Declare the AMPLNonlinearFunction class for setting up evaluation and derivatives of nonlinear Functions. More...

#include <AMPLNonlinearFunction.h>

Inheritance diagram for MINOTAUR_AMPL::AMPLNonlinearFunction:

Collaboration diagram for MINOTAUR_AMPL::AMPLNonlinearFunction:

Public Member Functions
	AMPLNonlinearFunction ()
	Default constructor.

	AMPLNonlinearFunction (Minotaur::UInt i, Minotaur::UInt nvars, ASL *my_asl, bool is_in_obj)
	Create a nonlinear function for a given constraint.

Minotaur::NonlinearFunctionPtr	cloneWithVars (Minotaur::VariableConstIterator vbeg, int *err) const
	Make a clone using new variables.

double	eval (const double x, int error)
	Evaluate the function at a given point x.

void	evalGradient (const double x, double grad_f, int *error)
	Evaluate and add gradient at a given point.

void	evalHessian (const double mult, const double x, const Minotaur::LTHessStor stor, double values, int error)
	Evaluate and add hessian at a given point.

void	fillHessStor (Minotaur::LTHessStor *)
	Fill sparsity of hessian into hessian storage.

void	finalHessStor (const Minotaur::LTHessStor *)
	Finalize hessian preparation.

void	fillJac (const double , double , int *)
	Evaluate and add gradient at a given point to the jacobian.

void	getVars (Minotaur::VariableSet *)
	Get variables used in this function.

void	multiply (double c)
	Multiply by a constant.

void	prepJac (Minotaur::VarSetConstIter, Minotaur::VarSetConstIter)
	Prepare for evaluating sparse jacobian.

void	setVars (Minotaur::VarSetConstIterator vb, Minotaur::VarSetConstIterator ve)
	Tell what variables are in this function.

void	write (std::ostream &out) const
	Display the nonlinear function.

Public Member Functions inherited from Minotaur::NonlinearFunction
	NonlinearFunction ()
	Default constructor.

virtual	~NonlinearFunction ()
	Destroy.

virtual void	addConst (const double d, int &err)
	Add a constant to the function.

virtual void	sqrRoot (int &err)
	Change the nonlinear function to its square-root.

virtual void	computeBounds (double lb, double ub, int *error)
	Calculate upper and lower bounds on the function using bounds of the variables.

virtual std::string	getNlString (int *err)
	Return a string in AMPL's .nl format (postfix notation) of this nonlinear function.

virtual double	getFixVarOffset (VariablePtr, double)
	If a variable is fixed at a given value and removed, what is the constant (offset) needed to be added.

virtual FunctionType	getType () const
	Return the type of function: polynomial, ...

virtual bool	hasVar (ConstVariablePtr v) const
	Check if function contains a variable.

virtual bool	isSumOfSquares () const
	Check if the function is a sum of square terms.

virtual size_t	numVars ()
	Return the number of variables in this function.

virtual void	removeVar (VariablePtr, double)
	Remove a variable v from the function after fixing it to value val.

virtual void	subst (VariablePtr, VariablePtr, double)
	Substitute a variable with another.

virtual NonlinearFunctionPtr	getPersp (VariablePtr z, double eps, int *err) const
	Take perspective of this function with respect to a given variable.

virtual void	varBoundMods (double, double, VarBoundModVector &, SolveStatus *)
	Tighten variables based on function bounds.

virtual VariableSet::iterator	varsBegin ()
	First iterator for variables.

virtual VariableSet::iterator	varsEnd ()
	Last iterator for variables.

Additional Inherited Members
Protected Attributes inherited from Minotaur::NonlinearFunction
VariableSet	vars_
	A set of variables used in this function.

Detailed Description

Declare the AMPLNonlinearFunction class for setting up evaluation and derivatives of nonlinear Functions.

This class does not contain the computational graph of the function. Use it only for evaluating function values and derivatives.

Constructor & Destructor Documentation

◆ AMPLNonlinearFunction()

AMPLNonlinearFunction::AMPLNonlinearFunction	(	Minotaur::UInt	i,
		Minotaur::UInt	nvars,
		ASL *	my_asl,
		bool	is_in_obj
	)

Create a nonlinear function for a given constraint.

Parameters

[in]	i	The constraint number of which we need the nonlinear function. This value is ignored if is_obj is True.
[in]	nvars	Total number of variables in the problem.
[in]	my_asl	Pointer to ASL for calling its routines.
[in]	is_obj	If True, then get function from objective. Otherwise, get function of i-th constriant.

Member Function Documentation

◆ cloneWithVars()

Minotaur::NonlinearFunctionPtr AMPLNonlinearFunction::cloneWithVars	(	Minotaur::VariableConstIterator	vbeg,
		int *	err
	)		const

virtual

Make a clone using new variables.

Parameters

[in]	vbeg	it points to the variable id 0. vbeg+k points to variable id k, where k>=0.
[out]	err	must be nonzero if function wasn't cloned.

Implements Minotaur::NonlinearFunction.

