Namespaces
namespace	detail

namespace	preconditioner

Classes
struct	BatchQP

struct	Ldlt

struct	Model
	This class stores the model of the QP problem. More...

struct	PrimalDualGradResult

struct	QP
	This class defines the API of PROXQP solver with sparse backend. More...

struct	QpView

struct	QpViewMut

struct	SparseModel

struct	Workspace
	This class defines the workspace of the sparse solver. More...

Typedefs
template<typename T >
using	DMat = Eigen::Matrix<T, -1, -1>

template<typename T , typename I >
using	SparseMat = Eigen::SparseMatrix<T, Eigen::ColMajor, I>

template<typename T >
using	VecRef = Eigen::Ref<Eigen::Matrix<T, DYN, 1> const>

template<typename T >
using	MatRef = Eigen::Ref<Eigen::Matrix<T, DYN, DYN> const>

template<typename T >
using	Vec = Eigen::Matrix<T, DYN, 1>

template<typename T , typename I >
using	Mat = Eigen::SparseMatrix<T, Eigen::ColMajor, I>

using	VecBool = Eigen::Matrix<bool, DYN, 1>

Enumerations
enum	{ layout = Eigen::RowMajor }

Functions
template<typename T , typename I >
void	solve_in_parallel (proxqp::sparse::BatchQP< T, I > &qps, const optional< size_t > num_threads=nullopt)

template<typename T , typename I >
void	solve_in_parallel (std::vector< proxqp::sparse::QP< T, I > > &qps, const optional< size_t > num_threads=nullopt)

template<typename T , typename I >
T	power_iteration (SparseMat< T, I > &H, sparse::Vec< T > &dw, sparse::Vec< T > &rhs, sparse::Vec< T > &err_v, T power_iteration_accuracy, isize nb_power_iteration)

template<typename T , typename I >
T	min_eigen_value_via_modified_power_iteration (SparseMat< T, I > &H, sparse::Vec< T > &dw, sparse::Vec< T > &rhs, sparse::Vec< T > &err_v, T max_eigen_value, T power_iteration_accuracy, isize nb_power_iteration)

template<typename T , typename I >
T	estimate_minimal_eigen_value_of_symmetric_matrix (SparseMat< T, I > &H, T power_iteration_accuracy, isize nb_power_iteration)

template<typename T >
void	update_default_rho_with_minimal_Hessian_eigen_value (optional< T > manual_minimal_H_eigenvalue, Results< T > &results, Settings< T > &settings)

template<typename T , typename I >
void	update_proximal_parameters (Settings< T > &settings, Results< T > &results, Workspace< T, I > &work, optional< T > rho_new, optional< T > mu_eq_new, optional< T > mu_in_new)

template<typename T , typename I >
void	warm_start (optional< VecRef< T > > x_wm, optional< VecRef< T > > y_wm, optional< VecRef< T > > z_wm, Results< T > &results, Settings< T > &settings, Model< T, I > &model)

template<typename T , typename I , typename P >
void	qp_setup (QpView< T, I > qp, Results< T > &results, Model< T, I > &data, Workspace< T, I > &work, Settings< T > &settings, P &precond, PreconditionerStatus &preconditioner_status)

template<typename T , typename I >
auto	have_same_structure (proxsuite::linalg::sparse::MatRef< T, I > a, proxsuite::linalg::sparse::MatRef< T, I > b) -> bool

template<typename T , typename I >
void	copy (proxsuite::linalg::sparse::MatMut< T, I > a, proxsuite::linalg::sparse::MatRef< T, I > b)

template<typename T , typename I >
void	ldl_solve (VectorViewMut< T > sol, VectorView< T > rhs, isize n_tot, proxsuite::linalg::sparse::MatMut< T, I > ldl, Eigen::MINRES< detail::AugmentedKkt< T, I >, Eigen::Upper\|Eigen::Lower, Eigen::IdentityPreconditioner > &iterative_solver, bool do_ldlt, proxsuite::linalg::veg::dynstack::DynStackMut stack, T ldl_values, I perm, I ldl_col_ptrs, I const perm_inv)

template<typename T , typename I >
void	ldl_iter_solve_noalias (VectorViewMut< T > sol, VectorView< T > rhs, VectorView< T > init_guess, Results< T > const &results, Model< T, I > const &data, isize n_tot, proxsuite::linalg::sparse::MatMut< T, I > ldl, Eigen::MINRES< detail::AugmentedKkt< T, I >, Eigen::Upper\|Eigen::Lower, Eigen::IdentityPreconditioner > &iterative_solver, bool do_ldlt, proxsuite::linalg::veg::dynstack::DynStackMut stack, T ldl_values, I perm, I ldl_col_ptrs, I const perm_inv, Settings< T > const &settings, proxsuite::linalg::sparse::MatMut< T, I > kkt_active, proxsuite::linalg::veg::SliceMut< bool > active_constraints)

