if x is the vector of variables for the current basis selection and x_full is the vector of variables in the original order including the fixed variables then the following is true:

x(i) == x_full(var_remove_fixed_to_full()(var_perm()(i))), for i = 1...n

The permutation equ_perm() gives the partitioning of the equality constraints into decomposed and undecomposed equalities. Decomposed inequality constraints are not supported currently.

Subclass developers notes

Handling of multiple updates by subclasses: Here we discuss the protocol for the handling of multiple updates to quantities during the calculation of other quantities. In order to simplify the implementation of subclasses as much as possible, storage for all iteration quantities will be passed to the subclass in the methods imp_calc_f_orig(), imp_calc_c_orig(), imp_calc_h_orig() and imp_calc_Gf_orig() regardless of what quantities where set by the user in the NLP interface. The subclass can always find out what was set by the client by calling get_f(), get_c(), get_Gf() etc. Therefore, in general, clients should just only compute what is required in each call to imp_calc_xxx_orig() and only update other quantities if it is absolutely free to do so (e.g. computing a function value when a gradient is computed using AD) or is required to do so (e.g.an external interface that forces both f_orig(x_orig), c_orig(x_orig) and h_orig(x_orig) be computed at the same time). It is up to the subclass to remember when a quantity has already been computed so that it will not be computed again unnecessarily. It is always safe for the subclass to ignore these issues and just do what is easiest. More careful implementations can be handled by the subclass by keeping track of get_xxx() and newx and remembering when quantities are computed.

The following methods from the NLP interface must be overridden by the NLP subclass: max_var_bounds_viol(), set_multi_calc(), multi_calc().

The following methods from the NLPVarReductPerm interface must be overridden by the NLP subclass: nlp_selects_basis().

In addition, the methods from this interface that must be overridden are: imp_n_orig(), imp_m_orig(), imp_mI_orig(), imp_xinit_orig(), imp_has_var_bounds(), imp_xl_orig(), imp_xu_orig(), imp_hl_orig(), imp_hu_orig(), imp_calc_f_orig(), imp_calc_c_orig(), imp_calc_h_orig() and imp_calc_Gf_orig().

The NLP method initialize() should also be overridden by all of the subclasses (and call initialize() on its direct subclass).

The following methods (with default implementations) may also be overridden by a subclass: imp_get_next_basis() and imp_report_orig_final_solution().

Definition at line 221 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

Constructor & Destructor Documentation

NLPInterfacePack::NLPSerialPreprocess::NLPSerialPreprocess ( )

Default Constructor.

This initalizes the basis to the first basis if the subclass specifies one and if not picks to first r variables as the dependent variables and the last n-r variables as the independent variables. Also the default behavior is to force the initial point in bounds.

Definition at line 85 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

Member Function Documentation

value_type NLPInterfacePack::NLPSerialPreprocess::fixed_var_mult ( )

static

Gives the value of a Lagrange multipler for a fixed variable bound .that has been preprocessed out of the problem.

Definition at line 78 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

void NLPInterfacePack::NLPSerialPreprocess::force_xinit_in_bounds ( bool force_xinit_in_bounds )

virtual

Implements NLPInterfacePack::NLP.

Definition at line 96 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

bool NLPInterfacePack::NLPSerialPreprocess::force_xinit_in_bounds ( ) const

virtual

Implements NLPInterfacePack::NLP.

Definition at line 101 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

void NLPInterfacePack::NLPSerialPreprocess::initialize ( bool test_setup )

virtual

Reimplemented from NLPInterfacePack::NLPObjGrad.

Reimplemented in NLPInterfacePack::ExampleNLPBanded, NLPInterfacePack::NLPSerialPreprocessExplJac, and NLPInterfacePack::NLPWBCounterExample.

Definition at line 106 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

bool NLPInterfacePack::NLPSerialPreprocess::is_initialized ( ) const

virtual

Implements NLPInterfacePack::NLP.