◆ eval()

double AMPLNonlinearFunction::eval	(	const double *	x,
		int *	error
	)

virtual

Evaluate the function at a given point x.

Parameters

[in]	x	The size of the array x must exceed the highest index of the variables used in the function.
[out]	*error	It should be set a positive value if there is error encountered while evaluating. Leave undisturbed otherwise.

Returns: The value of function of x.

Implements Minotaur::NonlinearFunction.

◆ evalGradient()

void AMPLNonlinearFunction::evalGradient	(	const double *	x,
		double *	grad_f,
		int *	error
	)

virtual

Evaluate and add gradient at a given point.

Parameters

[in]	x	The size of the array x must exceed the highest index of the variables used in the function.
[out]	grad_f	The values of grad_f are incremented with the gradient of this function at x. The array grad_f is dense.
[out]	error	It should be set a positive value if there is error encountered while evaluating. Leave undisturbed otherwise.

Implements Minotaur::NonlinearFunction.

◆ evalHessian()

void AMPLNonlinearFunction::evalHessian	(	const double	mult,
		const double *	x,
		const Minotaur::LTHessStor *	stor,
		double *	values,
		int *	error
	)

virtual

Evaluate and add hessian at a given point.

Parameters

[in]	mult	Multiplier for this objective/constraint function
[in]	x	The point where we need the hessian.
[in]	stor	The Hessian storage information.
[out]	values	The Hessian values to which we add Hessian of this function.
[out]	error	We set it to nonzero if any errors are encountered. Do not change it otherwise.

Implements Minotaur::NonlinearFunction.

◆ fillHessStor()

void AMPLNonlinearFunction::fillHessStor ( Minotaur::LTHessStor * stor )

virtual

Fill sparsity of hessian into hessian storage.

Parameters

[in,out] stor We add variables into stor->cols

Implements Minotaur::NonlinearFunction.

◆ fillJac()

void AMPLNonlinearFunction::fillJac	(	const double *	x,
		double *	values,
		int *	error
	)

virtual

Evaluate and add gradient at a given point to the jacobian.

Parameters

[in]	x	The size of the array x must exceed the highest index of the variables used in the function.
[out]	values	The values of jacobian are incremented with the gradient of this function at x. 'values' only contains nonzeros of jacobian. The indices (or offsets) where this nonlinear function should put in the values should be calculated in the prepJac function.
[out]	error	It should be set a zero value if there is error encountered while evaluating. Leave undisturbed otherwise.

Implements Minotaur::NonlinearFunction.

◆ finalHessStor()

void AMPLNonlinearFunction::finalHessStor ( const Minotaur::LTHessStor * stor )

virtual

Finalize hessian preparation.

Parameters

[in] stor contains the sparsity pattern of hessian of lagrangian. The nonlinear function should save offsets or make other preparation to evaluate hessian.

Implements Minotaur::NonlinearFunction.

◆ getVars()

void AMPLNonlinearFunction::getVars ( Minotaur::VariableSet * vars )

virtual

Get variables used in this function.

Parameters

[in] vars A set of variable-pointers into which variables are to be inserted.

Implements Minotaur::NonlinearFunction.

◆ multiply()

void AMPLNonlinearFunction::multiply ( double c )

virtual

Multiply by a constant.

Parameters

[in] c double value with which we want to multiply.

Implements Minotaur::NonlinearFunction.

◆ prepJac()

void AMPLNonlinearFunction::prepJac	(	Minotaur::VarSetConstIter	vbeg,
		Minotaur::VarSetConstIter	vend
	)

virtual

Prepare for evaluating sparse jacobian.

All the variables that are in this function can occur in one or more of linear, quadratic and nonlinear functions. All variables that occur in the whole function can be accessed by iterating between vbeg and vend. This function is used to find the offset for variables that occur in the nonlinear function.

Implements Minotaur::NonlinearFunction.

◆ setVars()

void AMPLNonlinearFunction::setVars	(	Minotaur::VarSetConstIterator	vb,
		Minotaur::VarSetConstIterator	ve
	)

Tell what variables are in this function.

These variabvles are then stored in this class for future use by Minotaur routines.

Parameters

[in]	vb	Iterator pointing to first element of set.
[in]	ve	Iterator pointing to end of set.

◆ write()

void AMPLNonlinearFunction::write ( std::ostream & out ) const

virtual

Display the nonlinear function.

Reimplemented from Minotaur::NonlinearFunction.

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

/home/amahajan/tmp/minotaur-test/src/interfaces/AMPLNonlinearFunction.h
/home/amahajan/tmp/minotaur-test/src/interfaces/AMPLNonlinearFunction.cpp

Public Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ AMPLNonlinearFunction()

Member Function Documentation

◆ cloneWithVars()

◆ eval()

◆ evalGradient()

◆ evalHessian()

◆ fillHessStor()

◆ fillJac()

◆ finalHessStor()

◆ getVars()

◆ multiply()

◆ prepJac()

◆ setVars()

◆ write()