template<typename T , typename I >
void	ldl_solve_in_place (VectorViewMut< T > rhs, VectorView< T > init_guess, Results< T > const &results, Model< T, I > const &data, isize n_tot, proxsuite::linalg::sparse::MatMut< T, I > ldl, Eigen::MINRES< detail::AugmentedKkt< T, I >, Eigen::Upper\|Eigen::Lower, Eigen::IdentityPreconditioner > &iterative_solver, bool do_ldlt, proxsuite::linalg::veg::dynstack::DynStackMut stack, T ldl_values, I perm, I ldl_col_ptrs, I const perm_inv, Settings< T > const &settings, proxsuite::linalg::sparse::MatMut< T, I > kkt_active, proxsuite::linalg::veg::SliceMut< bool > active_constraints)

template<typename T , typename I >
auto	inner_reconstructed_matrix (proxsuite::linalg::sparse::MatMut< T, I > ldl) -> DMat< T >

template<typename T , typename I >
auto	reconstructed_matrix (proxsuite::linalg::sparse::MatMut< T, I > ldl, I const *perm_inv, isize n_tot) -> DMat< T >

template<typename T , typename I >
auto	reconstruction_error (proxsuite::linalg::sparse::MatMut< T, I > ldl, I const *perm_inv, Results< T > const &results, Model< T, I > const &data, isize n_tot, proxsuite::linalg::sparse::MatMut< T, I > kkt_active, proxsuite::linalg::veg::SliceMut< bool > active_constraints) -> DMat< T >

template<typename T , typename I , typename P >
void	qp_solve (Results< T > &results, Model< T, I > &data, const Settings< T > &settings, Workspace< T, I > &work, P &precond)

template<typename T , typename I >
void	print_setup_header (const Settings< T > &settings, Results< T > &results, const Model< T, I > &model)

template<typename T , typename I >
void	refactorize (Workspace< T, I > &work, Results< T > const &results, Settings< T > const &settings, proxsuite::linalg::sparse::MatMut< T, I > kkt_active, proxsuite::linalg::veg::SliceMut< bool > active_constraints, Model< T, I > const &data, proxsuite::linalg::veg::dynstack::DynStackMut stack, proxsuite::linalg::veg::Tag< T > &xtag)

template<typename T , typename I >
proxqp::Results< T >	solve (optional< SparseMat< T, I > > H, optional< VecRef< T > > g, optional< SparseMat< T, I > > A, optional< VecRef< T > > b, optional< SparseMat< T, I > > C, optional< VecRef< T > > l, optional< VecRef< T > > u, optional< VecRef< T > > x=nullopt, optional< VecRef< T > > y=nullopt, optional< VecRef< T > > z=nullopt, optional< T > eps_abs=nullopt, optional< T > eps_rel=nullopt, optional< T > rho=nullopt, optional< T > mu_eq=nullopt, optional< T > mu_in=nullopt, optional< bool > verbose=nullopt, bool compute_preconditioner=true, bool compute_timings=false, optional< isize > max_iter=nullopt, proxsuite::proxqp::InitialGuessStatus initial_guess=proxsuite::proxqp::InitialGuessStatus::EQUALITY_CONSTRAINED_INITIAL_GUESS, proxsuite::proxqp::SparseBackend sparse_backend=proxsuite::proxqp::SparseBackend::Automatic, bool check_duality_gap=false, optional< T > eps_duality_gap_abs=nullopt, optional< T > eps_duality_gap_rel=nullopt, bool primal_infeasibility_solving=false, optional< T > manual_minimal_H_eigenvalue=nullopt)

Variables
static constexpr auto	DYN = Eigen::Dynamic

Typedef Documentation

◆ DMat

template<typename T >

using proxsuite::proxqp::sparse::DMat = Eigen::Matrix<T, -1, -1>

Definition at line 23 of file fwd.hpp.

◆ SparseMat

template<typename T , typename I >

using proxsuite::proxqp::sparse::SparseMat = Eigen::SparseMatrix<T, Eigen::ColMajor, I>

Definition at line 31 of file fwd.hpp.