Reimplemented in NLPInterfacePack::ExampleNLPBanded, NLPInterfacePack::NLPSerialPreprocessExplJac, and NLPInterfacePack::NLPWBCounterExample.

Definition at line 333 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

size_type NLPInterfacePack::NLPSerialPreprocess::n ( ) const

virtual

Reimplemented from NLPInterfacePack::NLP.

Definition at line 338 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

size_type NLPInterfacePack::NLPSerialPreprocess::m ( ) const

virtual

Reimplemented from NLPInterfacePack::NLP.

Definition at line 344 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

NLP::vec_space_ptr_t NLPInterfacePack::NLPSerialPreprocess::space_x ( ) const

virtual

Implements NLPInterfacePack::NLP.

Definition at line 350 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

NLP::vec_space_ptr_t NLPInterfacePack::NLPSerialPreprocess::space_c ( ) const

virtual

Implements NLPInterfacePack::NLP.

Definition at line 356 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

size_type NLPInterfacePack::NLPSerialPreprocess::num_bounded_x ( ) const

virtual

Implements NLPInterfacePack::NLP.

Definition at line 362 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

const Vector & NLPInterfacePack::NLPSerialPreprocess::xl ( ) const

virtual

Implements NLPInterfacePack::NLP.

Definition at line 367 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

const Vector & NLPInterfacePack::NLPSerialPreprocess::xu ( ) const

virtual

Implements NLPInterfacePack::NLP.

Definition at line 373 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

const Vector & NLPInterfacePack::NLPSerialPreprocess::xinit ( ) const

virtual

Implements NLPInterfacePack::NLP.

Definition at line 379 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

void NLPInterfacePack::NLPSerialPreprocess::get_init_lagrange_mult	(	VectorMutable *	lambda,
		VectorMutable *	nu
	)		const

virtual

Reimplemented from NLPInterfacePack::NLP.

Definition at line 385 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

void NLPInterfacePack::NLPSerialPreprocess::scale_f ( value_type scale_f )

virtual

Implements NLPInterfacePack::NLP.

Definition at line 398 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

value_type NLPInterfacePack::NLPSerialPreprocess::scale_f ( ) const

virtual

Implements NLPInterfacePack::NLP.

Definition at line 404 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

void NLPInterfacePack::NLPSerialPreprocess::report_final_solution	(	const Vector &	x,
		const Vector *	lambda,
		const Vector *	nu,
		bool	is_optimal
	)

virtual

Overridden to permute the variables back into an order that is natural to the subclass.

The default implementation of this function is to call the method imp_report_full_final_solution(x_full,lambda_full,nu_full). This function translates from x, lambda and nu into the original order with fixed variables added back to form x_full, lambda_full, lambdaI_full and nu_full.

Reimplemented from NLPInterfacePack::NLP.

Definition at line 410 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

size_type NLPInterfacePack::NLPSerialPreprocess::ns ( ) const

virtual

Reimplemented from NLPInterfacePack::NLP.

Definition at line 456 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

NLP::vec_space_ptr_t NLPInterfacePack::NLPSerialPreprocess::space_c_breve ( ) const

virtual

Reimplemented from NLPInterfacePack::NLP.

Definition at line 463 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

NLP::vec_space_ptr_t NLPInterfacePack::NLPSerialPreprocess::space_h_breve ( ) const

virtual

Reimplemented from NLPInterfacePack::NLP.

Definition at line 470 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

const Vector & NLPInterfacePack::NLPSerialPreprocess::hl_breve ( ) const

virtual

Reimplemented from NLPInterfacePack::NLP.

Definition at line 477 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

const Vector & NLPInterfacePack::NLPSerialPreprocess::hu_breve ( ) const

virtual

Reimplemented from NLPInterfacePack::NLP.

Definition at line 483 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

const Permutation & NLPInterfacePack::NLPSerialPreprocess::P_var ( ) const

virtual

Reimplemented from NLPInterfacePack::NLP.

