Detailed Description

Provides an abstract interface to represent an expression, mapping a fixed or dynamic number of inputs to a fixed number of outputs, that may be evaluated on a scalar type of double or AutoDiffXd.

These objects, and its derivatives, are meant to be bound to a given set of variables using the Binding<> class.

#include <drake/solvers/evaluator_base.h>

Public Member Functions
virtual	~EvaluatorBase ()

void	Eval (const Eigen::Ref< const Eigen::VectorXd > &x, Eigen::VectorXd *y) const
	Evaluates the expression. More...

void	Eval (const Eigen::Ref< const AutoDiffVecXd > &x, AutoDiffVecXd *y) const
	Evaluates the expression. More...

void	Eval (const Eigen::Ref< const VectorX< symbolic::Variable >> &x, VectorX< symbolic::Expression > *y) const
	Evaluates the expression. More...

void	set_description (const std::string &description)
	Set a human-friendly description for the evaluator. More...

const std::string &	get_description () const
	Getter for a human-friendly description for the evaluator. More...

std::ostream &	Display (std::ostream &os, const VectorX< symbolic::Variable > &vars) const
	Formats this evaluator into the given stream using `vars` for the bound decision variable names. More...

std::ostream &	Display (std::ostream &os) const
	Formats this evaluator into the given stream, without displaying the decision variables it is bound to. More...

std::string	ToLatex (const VectorX< symbolic::Variable > &vars, int precision=3) const
	Returns a LaTeX string describing this evaluator. More...

int	num_vars () const
	Getter for the number of variables, namely the number of rows in x, as used in Eval(x, y). More...

int	num_outputs () const
	Getter for the number of outputs, namely the number of rows in y, as used in Eval(x, y). More...

void	SetGradientSparsityPattern (const std::vector< std::pair< int, int >> &gradient_sparsity_pattern)
	Set the sparsity pattern of the gradient matrix ∂y/∂x (the gradient of y value in Eval, w.r.t x in Eval) . More...

const std::optional< std::vector< std::pair< int, int > > > &	gradient_sparsity_pattern () const
	Returns the vector of (row_index, col_index) that contains all the entries in the gradient of Eval function (∂y/∂x) whose value could be non-zero, namely if ∂yᵢ/∂xⱼ could be non-zero, then the pair (i, j) is in gradient_sparsity_pattern. More...

Does not allow copy, move, or assignment
	EvaluatorBase (const EvaluatorBase &)=delete

EvaluatorBase &	operator= (const EvaluatorBase &)=delete

	EvaluatorBase (EvaluatorBase &&)=delete

EvaluatorBase &	operator= (EvaluatorBase &&)=delete

Protected Member Functions
	EvaluatorBase (int num_outputs, int num_vars, const std::string &description="")
	Constructs a evaluator. More...

virtual void	DoEval (const Eigen::Ref< const Eigen::VectorXd > &x, Eigen::VectorXd *y) const =0
	Implements expression evaluation for scalar type double. More...

virtual void	DoEval (const Eigen::Ref< const AutoDiffVecXd > &x, AutoDiffVecXd *y) const =0
	Implements expression evaluation for scalar type AutoDiffXd. More...

virtual void	DoEval (const Eigen::Ref< const VectorX< symbolic::Variable >> &x, VectorX< symbolic::Expression > *y) const =0
	Implements expression evaluation for scalar type symbolic::Expression. More...

virtual std::ostream &	DoDisplay (std::ostream &os, const VectorX< symbolic::Variable > &vars) const
	NVI implementation of Display. More...

virtual std::string	DoToLatex (const VectorX< symbolic::Variable > &vars, int precision) const

void	set_num_outputs (int num_outputs)

Constructor & Destructor Documentation

◆ EvaluatorBase() [1/3]

EvaluatorBase ( const EvaluatorBase & )

delete

◆ EvaluatorBase() [2/3]

EvaluatorBase ( EvaluatorBase && )

delete

◆ ~EvaluatorBase()

virtual ~EvaluatorBase ( )

virtual

◆ EvaluatorBase() [3/3]

EvaluatorBase	(	int	num_outputs,
		int	num_vars,
		const std::string &	description = `""`
	)

protected

Constructs a evaluator.