◆ VecRef

template<typename T >

using proxsuite::proxqp::sparse::VecRef = Eigen::Ref<Eigen::Matrix<T, DYN, 1> const>

Definition at line 34 of file fwd.hpp.

◆ MatRef

template<typename T >

using proxsuite::proxqp::sparse::MatRef = Eigen::Ref<Eigen::Matrix<T, DYN, DYN> const>

Definition at line 36 of file fwd.hpp.

◆ Vec

template<typename T >

using proxsuite::proxqp::sparse::Vec = Eigen::Matrix<T, DYN, 1>

Definition at line 38 of file fwd.hpp.

◆ Mat

template<typename T , typename I >

using proxsuite::proxqp::sparse::Mat = Eigen::SparseMatrix<T, Eigen::ColMajor, I>

Definition at line 41 of file fwd.hpp.

◆ VecBool

using proxsuite::proxqp::sparse::VecBool = Eigen::Matrix<bool, DYN, 1>

Definition at line 43 of file fwd.hpp.

Enumeration Type Documentation

◆ anonymous enum

anonymous enum

Enumerator
layout

Definition at line 26 of file fwd.hpp.

Function Documentation

◆ solve_in_parallel() [1/2]

template<typename T , typename I >

void proxsuite::proxqp::sparse::solve_in_parallel	(	proxqp::sparse::BatchQP< T, I > &	qps,
		const optional< size_t >	num_threads = nullopt )

Definition at line 144 of file qp_solve.hpp.

◆ solve_in_parallel() [2/2]

template<typename T , typename I >

void proxsuite::proxqp::sparse::solve_in_parallel	(	std::vector< proxqp::sparse::QP< T, I > > &	qps,
		const optional< size_t >	num_threads = nullopt )

Definition at line 166 of file qp_solve.hpp.

◆ power_iteration()

template<typename T , typename I >

T proxsuite::proxqp::sparse::power_iteration	(	SparseMat< T, I > &	H,
		sparse::Vec< T > &	dw,
		sparse::Vec< T > &	rhs,
		sparse::Vec< T > &	err_v,
		T	power_iteration_accuracy,
		isize	nb_power_iteration )

Definition at line 20 of file helpers.hpp.

◆ min_eigen_value_via_modified_power_iteration()

template<typename T , typename I >

T proxsuite::proxqp::sparse::min_eigen_value_via_modified_power_iteration	(	SparseMat< T, I > &	H,
		sparse::Vec< T > &	dw,
		sparse::Vec< T > &	rhs,
		sparse::Vec< T > &	err_v,
		T	max_eigen_value,
		T	power_iteration_accuracy,
		isize	nb_power_iteration )

Definition at line 60 of file helpers.hpp.

◆ estimate_minimal_eigen_value_of_symmetric_matrix()

template<typename T , typename I >

T proxsuite::proxqp::sparse::estimate_minimal_eigen_value_of_symmetric_matrix	(	SparseMat< T, I > &	H,
		T	power_iteration_accuracy,
		isize	nb_power_iteration )

Estimate minimal eigenvalue of a symmetric Matrix via power iteration

Parameters

H	symmetric matrix.
power_iteration_accuracy	power iteration algorithm accuracy tracked
nb_power_iteration	maximal number of power iteration executed

Definition at line 118 of file helpers.hpp.

◆ update_default_rho_with_minimal_Hessian_eigen_value()

template<typename T >

void proxsuite::proxqp::sparse::update_default_rho_with_minimal_Hessian_eigen_value	(	optional< T >	manual_minimal_H_eigenvalue,
		Results< T > &	results,
		Settings< T > &	settings )

Estimate H minimal eigenvalue

Parameters

settings	solver settings
results	solver results.
manual_minimal_H_eigenvalue	minimal H eigenvalue estimate.

Definition at line 159 of file helpers.hpp.

◆ update_proximal_parameters()

template<typename T , typename I >

void proxsuite::proxqp::sparse::update_proximal_parameters	(	Settings< T > &	settings,
		Results< T > &	results,
		Workspace< T, I > &	work,
		optional< T >	rho_new,
		optional< T >	mu_eq_new,
		optional< T >	mu_in_new )

Update the proximal parameters of the results object.

Parameters

rho_new	primal proximal parameter
mu_eq_new	dual equality proximal parameter
mu_in_new	dual inequality proximal parameter
results	solver result

Definition at line 183 of file helpers.hpp.