Definition at line 489 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

const Permutation & NLPInterfacePack::NLPSerialPreprocess::P_equ ( ) const

virtual

Reimplemented from NLPInterfacePack::NLP.

Definition at line 495 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

const NLPVarReductPerm::perm_fcty_ptr_t NLPInterfacePack::NLPSerialPreprocess::factory_P_var ( ) const

virtual

Implements NLPInterfacePack::NLPVarReductPerm.

Definition at line 504 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

const NLPVarReductPerm::perm_fcty_ptr_t NLPInterfacePack::NLPSerialPreprocess::factory_P_equ ( ) const

virtual

Implements NLPInterfacePack::NLPVarReductPerm.

Definition at line 510 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

Range1D NLPInterfacePack::NLPSerialPreprocess::var_dep ( ) const

virtual

Implements NLPInterfacePack::NLPVarReductPerm.

Definition at line 515 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

Range1D NLPInterfacePack::NLPSerialPreprocess::var_indep ( ) const

virtual

Implements NLPInterfacePack::NLPVarReductPerm.

Definition at line 521 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

Range1D NLPInterfacePack::NLPSerialPreprocess::equ_decomp ( ) const

virtual

Implements NLPInterfacePack::NLPVarReductPerm.

Definition at line 527 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

Range1D NLPInterfacePack::NLPSerialPreprocess::equ_undecomp ( ) const

virtual

Implements NLPInterfacePack::NLPVarReductPerm.

Definition at line 533 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

bool NLPInterfacePack::NLPSerialPreprocess::nlp_selects_basis ( ) const

virtual

Implements NLPInterfacePack::NLPVarReductPerm.

Reimplemented in NLPInterfacePack::ExampleNLPBanded, and NLPInterfacePack::NLPWBCounterExample.

Definition at line 539 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

bool NLPInterfacePack::NLPSerialPreprocess::get_next_basis	(	Permutation *	P_var,
		Range1D *	var_dep,
		Permutation *	P_equ,
		Range1D *	equ_decomp
	)

virtual

Implements NLPInterfacePack::NLPVarReductPerm.

Reimplemented in NLPInterfacePack::NLPSerialPreprocessExplJac.

Definition at line 557 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

void NLPInterfacePack::NLPSerialPreprocess::set_basis	(	const Permutation &	P_var,
		const Range1D &	var_dep,
		const Permutation *	P_equ,
		const Range1D *	equ_decomp
	)

virtual

Implements NLPInterfacePack::NLPVarReductPerm.

Reimplemented in NLPInterfacePack::NLPSerialPreprocessExplJac.

Definition at line 574 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

void NLPInterfacePack::NLPSerialPreprocess::get_basis	(	Permutation *	P_var,
		Range1D *	var_dep,
		Permutation *	P_equ,
		Range1D *	equ_decomp
	)		const

virtual

Implements NLPInterfacePack::NLPVarReductPerm.

Definition at line 607 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

void NLPInterfacePack::NLPSerialPreprocess::imp_calc_f	(	const Vector &	x,
		bool	newx,
		const ZeroOrderInfo &	zero_order_info
	)		const

protectedvirtual

Implements NLPInterfacePack::NLP.

Definition at line 650 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

void NLPInterfacePack::NLPSerialPreprocess::imp_calc_c	(	const Vector &	x,
		bool	newx,
		const ZeroOrderInfo &	zero_order_info
	)		const

protectedvirtual

Implements NLPInterfacePack::NLP.

Definition at line 663 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

void NLPInterfacePack::NLPSerialPreprocess::imp_calc_c_breve	(	const Vector &	x,
		bool	newx,
		const ZeroOrderInfo &	zero_order_info_breve
	)		const

protectedvirtual

Reimplemented from NLPInterfacePack::NLP.

Definition at line 685 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

void NLPInterfacePack::NLPSerialPreprocess::imp_calc_h_breve	(	const Vector &	x,
		bool	newx,
		const ZeroOrderInfo &	zero_order_info_breve
	)		const

protectedvirtual

Reimplemented from NLPInterfacePack::NLP.