Parameters

num_outputs.	The number of rows in the output.
num_vars.	The number of rows in the input. If the input dimension is not known, then set `num_vars` to Eigen::Dynamic.
description	A human-friendly description.

See also: Eval(...)

Member Function Documentation

◆ Display() [1/2]

std::ostream& Display	(	std::ostream &	os,
		const VectorX< symbolic::Variable > &	vars
	)		const

Formats this evaluator into the given stream using vars for the bound decision variable names.

The size of vars must match the num_vars() declared by this evaluator. (If num_vars() is Eigen::Dynamic, then vars may be any size.)

◆ Display() [2/2]

std::ostream& Display ( std::ostream & os ) const

Formats this evaluator into the given stream, without displaying the decision variables it is bound to.

◆ DoDisplay()

virtual std::ostream& DoDisplay	(	std::ostream &	os,
		const VectorX< symbolic::Variable > &	vars
	)		const

protectedvirtual

NVI implementation of Display.

The default implementation will report the NiceTypeName, get_description, and list the bound variables. Subclasses may override to customize the message.

Precondition: vars size is consistent with num_vars".

Reimplemented in ExpressionConstraint, LinearConstraint, ExpressionCost, PerspectiveQuadraticCost, LInfNormCost, L2NormCost, L1NormCost, and LinearCost.

◆ DoEval() [1/3]

virtual void DoEval	(	const Eigen::Ref< const Eigen::VectorXd > &	x,
		Eigen::VectorXd *	y
	)		const

protectedpure virtual

Implements expression evaluation for scalar type double.

Parameters

x	Input vector.
y	Output vector.

Precondition: x must be of size num_vars x 1.

Postcondition: y will be of size num_outputs x 1.

Implemented in ExponentialConeConstraint, ExpressionConstraint, LinearMatrixInequalityConstraint, PositiveSemidefiniteConstraint, LinearComplementarityConstraint, LinearConstraint, ExpressionCost, EvaluatorCost< EvaluatorType >, EvaluatorCost< PolynomialEvaluator >, PerspectiveQuadraticCost, LInfNormCost, L2NormCost, L1NormCost, DirectCollocationConstraint, and LinearCost.

◆ DoEval() [2/3]

virtual void DoEval	(	const Eigen::Ref< const AutoDiffVecXd > &	x,
		AutoDiffVecXd *	y
	)		const

protectedpure virtual

Implements expression evaluation for scalar type AutoDiffXd.

Parameters

x	Input vector.
y	Output vector.

Precondition: x must be of size num_vars x 1.

Postcondition: y will be of size num_outputs x 1.

Implemented in ExponentialConeConstraint, ExpressionConstraint, LinearMatrixInequalityConstraint, PositiveSemidefiniteConstraint, LinearComplementarityConstraint, LinearConstraint, ExpressionCost, EvaluatorCost< EvaluatorType >, EvaluatorCost< PolynomialEvaluator >, PerspectiveQuadraticCost, LInfNormCost, L2NormCost, L1NormCost, DirectCollocationConstraint, and LinearCost.

◆ DoEval() [3/3]

virtual void DoEval	(	const Eigen::Ref< const VectorX< symbolic::Variable >> &	x,
		VectorX< symbolic::Expression > *	y
	)		const

protectedpure virtual

Implements expression evaluation for scalar type symbolic::Expression.

Parameters

[in]	x	Input vector.
[out]	y	Output vector.

Precondition: x must be of size num_vars x 1.

Postcondition: y will be of size num_outputs x 1.

Implemented in ExponentialConeConstraint, ExpressionConstraint, LinearMatrixInequalityConstraint, PositiveSemidefiniteConstraint, LinearComplementarityConstraint, LinearConstraint, ExpressionCost, EvaluatorCost< EvaluatorType >, EvaluatorCost< PolynomialEvaluator >, PerspectiveQuadraticCost, LInfNormCost, L2NormCost, L1NormCost, DirectCollocationConstraint, and LinearCost.

◆ DoToLatex()

virtual std::string DoToLatex	(	const VectorX< symbolic::Variable > &	vars,
		int	precision
	)		const

protectedvirtual

Reimplemented in ExponentialConeConstraint, ExpressionConstraint, LinearMatrixInequalityConstraint, PositiveSemidefiniteConstraint, LinearComplementarityConstraint, LinearConstraint, ExpressionCost, PerspectiveQuadraticCost, LInfNormCost, L2NormCost, L1NormCost, and LinearCost.