◆ warm_start()

template<typename T , typename I >

void proxsuite::proxqp::sparse::warm_start	(	optional< VecRef< T > >	x_wm,
		optional< VecRef< T > >	y_wm,
		optional< VecRef< T > >	z_wm,
		Results< T > &	results,
		Settings< T > &	settings,
		Model< T, I > &	model )

Warm start the results primal and dual variables.

Parameters

x_wm	primal proximal parameter
y_wm	dual equality proximal parameter
z_wm	dual inequality proximal parameter
results	solver result
settings	solver settings

Definition at line 219 of file helpers.hpp.

◆ qp_setup()

template<typename T , typename I , typename P >

void proxsuite::proxqp::sparse::qp_setup	(	QpView< T, I >	qp,
		Results< T > &	results,
		Model< T, I > &	data,
		Workspace< T, I > &	work,
		Settings< T > &	settings,
		P &	precond,
		PreconditionerStatus &	preconditioner_status )

Setups the QP solver model.

Parameters

qp	view of the QP model.
work	solver workspace.
settings	solver settings.
data	solver model.
results	solver result.
precond	preconditioner.
preconditioner_status	bool variable for deciding whether executing the preconditioning algorithm, or keeping previous preconditioning variables, or using the identity preconditioner (i.e., no preconditioner).

Definition at line 282 of file helpers.hpp.

◆ have_same_structure()

template<typename T , typename I >

auto proxsuite::proxqp::sparse::have_same_structure	(	proxsuite::linalg::sparse::MatRef< T, I >	a,
		proxsuite::linalg::sparse::MatRef< T, I >	b ) -> bool

Checks whether matrix b has the same sparsity structure as matrix a.

Parameters

a	matrix.
b	matrix.

Definition at line 414 of file helpers.hpp.

◆ copy()

template<typename T , typename I >

void proxsuite::proxqp::sparse::copy	(	proxsuite::linalg::sparse::MatMut< T, I >	a,
		proxsuite::linalg::sparse::MatRef< T, I >	b )

Copies matrix b elements into matrix a.

Parameters

a	matrix.
b	matrix.

Definition at line 443 of file helpers.hpp.

◆ ldl_solve()

template<typename T , typename I >

void proxsuite::proxqp::sparse::ldl_solve	(	VectorViewMut< T >	sol,
		VectorView< T >	rhs,
		isize	n_tot,
		proxsuite::linalg::sparse::MatMut< T, I >	ldl,
		Eigen::MINRES< detail::AugmentedKkt< T, I >, Eigen::Upper\|Eigen::Lower, Eigen::IdentityPreconditioner > &	iterative_solver,
		bool	do_ldlt,
		proxsuite::linalg::veg::dynstack::DynStackMut	stack,
		T *	ldl_values,
		I *	perm,
		I *	ldl_col_ptrs,
		I const *	perm_inv )

Definition at line 40 of file solver.hpp.

◆ ldl_iter_solve_noalias()

template<typename T , typename I >

void proxsuite::proxqp::sparse::ldl_iter_solve_noalias	(	VectorViewMut< T >	sol,
		VectorView< T >	rhs,
		VectorView< T >	init_guess,
		Results< T > const &	results,
		Model< T, I > const &	data,
		isize	n_tot,
		proxsuite::linalg::sparse::MatMut< T, I >	ldl,
		Eigen::MINRES< detail::AugmentedKkt< T, I >, Eigen::Upper\|Eigen::Lower, Eigen::IdentityPreconditioner > &	iterative_solver,
		bool	do_ldlt,
		proxsuite::linalg::veg::dynstack::DynStackMut	stack,
		T *	ldl_values,
		I *	perm,
		I *	ldl_col_ptrs,
		I const *	perm_inv,
		Settings< T > const &	settings,
		proxsuite::linalg::sparse::MatMut< T, I >	kkt_active,
		proxsuite::linalg::veg::SliceMut< bool >	active_constraints )

Definition at line 88 of file solver.hpp.

◆ ldl_solve_in_place()

template<typename T , typename I >

void proxsuite::proxqp::sparse::ldl_solve_in_place	(	VectorViewMut< T >	rhs,
		VectorView< T >	init_guess,
		Results< T > const &	results,
		Model< T, I > const &	data,
		isize	n_tot,
		proxsuite::linalg::sparse::MatMut< T, I >	ldl,
		Eigen::MINRES< detail::AugmentedKkt< T, I >, Eigen::Upper\|Eigen::Lower, Eigen::IdentityPreconditioner > &	iterative_solver,
		bool	do_ldlt,
		proxsuite::linalg::veg::dynstack::DynStackMut	stack,
		T *	ldl_values,
		I *	perm,
		I *	ldl_col_ptrs,
		I const *	perm_inv,
		Settings< T > const &	settings,
		proxsuite::linalg::sparse::MatMut< T, I >	kkt_active,
		proxsuite::linalg::veg::SliceMut< bool >	active_constraints )

Solves in place a linear system.