Definition at line 702 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

void NLPInterfacePack::NLPSerialPreprocess::imp_calc_Gf	(	const Vector &	x,
		bool	newx,
		const ObjGradInfo &	obj_grad_info
	)		const

protectedvirtual

Implements NLPInterfacePack::NLPObjGrad.

Definition at line 720 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

virtual bool NLPInterfacePack::NLPSerialPreprocess::imp_nlp_has_changed ( ) const

inlineprotectedvirtual

Return if the definition of the NLP has changed since the last call to initialize()

The default return is true. This function is present in order to avoid preprocessing when initialize() is called but nothing has changed.

Reimplemented in NLPInterfacePack::ExampleNLPBanded, and NLPInterfacePack::NLPWBCounterExample.

Definition at line 460 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

virtual size_type NLPInterfacePack::NLPSerialPreprocess::imp_n_orig ( ) const

protectedpure virtual

Return the number of variables in the original problem (including those fixed by bounds)

Implemented in NLPInterfacePack::ExampleNLPBanded, and NLPInterfacePack::NLPWBCounterExample.

virtual size_type NLPInterfacePack::NLPSerialPreprocess::imp_m_orig ( ) const

protectedpure virtual

Return the number of general equality constraints in the original problem.

Implemented in NLPInterfacePack::ExampleNLPBanded, and NLPInterfacePack::NLPWBCounterExample.

virtual size_type NLPInterfacePack::NLPSerialPreprocess::imp_mI_orig ( ) const

protectedpure virtual

Return the number of general inequality constraints in the original problem.

Implemented in NLPInterfacePack::ExampleNLPBanded, and NLPInterfacePack::NLPWBCounterExample.

virtual const DVectorSlice NLPInterfacePack::NLPSerialPreprocess::imp_xinit_orig ( ) const

protectedpure virtual

Return the original initial point (size imp_n_orig()).

Implemented in NLPInterfacePack::ExampleNLPBanded, and NLPInterfacePack::NLPWBCounterExample.

virtual bool NLPInterfacePack::NLPSerialPreprocess::imp_has_var_bounds ( ) const

protectedpure virtual

Return if the NLP has bounds.

Implemented in NLPInterfacePack::ExampleNLPBanded, and NLPInterfacePack::NLPWBCounterExample.

virtual const DVectorSlice NLPInterfacePack::NLPSerialPreprocess::imp_xl_orig ( ) const

protectedpure virtual

Return the original lower variable bounds (size imp_n_orig()).

Only to be called if this->imp_has_var_bounds() == true. A lower bound is considered free if it is less than or equal to:

<tt>-NLP::infinite_bound()</tt>

Implemented in NLPInterfacePack::ExampleNLPBanded, and NLPInterfacePack::NLPWBCounterExample.

virtual const DVectorSlice NLPInterfacePack::NLPSerialPreprocess::imp_xu_orig ( ) const

protectedpure virtual

Return the original upper variable bounds (size imp_n_orig()).

Only to be called if this->imp_has_var_bounds() == true. An upper bound is considered free if it is greater than or equal to:

<tt>+NLP::infinite_bound()</tt>

Implemented in NLPInterfacePack::ExampleNLPBanded, and NLPInterfacePack::NLPWBCounterExample.

virtual const DVectorSlice NLPInterfacePack::NLPSerialPreprocess::imp_hl_orig ( ) const

protectedpure virtual

Return the original lower general inequality bounds (size imp_mI_orig()).

Only to be called if this->imp_mI_orig() == true. A lower bound is considered free if it is equal to:

-NLP::infinite_bound()

Implemented in NLPInterfacePack::ExampleNLPBanded, and NLPInterfacePack::NLPWBCounterExample.

virtual const DVectorSlice NLPInterfacePack::NLPSerialPreprocess::imp_hu_orig ( ) const

protectedpure virtual

Return the original upper general inequality bounds (size imp_mI_orig()).

