Collaboration diagram for hpp::core::pathOptimization::QuadraticProgram:

Public Types
typedef Eigen::JacobiSVD< matrix_t >	Decomposition_t
typedef Eigen::LLT< matrix_t, Eigen::Lower >	LLT_t

Public Member Functions
	QuadraticProgram (size_type inputSize)
	QuadraticProgram (const QuadraticProgram &QP, const LinearConstraint &lc)
	QuadraticProgram (const QuadraticProgram &QP)
	~QuadraticProgram ()
void	accuracy (value_type acc)
value_type	accuracy () const
void	addRows (const std::size_t &nbRows)
Program subject to linear equality constraints.
void	reduced (const LinearConstraint &lc, QuadraticProgram &QPr) const
void	decompose ()
void	solve ()
Program subject to linear equality and inequality constraints.
void	computeLLT ()
double	solve (const LinearConstraint &ce, const LinearConstraint &ci)

Public Attributes
value_type	accuracy_
Model
matrix_t	H
vector_t	b
bool	bIsZero
Data (for inequality constraints)
LLT_t	llt
value_type	trace
Eigen::VectorXi	activeConstraint
int	activeSetSize
Data (for equality constraints)
Decomposition_t	dec
vector_t	xStar

Detailed Description

Quadratic program

This class stores a quadratic cost defined by \( \frac{1}{2} x^T H x + b^T x \) where \( (H, b) \) are the parameters.

It can then solve the two following program:

Program subject to linear equality constraints
\begin{eqnarray*}\min & \frac{1}{2} x^T H x + b^T x \\ A_0 x = b_0 \end{eqnarray*}
This is done via reduced, decompose and solve methods
Program subject to linear equality and inequality constraints:
\begin{eqnarray*}\min & \frac{1}{2} x^T H x + b^T x \\ A_0 x = b_0 \\ A_1 x \ge b_1 \end{eqnarray*}
This is done via computeLLT and solve methods and uses quadprog

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

Public Types