A linear constraint \( J \times x = b \). More...

#include <hpp/core/path-optimization/linear-constraint.hh>

Public Member Functions
	LinearConstraint (size_type inputSize, size_type outputSize)

	~LinearConstraint ()

void	concatenate (const LinearConstraint &oc)

bool	decompose (bool check=false, bool throwIfNotValid=false)
	Compute one solution and a base of the kernel of matrix J. More...

void	computeRank ()
	Compute rank of the constraint using a LU decomposition. More...

bool	reduceConstraint (const LinearConstraint &lc, LinearConstraint &lcr, bool computeRank=true) const
	Reduced constraint into the set of solutions of this constraint. More...

void	computeSolution (const vector_t &v)
	Compute the unique solution derived from v into xSol. More...

bool	isSatisfied (const vector_t &x, const value_type &threshold=Eigen::NumTraits< value_type >::dummy_precision())
	Returns \( ( J \times x - b ).isZero (threshold) \). More...

void	addRows (const std::size_t &nbRows)

Public Attributes
Model
matrix_t	J

vector_t	b

Data
Solutions are \( \left\{ x^* + PK \times v, v \in \mathbb{R}^{nCols(J) - rank} \right\} \)
size_type	rank
	Rank of J. More...

matrix_t	PK
	Projector onto \( kernel(J) \). More...

vector_t	xStar
	\( x^* \) is a particular solution. More...

vector_t	xSol

Detailed Description

A linear constraint \( J \times x = b \).

Constructor & Destructor Documentation

◆ LinearConstraint()

hpp::core::pathOptimization::LinearConstraint::LinearConstraint	(	size_type	inputSize,
		size_type	outputSize
	)

inline

References b, J, and ~LinearConstraint().

◆ ~LinearConstraint()

hpp::core::pathOptimization::LinearConstraint::~LinearConstraint ( )

Referenced by LinearConstraint().

Member Function Documentation

◆ addRows()

void hpp::core::pathOptimization::LinearConstraint::addRows ( const std::size_t & nbRows )

inline

References b, and J.

◆ computeRank()

void hpp::core::pathOptimization::LinearConstraint::computeRank ( )

inline

Compute rank of the constraint using a LU decomposition.

References J, and rank.

Referenced by reduceConstraint().

◆ computeSolution()

void hpp::core::pathOptimization::LinearConstraint::computeSolution ( const vector_t & v )

inline

Compute the unique solution derived from v into xSol.

\( xSol \gets x^* + PK \times v \)

Parameters

v	an element of the kernel of matrix J.

Return values

this->xSol

References isSatisfied(), PK, xSol, and xStar.

◆ concatenate()

void hpp::core::pathOptimization::LinearConstraint::concatenate ( const LinearConstraint & oc )

inline

References b, and J.

◆ decompose()

bool hpp::core::pathOptimization::LinearConstraint::decompose	(	bool	check = `false`,
		bool	throwIfNotValid = `false`
	)

Compute one solution and a base of the kernel of matrix J.

rank is also updated.

Parameters

check If true, checks whether the constraint is feasible.

Returns: whether the constraint is feasible (alwys true when check is false)

◆ isSatisfied()

bool hpp::core::pathOptimization::LinearConstraint::isSatisfied	(	const vector_t &	x,
		const value_type &	threshold = `Eigen::NumTraits<value_type>::dummy_precision()`
	)

inline

Returns \( ( J \times x - b ).isZero (threshold) \).

References b, and J.

Referenced by computeSolution().

◆ reduceConstraint()

bool hpp::core::pathOptimization::LinearConstraint::reduceConstraint	(	const LinearConstraint &	lc,
		LinearConstraint &	lcr,
		bool	computeRank = `true`
	)		const