Only to be called if this->imp_mI_orig() == true. An upper bound is considered free if it is equal to:

+NLP::infinite_bound()

Implemented in NLPInterfacePack::ExampleNLPBanded, and NLPInterfacePack::NLPWBCounterExample.

virtual void NLPInterfacePack::NLPSerialPreprocess::imp_calc_f_orig	(	const DVectorSlice &	x_full,
		bool	newx,
		const ZeroOrderInfoSerial &	zero_order_info
	)		const

protectedpure virtual

Calculate the objective function for the original NLP.

Implemented in NLPInterfacePack::ExampleNLPBanded, and NLPInterfacePack::NLPWBCounterExample.

virtual void NLPInterfacePack::NLPSerialPreprocess::imp_calc_c_orig	(	const DVectorSlice &	x_full,
		bool	newx,
		const ZeroOrderInfoSerial &	zero_order_info
	)		const

protectedpure virtual

Calculate the vector for all of the general equality constaints in the original NLP.

Implemented in NLPInterfacePack::ExampleNLPBanded, and NLPInterfacePack::NLPWBCounterExample.

virtual void NLPInterfacePack::NLPSerialPreprocess::imp_calc_h_orig	(	const DVectorSlice &	x_full,
		bool	newx,
		const ZeroOrderInfoSerial &	zero_order_info
	)		const

protectedpure virtual

Calculate the vector for all of the general inequality constaints in the original NLP.

Implemented in NLPInterfacePack::ExampleNLPBanded, and NLPInterfacePack::NLPWBCounterExample.

virtual void NLPInterfacePack::NLPSerialPreprocess::imp_calc_Gf_orig	(	const DVectorSlice &	x_full,
		bool	newx,
		const ObjGradInfoSerial &	obj_grad_info
	)		const

protectedpure virtual

Calculate the vector for the gradient of the objective in the original NLP.

Note that the dimension of obj_grad_info.Gf->dim() is n_orig + mI_orig.

On input, if mI_orig > 0 then (*obj_grad_info.Gf)(n_orig+1,n_orig+mI_orig) is initialized to 0.0 (since slacks do not ordinarily do not appear in the objective function). However, the subclass can assign (smooth) contributions for the slacks if desired.

Implemented in NLPInterfacePack::ExampleNLPBanded, and NLPInterfacePack::NLPWBCounterExample.

bool NLPInterfacePack::NLPSerialPreprocess::imp_get_next_basis	(	IVector *	var_perm_full,
		IVector *	equ_perm_full,
		size_type *	rank_full,
		size_type *	rank
	)

protectedvirtual

Return the next basis selection (default returns false).

Parameters

var_perm_full	out. Contains the variable permutations (including slack variables possibly).
equ_perm_full	[out] (size = `m_orig + mI_orig)`). Contains the constriant permutations (including general inequalities possibly).
rank_full	[out] Returns the rank of the basis before fixed variables are taken out of `var_perm_full` and `equ_perm`.
rank	[out] Returns the rank of the basis after fixed variables are taken out of `var_perm_full` and `equ_perm`. If there are no fixed variables then `rank` should be equal to `rank_full`.

Postconditions:

[return == true] var_perm_full(i) < var_perm_full(i+1), for i = 1...rank_full-1
[return == true] var_perm_full(i) < var_perm_full(i+1), for i = rank_full...n_full-1
[return == true] equ_perm_full(i) < equ_perm_full(i+1), for i = 1...rank-1
[return == true] equ_perm_full(i) < equ_perm_full(i+1), for i = rank...m_full-1

This method will only be called if this->nlp_selects_basis() == true.

