igl::mosek Namespace Reference

Classes
struct	MosekData
	Structure for holding MOSEK data for solving a quadratic program. More...

Functions
template<typename DerivedV, typename DerivedEle, typename Derivedb, typename Derivedbc, typename DerivedW>
bool	bbw (const Eigen::MatrixBase< DerivedV > &V, const Eigen::MatrixBase< DerivedEle > &Ele, const Eigen::MatrixBase< Derivedb > &b, const Eigen::MatrixBase< Derivedbc > &bc, igl::BBWData &data, igl::mosek::MosekData &mosek_data, Eigen::PlainObjectBase< DerivedW > &W)
	Compute Bounded Biharmonic Weights on a given domain (V,Ele) with a given set of boundary conditions.
MSKrescodee	mosek_guarded (const MSKrescodee r)
	Little function to wrap around mosek call to handle errors.
bool	mosek_linprog (const Eigen::VectorXd &c, const Eigen::SparseMatrix< double > &A, const Eigen::VectorXd &lc, const Eigen::VectorXd &uc, const Eigen::VectorXd &lx, const Eigen::VectorXd &ux, Eigen::VectorXd &x)
	Solve a linear program using mosek.
bool	mosek_linprog (const Eigen::VectorXd &c, const Eigen::SparseMatrix< double > &A, const Eigen::VectorXd &lc, const Eigen::VectorXd &uc, const Eigen::VectorXd &lx, const Eigen::VectorXd &ux, const MSKenv_t &env, Eigen::VectorXd &x)
	This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
template<typename Index, typename Scalar>
bool	mosek_quadprog (const Index n, std::vector< Index > &Qi, std::vector< Index > &Qj, std::vector< Scalar > &Qv, const std::vector< Scalar > &c, const Scalar cf, const Index m, std::vector< Scalar > &Av, std::vector< Index > &Ari, const std::vector< Index > &Acp, const std::vector< Scalar > &lc, const std::vector< Scalar > &uc, const std::vector< Scalar > &lx, const std::vector< Scalar > &ux, MosekData &mosek_data, std::vector< Scalar > &x)
bool	mosek_quadprog (const Eigen::SparseMatrix< double > &Q, const Eigen::VectorXd &c, const double cf, const Eigen::SparseMatrix< double > &A, const Eigen::VectorXd &lc, const Eigen::VectorXd &uc, const Eigen::VectorXd &lx, const Eigen::VectorXd &ux, MosekData &mosek_data, Eigen::VectorXd &x)
	This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Function Documentation

bbw()

template<typename DerivedV, typename DerivedEle, typename Derivedb, typename Derivedbc, typename DerivedW>

bool igl::mosek::bbw	(	const Eigen::MatrixBase< DerivedV > &	V,
		const Eigen::MatrixBase< DerivedEle > &	Ele,
		const Eigen::MatrixBase< Derivedb > &	b,
		const Eigen::MatrixBase< Derivedbc > &	bc,
		igl::BBWData &	data,
		igl::mosek::MosekData &	mosek_data,
		Eigen::PlainObjectBase< DerivedW > &	W )

Compute Bounded Biharmonic Weights on a given domain (V,Ele) with a given set of boundary conditions.

Template Parameters

DerivedV	derived type of eigen matrix for V (e.g. Eigen::MatrixXd)
DerivedF	derived type of eigen matrix for F (e.g. Eigen::MatrixXi)
Derivedb	derived type of eigen matrix for b (e.g. Eigen::VectorXi)
Derivedbc	derived type of eigen matrix for bc (e.g. Eigen::MatrixXd)
DerivedW	derived type of eigen matrix for W (e.g. Eigen::MatrixXd)

Parameters

[in]	V	#V by dim vertex positions
[in]	Ele	#Elements by simplex-size list of element indices
[in]	b	#b boundary indices into V
[in]	bc	#b by #W list of boundary values
[in]	data	object containing options, initial guess --> solution and results
[in]	mosek_data	object containing mosek options
[out]	W	#V by #W list of unnormalized weights to normalize use igl::normalize_row_sums(W,W);

Returns

true on success, false on failure

mosek_guarded()

MSKrescodee igl::mosek::mosek_guarded

(

const MSKrescodee

r

)

Little function to wrap around mosek call to handle errors.

Parameters

[in]

r

mosek error code returned from mosek call

Returns

r untouched

mosek_linprog() [1/2]

bool igl::mosek::mosek_linprog	(	const Eigen::VectorXd &	c,
		const Eigen::SparseMatrix< double > &	A,
		const Eigen::VectorXd &	lc,
		const Eigen::VectorXd &	uc,
		const Eigen::VectorXd &	lx,
		const Eigen::VectorXd &	ux,
		Eigen::VectorXd &	x )

Solve a linear program using mosek.

Given in the form:

min c'x
s.t. lc <= A x <= uc
     lx <= x <= ux

Parameters

[in]	c	#x list of linear objective coefficients
[in]	A	#A by #x matrix of linear inequality constraint coefficients
[in]	lc	#A list of lower constraint bounds
[in]	uc	#A list of upper constraint bounds
[in]	lx	#x list of lower variable bounds
[in]	ux	#x list of upper variable bounds
[out]	x	#x list of solution values

Returns

true iff success.

mosek_linprog() [2/2]

bool igl::mosek::mosek_linprog	(	const Eigen::VectorXd &	c,
		const Eigen::SparseMatrix< double > &	A,
		const Eigen::VectorXd &	lc,
		const Eigen::VectorXd &	uc,
		const Eigen::VectorXd &	lx,
		const Eigen::VectorXd &	ux,
		const MSKenv_t &	env,
		Eigen::VectorXd &	x )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Wrapper that keeps mosek environment alive (if licence checking is becoming a bottleneck)

Parameters

[in]

env

mosek environment

mosek_quadprog() [1/2]

template<typename Index, typename Scalar>

bool igl::mosek::mosek_quadprog	(	const Index	n,
		std::vector< Index > &	Qi,
		std::vector< Index > &	Qj,
		std::vector< Scalar > &	Qv,
		const std::vector< Scalar > &	c,
		const Scalar	cf,
		const Index	m,
		std::vector< Scalar > &	Av,
		std::vector< Index > &	Ari,
		const std::vector< Index > &	Acp,
		const std::vector< Scalar > &	lc,
		const std::vector< Scalar > &	uc,
		const std::vector< Scalar > &	lx,
		const std::vector< Scalar > &	ux,
		MosekData &	mosek_data,
		std::vector< Scalar > &	x )

mosek_quadprog() [2/2]

bool igl::mosek::mosek_quadprog	(	const Eigen::SparseMatrix< double > &	Q,
		const Eigen::VectorXd &	c,
		const double	cf,
		const Eigen::SparseMatrix< double > &	A,
		const Eigen::VectorXd &	lc,
		const Eigen::VectorXd &	uc,
		const Eigen::VectorXd &	lx,
		const Eigen::VectorXd &	ux,
		MosekData &	mosek_data,
		Eigen::VectorXd &	x )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

[in]	Q	n by n square quadratic coefficients matrix only lower triangle is used.
[in]	c	n-long vector of linear coefficients
[in]	cf	constant coefficient
[in]	A	m by n square linear coefficienst matrix of inequality constraints
[in]	lc	m-long vector of lower bounds for linear inequality constraints
[in]	uc	m-long vector of upper bounds for linear inequality constraints
[in]	lx	n-long vector of lower bounds
[in]	ux	n-long vector of upper bounds
[in]	mosek_data	parameters struct
[out]	x	n-long solution vector

Returns

true only if optimization finishes without error