Parameters

rhs	right hand side vector of the linear system to solver.
init_guess	initial guess for solving the linear system
ldl	current ldlt.
do_ldlt	boolean variable for doing the ldlt (rather than MinRes algorithm).
perm_inv	pointer to the inverse of the permutation.
results	solver results.
data	model of the QP.
n_tot	dimension of the KKT matrix
kkt_active	active part of the KKT matrix.
active_constraints	vector boolean precising whether the constraints are active or not.
iterative_solver	iterative solver matrix free.
stack	memory stack.
ldl_values	pointor to ldl values.
perm	pointor to the ldl permutation.
ldl_col_ptrs	pointor to the column of the ldl.
perm_inv	pointor the inverse permutation.
settings	solver's settings.
kkt_active	active part of the kkt.

Definition at line 201 of file solver.hpp.

◆ inner_reconstructed_matrix()

template<typename T , typename I >

auto proxsuite::proxqp::sparse::inner_reconstructed_matrix ( proxsuite::linalg::sparse::MatMut< T, I > ldl ) -> DMat<T>

Reconstructs manually the permutted matrix.

Parameters

ldl	current ldlt.
do_ldlt	boolean variable for doing the ldlt (rather than MinRes algorithm).

Definition at line 250 of file solver.hpp.

◆ reconstructed_matrix()

template<typename T , typename I >

auto proxsuite::proxqp::sparse::reconstructed_matrix	(	proxsuite::linalg::sparse::MatMut< T, I >	ldl,
		I const *	perm_inv,
		isize	n_tot ) -> DMat<T>

Reconstructs manually the value of the KKT matrix.

Parameters

ldl	current ldlt.
do_ldlt	boolean variable for doing the ldlt (rather than MinRes algorithm).
perm_inv	pointer to the inverse of the permutation.
n_tot	dimension of the KKT matrix

Definition at line 271 of file solver.hpp.

◆ reconstruction_error()

template<typename T , typename I >

auto proxsuite::proxqp::sparse::reconstruction_error	(	proxsuite::linalg::sparse::MatMut< T, I >	ldl,
		I const *	perm_inv,
		Results< T > const &	results,
		Model< T, I > const &	data,
		isize	n_tot,
		proxsuite::linalg::sparse::MatMut< T, I >	kkt_active,
		proxsuite::linalg::veg::SliceMut< bool >	active_constraints ) -> DMat<T>

Derives the norm of the difference between current KKT and the one it should be (derived manually).

Parameters

ldl	current ldlt.
perm_inv	pointer to the inverse of the permutation.
results	solver results.
data	model of the QP.
n_tot	dimension of the KKT matrix
kkt_active	active part of the KKT matrix.
active_constraints	vector boolean precising whether the constraints are active or not.

Definition at line 299 of file solver.hpp.

◆ qp_solve()

template<typename T , typename I , typename P >

void proxsuite::proxqp::sparse::qp_solve	(	Results< T > &	results,
		Model< T, I > &	data,
		const Settings< T > &	settings,
		Workspace< T, I > &	work,
		P &	precond )

Executes the PROXQP algorithm.

Parameters

results	solver results.
data	QP problem model as defined by the user (without any scaling performed).
settings	solver settings.
work	solver workspace.
precond	preconditioner.

Definition at line 344 of file solver.hpp.

◆ print_setup_header()

template<typename T , typename I >

void proxsuite::proxqp::sparse::print_setup_header	(	const Settings< T > &	settings,
		Results< T > &	results,
		const Model< T, I > &	model )

Definition at line 36 of file utils.hpp.

◆ refactorize()

template<typename T , typename I >

void proxsuite::proxqp::sparse::refactorize	(	Workspace< T, I > &	work,
		Results< T > const &	results,
		Settings< T > const &	settings,
		proxsuite::linalg::sparse::MatMut< T, I >	kkt_active,
		proxsuite::linalg::veg::SliceMut< bool >	active_constraints,
		Model< T, I > const &	data,
		proxsuite::linalg::veg::dynstack::DynStackMut	stack,
		proxsuite::linalg::veg::Tag< T > &	xtag )

Definition at line 33 of file workspace.hpp.