The basis returned by the subclass must be sorted var_perm_full = [ dep | indep ] and equ_perm_full = [ equ_decomp | equ_undecomp ]. The subclass should not remove the variables fixed by bounds from var_perm_full as they will be removed by this class as they are translated. In addition, the subclass can also include slack variables in the basis (if mI_orig > 0>/tt>). Therefore, a nonsingular basis before fixed variables are removed may not be nonsingular once the fixed variables are removed. During the translation of var_perm_perm, the variables fixed by bounds are removed by compacting var_perm_full and adjusting the remaining indices. For this to be correct with variables fixed by bounds, it is assumed that the subclass knows which variables are fixed by bounds and can construct var_perm_full so that after the translated the basis will be nonsingular. The first rank entries in var_perm_full[1:rank_full] left after the fixed variables have been removed give the indices of the dependent (basic) variables and the remaining variables in var_perm_full[rank_full+1,n_full] are the indices for the independent (nonbasic) variables. To simplify things, it would be wise for the NLP subclass not to put fixed variables in the basis since this will greatly simplify selecting a nonsingular basis.

The first time this method is called, the subclass should return the first suggested basis selection (even if it happens to be identical to the original ordering).

The default implementation returns false which implies that the NLP subclass has no idea what a good basis selection should be..

Reimplemented in NLPInterfacePack::ExampleNLPBanded, and NLPInterfacePack::NLPWBCounterExample.

Definition at line 739 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

virtual void NLPInterfacePack::NLPSerialPreprocess::imp_report_orig_final_solution	(	const DVectorSlice &	x_full,
		const DVectorSlice *	lambda_orig,
		const DVectorSlice *	lambdaI_orig,
		const DVectorSlice *	nu_orig,
		bool	optimal
	)

inlineprotectedvirtual

To be overridden by subclasses to report the final solution in the original ordering natural to the subclass.

Note that the lagrange multipliers for fixed variables that have been preprocessed out of the problem are not computed by the optimization algorithm and are therefore not available. These multipliers are designated with the special value fixed_var_mult() but the numerical value is not significant.

The default implementation of this function is to do nothing.

Reimplemented in NLPInterfacePack::ExampleNLPBanded, and NLPInterfacePack::NLPWBCounterExample.

Definition at line 623 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

void NLPInterfacePack::NLPSerialPreprocess::set_not_initialized ( )

inlineprotected

Used by subclasses to set the state of the NLP to not initialized.

Definition at line 845 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

void NLPInterfacePack::NLPSerialPreprocess::assert_initialized ( ) const

protected

Assert if we have been initizlized (throws UnInitialized)

Definition at line 749 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

void NLPInterfacePack::NLPSerialPreprocess::set_x_full	(	const DVectorSlice &	x,
		bool	newx,
		DVectorSlice *	x_full
	)		const

protected

Set the full x vector if newx == true

Definition at line 756 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

DVectorSlice NLPInterfacePack::NLPSerialPreprocess::x_full ( ) const

inlineprotected

Give reference to current x_full.

Definition at line 851 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

const NLPSerialPreprocess::ZeroOrderInfoSerial NLPInterfacePack::NLPSerialPreprocess::zero_order_orig_info ( ) const

inlineprotected

Definition at line 858 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

const NLPSerialPreprocess::ObjGradInfoSerial NLPInterfacePack::NLPSerialPreprocess::obj_grad_orig_info ( ) const

inlineprotected

Definition at line 865 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

const IVector & NLPInterfacePack::NLPSerialPreprocess::var_remove_fixed_to_full ( ) const

inlineprotected

Permutation vector for partitioning free and fixed variables.

var_remove_fixed_to_full =  [ not fixed by bounds |  fixed by bounds  ]
                            [1 ..                n|n + 1 ..     n_full]

The mapping i_full = var_remove_fixed_to_full()(i_free_fixed) gives the index of the original variable (i_full) for the sets of variables not fixed and fixed (upper and lower bounds where equal).

Definition at line 871 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

const IVector & NLPInterfacePack::NLPSerialPreprocess::var_full_to_remove_fixed ( ) const

inlineprotected

Inverse permutation vector of var_remove_fixed_to_full().

