Public Types
using	DenseVector = Eigen::Vector< Scalar, Eigen::Dynamic >
	Type alias for dense vector.

using	SparseVector = Eigen::SparseVector< Scalar >
	Type alias for sparse vector.

Public Member Functions
constexpr	Filter (Scalar initial_constraint_violation=Scalar(0))

void	reset ()
	Resets the filter.

bool	try_add (const FilterEntry< Scalar > &current_entry, const FilterEntry< Scalar > &trial_entry, const DenseVector &p_x, const SparseVector &g, Scalar α)

bool	last_rejection_due_to_filter () const

Public Attributes
Scalar	min_constraint_violation
	The minimum constraint violation.

Scalar	max_constraint_violation
	The maximum constraint violation.

Detailed Description

template<typename Scalar>
class slp::Filter< Scalar >

Step filter.

See the section on filters in chapter 15 of [1].

Template Parameters

Scalar Scalar type.

Constructor & Destructor Documentation

template<typename Scalar >

constexpr slp::Filter< Scalar >::Filter ( Scalar initial_constraint_violation = Scalar(0) )

inlineexplicitconstexpr

Constructs an empty filter.

Parameters

initial_constraint_violation The optimization problem's initial constraint violation.

template<typename Scalar >

bool slp::Filter< Scalar >::last_rejection_due_to_filter ( ) const

inline

Returns true if the most recent trial entry rejection was due to the filter.

Returns: True if the most recent trial entry rejection was due to the filter.

template<typename Scalar >

bool slp::Filter< Scalar >::try_add	(	const FilterEntry< Scalar > &	current_entry,
		const FilterEntry< Scalar > &	trial_entry,
		const DenseVector &	p_x,
		const SparseVector &	g,
		Scalar	α
	)

inline

Returns true if the given trial entry is acceptable to the filter.

Parameters

current_entry	The entry corresponding to the current iterate.
trial_entry	The entry corresponding to the trial iterate.
p_x	Decision variable primal step.
g	Cost function gradient.
α	The step size (0, 1].

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

/home/runner/work/Sleipnir/Sleipnir/include/sleipnir/optimization/solver/util/filter.hpp