◆ solve()

template<typename T , typename I >

proxqp::Results< T > proxsuite::proxqp::sparse::solve	(	optional< SparseMat< T, I > >	H,
		optional< VecRef< T > >	g,
		optional< SparseMat< T, I > >	A,
		optional< VecRef< T > >	b,
		optional< SparseMat< T, I > >	C,
		optional< VecRef< T > >	l,
		optional< VecRef< T > >	u,
		optional< VecRef< T > >	x = nullopt,
		optional< VecRef< T > >	y = nullopt,
		optional< VecRef< T > >	z = nullopt,
		optional< T >	eps_abs = nullopt,
		optional< T >	eps_rel = nullopt,
		optional< T >	rho = nullopt,
		optional< T >	mu_eq = nullopt,
		optional< T >	mu_in = nullopt,
		optional< bool >	verbose = nullopt,
		bool	compute_preconditioner = true,
		bool	compute_timings = false,
		optional< isize >	max_iter = nullopt,
		proxsuite::proxqp::InitialGuessStatus	initial_guess = proxsuite::proxqp::InitialGuessStatus::EQUALITY_CONSTRAINED_INITIAL_GUESS,
		proxsuite::proxqp::SparseBackend	sparse_backend = proxsuite::proxqp::SparseBackend::Automatic,
		bool	check_duality_gap = false,
		optional< T >	eps_duality_gap_abs = nullopt,
		optional< T >	eps_duality_gap_rel = nullopt,
		bool	primal_infeasibility_solving = false,
		optional< T >	manual_minimal_H_eigenvalue = nullopt )

Solves the QP problem using PROXQP algorithm without the need to define a QP object, with matrices defined by Dense Eigen matrices. It is possible to set up some of the solver parameters (warm start, initial guess option, proximal step sizes, absolute and relative accuracies, maximum number of iterations, preconditioner execution).

Parameters

H	quadratic cost input defining the QP model.
g	linear cost input defining the QP model.
A	equality constraint matrix input defining the QP model.
b	equality constraint vector input defining the QP model.
C	inequality constraint matrix input defining the QP model.
l	lower inequality constraint vector input defining the QP model.
u	upper inequality constraint vector input defining the QP model.
x	primal warm start.
y	dual equality constraint warm start.
z	dual inequality constraint warm start.
verbose	if set to true, the solver prints more information about each iteration.
compute_preconditioner	boolean parameter for executing or not the preconditioner.
compute_timings	boolean parameter for computing the solver timings.
rho	proximal step size wrt primal variable.
mu_eq	proximal step size wrt equality constrained multiplier.
mu_in	proximal step size wrt inequality constrained multiplier.
eps_abs	absolute accuracy threshold.
eps_rel	relative accuracy threshold.
max_iter	maximum number of iteration.
initial_guess	initial guess option for warm starting or not the initial iterate values.
check_duality_gap	If set to true, include the duality gap in absolute and relative stopping criteria.
eps_duality_gap_abs	absolute accuracy threshold for the duality-gap criterion.
eps_duality_gap_rel	relative accuracy threshold for the duality-gap criterion.

Definition at line 707 of file wrapper.hpp.

Variable Documentation

◆ DYN

constexpr auto proxsuite::proxqp::sparse::DYN = Eigen::Dynamic

staticconstexpr

Definition at line 25 of file fwd.hpp.

Namespaces

Classes

Typedefs

Enumerations

Functions

Variables

Typedef Documentation

◆ DMat

◆ SparseMat

◆ VecRef

◆ MatRef

◆ Vec

◆ Mat

◆ VecBool

Enumeration Type Documentation

◆ anonymous enum

Function Documentation

◆ solve_in_parallel() [1/2]

◆ solve_in_parallel() [2/2]

◆ power_iteration()

◆ min_eigen_value_via_modified_power_iteration()

◆ estimate_minimal_eigen_value_of_symmetric_matrix()

◆ update_default_rho_with_minimal_Hessian_eigen_value()

◆ update_proximal_parameters()

◆ warm_start()

◆ qp_setup()

◆ have_same_structure()

◆ copy()

◆ ldl_solve()

◆ ldl_iter_solve_noalias()

◆ ldl_solve_in_place()

◆ inner_reconstructed_matrix()

◆ reconstructed_matrix()

◆ reconstruction_error()

◆ qp_solve()

◆ print_setup_header()

◆ refactorize()

◆ solve()

Variable Documentation

◆ DYN