The inverse mapping i_free_fixed = var_full_to_remove_fixed()(i_full) can be used to determine if a variable is free for fixed.

Definition at line 877 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

const IVector & NLPInterfacePack::NLPSerialPreprocess::var_perm ( ) const

inlineprotected

Permutes from the compated variable vector (removing fixed variables) to the current basis selection.

On top of this partitioning of free and fixed variables, there is a permutation of the free variables into dependent and independent variables that is needed by the optimization algorithm.

var_perm = [ dependent variables | independent variables ]
           [1..                 r|r+1..                 n]

The mapping i_free_fixed = var_perm()(i_perm) is used to determine the index of a free variable in var_remove_fixed_to_full() given its index (i_perm) being used by the client.

Definition at line 883 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

const IVector & NLPInterfacePack::NLPSerialPreprocess::equ_perm ( ) const

inlineprotected

Permutes from the original constriant ordering to the current basis selection.

equ_perm = [ decomposed equalities | undecomposed equalities ]
           [1..                   r|n-r+1...                n]

The mapping j_full = equ_perm()(j_perm) is used to determine the index of the constriant in c_full given its index i_perm being used by the NLP client.

Definition at line 889 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

const IVector & NLPInterfacePack::NLPSerialPreprocess::inv_equ_perm ( ) const

inlineprotected

Inverse of equ_perm()

The mapping j_perm = inv_equ_perm()(j_full) is used to determine the index j_perm of the constriant c being used by the client given the index in c_full.

Definition at line 895 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

void NLPInterfacePack::NLPSerialPreprocess::var_from_full	(	DVectorSlice::const_iterator	vec_full,
		DVectorSlice::iterator	vec
	)		const

protected

Definition at line 766 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

void NLPInterfacePack::NLPSerialPreprocess::var_to_full	(	DVectorSlice::const_iterator	vec,
		DVectorSlice::iterator	vec_full
	)		const

protected

Definition at line 783 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

void NLPInterfacePack::NLPSerialPreprocess::equ_from_full	(	const DVectorSlice &	c_orig,
		const DVectorSlice &	h_orig,
		const DVectorSlice &	s_orig,
		DVectorSlice *	c_full
	)		const

protected

Definition at line 789 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

bool NLPInterfacePack::NLPSerialPreprocess::get_next_basis_remove_fixed	(	IVector *	var_perm,
		IVector *	equ_perm,
		size_type *	rank
	)

private

Definition at line 806 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

void NLPInterfacePack::NLPSerialPreprocess::assert_and_set_basis	(	const IVector &	var_perm,
		const IVector &	equ_perm,
		size_type	rank
	)

private

Definition at line 979 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

void NLPInterfacePack::NLPSerialPreprocess::assert_bounds_on_variables ( ) const

private

Definition at line 1022 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

void NLPInterfacePack::NLPSerialPreprocess::do_force_xinit_in_bounds ( )

private

Definition at line 1029 of file NLPInterfacePack_NLPSerialPreprocess.cpp.