◆ Eval() [1/3]

void Eval	(	const Eigen::Ref< const Eigen::VectorXd > &	x,
		Eigen::VectorXd *	y
	)		const

Evaluates the expression.

Parameters

[in]	x	A `num_vars` x 1 input vector.
[out]	y	A `num_outputs` x 1 output vector.

◆ Eval() [2/3]

void Eval	(	const Eigen::Ref< const AutoDiffVecXd > &	x,
		AutoDiffVecXd *	y
	)		const

Evaluates the expression.

Parameters

[in]	x	A `num_vars` x 1 input vector.
[out]	y	A `num_outputs` x 1 output vector.

◆ Eval() [3/3]

void Eval	(	const Eigen::Ref< const VectorX< symbolic::Variable >> &	x,
		VectorX< symbolic::Expression > *	y
	)		const

Evaluates the expression.

Parameters

[in]	x	A `num_vars` x 1 input vector.
[out]	y	A `num_outputs` x 1 output vector.

◆ get_description()

const std::string& get_description ( ) const

Getter for a human-friendly description for the evaluator.

◆ gradient_sparsity_pattern()

const std::optional<std::vector<std::pair<int, int> > >& gradient_sparsity_pattern ( ) const

Returns the vector of (row_index, col_index) that contains all the entries in the gradient of Eval function (∂y/∂x) whose value could be non-zero, namely if ∂yᵢ/∂xⱼ could be non-zero, then the pair (i, j) is in gradient_sparsity_pattern.

Return values

gradient_sparsity_pattern If nullopt, then we regard all entries of the gradient as potentially non-zero.

◆ num_outputs()

int num_outputs ( ) const

Getter for the number of outputs, namely the number of rows in y, as used in Eval(x, y).

◆ num_vars()

int num_vars ( ) const

Getter for the number of variables, namely the number of rows in x, as used in Eval(x, y).

◆ operator=() [1/2]

EvaluatorBase& operator= ( EvaluatorBase && )

delete

◆ operator=() [2/2]

EvaluatorBase& operator= ( const EvaluatorBase & )

delete

◆ set_description()

void set_description ( const std::string & description )

Set a human-friendly description for the evaluator.

◆ set_num_outputs()

void set_num_outputs ( int num_outputs )

protected

◆ SetGradientSparsityPattern()

void SetGradientSparsityPattern ( const std::vector< std::pair< int, int >> & gradient_sparsity_pattern )

Set the sparsity pattern of the gradient matrix ∂y/∂x (the gradient of y value in Eval, w.r.t x in Eval) .

gradient_sparsity_pattern contains all the pairs of (row_index, col_index) for which the corresponding entries could have non-zero value in the gradient matrix ∂y/∂x.

◆ ToLatex()

std::string ToLatex	(	const VectorX< symbolic::Variable > &	vars,
		int	precision = `3`
	)		const

Returns a LaTeX string describing this evaluator.

Does not include any characters to enter/exit math mode; you might want, e.g. "$$" + evaluator.ToLatex() + "$$".

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

drake/solvers/evaluator_base.h

Detailed Description

Public Member Functions

Protected Member Functions

Constructor & Destructor Documentation

◆ EvaluatorBase() [1/3]

◆ EvaluatorBase() [2/3]

◆ ~EvaluatorBase()

◆ EvaluatorBase() [3/3]

Member Function Documentation

◆ Display() [1/2]

◆ Display() [2/2]

◆ DoDisplay()

◆ DoEval() [1/3]

◆ DoEval() [2/3]

◆ DoEval() [3/3]

◆ DoToLatex()

◆ Eval() [1/3]

◆ Eval() [2/3]

◆ Eval() [3/3]

◆ get_description()

◆ gradient_sparsity_pattern()

◆ num_outputs()

◆ num_vars()

◆ operator=() [1/2]

◆ operator=() [2/2]

◆ set_description()

◆ set_num_outputs()

◆ SetGradientSparsityPattern()

◆ ToLatex()