Member Data Documentation

value_type NLPInterfacePack::NLPSerialPreprocess::f_orig_

mutableprivate

Definition at line 735 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

DVector NLPInterfacePack::NLPSerialPreprocess::c_orig_

mutableprivate

Definition at line 736 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

DVector NLPInterfacePack::NLPSerialPreprocess::h_orig_

mutableprivate

Definition at line 737 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

DVector NLPInterfacePack::NLPSerialPreprocess::Gf_full_

mutableprivate

Definition at line 738 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

bool NLPInterfacePack::NLPSerialPreprocess::initialized_

private

Definition at line 740 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

bool NLPInterfacePack::NLPSerialPreprocess::force_xinit_in_bounds_

private

Definition at line 743 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

value_type NLPInterfacePack::NLPSerialPreprocess::scale_f_

private

Definition at line 746 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

IVector NLPInterfacePack::NLPSerialPreprocess::var_full_to_fixed_

private

Definition at line 749 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

IVector NLPInterfacePack::NLPSerialPreprocess::inv_var_full_to_fixed_

private

Definition at line 758 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

IVector NLPInterfacePack::NLPSerialPreprocess::var_perm_

private

Definition at line 763 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

IVector NLPInterfacePack::NLPSerialPreprocess::equ_perm_

private

Definition at line 771 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

IVector NLPInterfacePack::NLPSerialPreprocess::inv_equ_perm_

private

Definition at line 778 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

DVector NLPInterfacePack::NLPSerialPreprocess::x_full_

mutableprivate

Definition at line 782 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

DVector NLPInterfacePack::NLPSerialPreprocess::xinit_full_

private

Definition at line 783 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

DVector NLPInterfacePack::NLPSerialPreprocess::xl_full_

private

Definition at line 784 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

DVector NLPInterfacePack::NLPSerialPreprocess::xu_full_

private

Definition at line 785 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

perm_fcty_ptr_t NLPInterfacePack::NLPSerialPreprocess::factory_P_var_

private

Definition at line 792 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

perm_fcty_ptr_t NLPInterfacePack::NLPSerialPreprocess::factory_P_equ_

private

Definition at line 793 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

VectorSpaceSerial NLPInterfacePack::NLPSerialPreprocess::space_x_

private

Definition at line 794 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

VectorSpaceSerial NLPInterfacePack::NLPSerialPreprocess::space_c_

private

Definition at line 795 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

VectorSpaceSerial NLPInterfacePack::NLPSerialPreprocess::space_c_breve_

private

Definition at line 796 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

VectorSpaceSerial NLPInterfacePack::NLPSerialPreprocess::space_h_breve_

private

Definition at line 797 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

size_type NLPInterfacePack::NLPSerialPreprocess::num_bounded_x_

private

Definition at line 798 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

VectorMutableDense NLPInterfacePack::NLPSerialPreprocess::xinit_

private

Definition at line 799 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

VectorMutableDense NLPInterfacePack::NLPSerialPreprocess::xl_

private

Definition at line 800 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

VectorMutableDense NLPInterfacePack::NLPSerialPreprocess::xu_

private

Definition at line 801 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

VectorMutableDense NLPInterfacePack::NLPSerialPreprocess::hl_breve_

private

Definition at line 802 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

VectorMutableDense NLPInterfacePack::NLPSerialPreprocess::hu_breve_

private

Definition at line 803 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

PermutationSerial NLPInterfacePack::NLPSerialPreprocess::P_var_

private

Definition at line 804 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

PermutationSerial NLPInterfacePack::NLPSerialPreprocess::P_equ_

private

Definition at line 805 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

size_type NLPInterfacePack::NLPSerialPreprocess::n_orig_

private

Definition at line 806 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

size_type NLPInterfacePack::NLPSerialPreprocess::m_orig_

private

Definition at line 807 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

size_type NLPInterfacePack::NLPSerialPreprocess::mI_orig_

private

Definition at line 808 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

size_type NLPInterfacePack::NLPSerialPreprocess::n_full_

private

Definition at line 809 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

size_type NLPInterfacePack::NLPSerialPreprocess::m_full_

private

Definition at line 810 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

size_type NLPInterfacePack::NLPSerialPreprocess::n_

private

Definition at line 811 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

size_type NLPInterfacePack::NLPSerialPreprocess::r_

private

Definition at line 812 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

int NLPInterfacePack::NLPSerialPreprocess::basis_selection_num_

private

Definition at line 814 of file NLPInterfacePack_NLPSerialPreprocess.hpp.

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

Classes

Public Member Functions

Static Public Member Functions

Private Member Functions

Private Attributes

Overridden public members from NLP

Overridden public members from NLPVarReductPerm

Overridden protected members from NLP

Overridden protected members from NLPObjGrad

Pure virtual methods to be defined by subclasses

Other protected implementation functions for subclasses to call

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation