Namespaces
namespace	anonymous_namespace{ocproblem-counters.cpp}

namespace	anonymous_namespace{problem-counters.cpp}

namespace	anonymous_namespace{qpalm-adapter.cpp}

namespace	casadi_loader

namespace	csv

namespace	cutest

namespace	detail

namespace	dl

namespace	functions

namespace	lbfgsb

namespace	lbfgspp

namespace	params

namespace	sets

namespace	sparsity

namespace	tag_invoke_fn_ns

namespace	tag_invoke_ns

namespace	util

namespace	vec_util

Classes
struct	ALMParams
	Parameters for the Augmented Lagrangian solver. More...

class	ALMSolver
	Augmented Lagrangian Method solver. More...

class	AndersonAccel
	Anderson Acceleration. More...

struct	AndersonAccelParams
	Parameters for the AndersonAccel class. More...

struct	AndersonDirection

struct	AndersonDirectionParams
	Parameters for the AndersonDirection class. More...

class	AtomicStopSignal

class	BoxConstrProblem
	Implements common problem functions for minimization problems with box constraints. More...

class	CasADiControlProblem

class	CasADiProblem
	Problem definition for a CasADi problem, loaded from a DLL. More...

struct	CBFGSParams
	Cautious BFGS update. More...

struct	CircularIndexIterator

struct	CircularIndices

class	CircularRange

struct	ControlProblemVTable

struct	ControlProblemWithCounters

class	CUTEstLoader

class	CUTEstProblem
	Wrapper for CUTEst problems loaded from an external shared library. More...

struct	Dim

struct	EigenConfig

struct	EigenConfigd
	Double-precision `double` configuration. More...

struct	EigenConfigf
	Single-precision `float` configuration. More...

struct	EigenConfigl
	`long double` configuration. More...

struct	EvalCounter

class	FunctionalProblem
	Problem class that allows specifying the basic functions as C++ `std::function`s. More...

struct	InnerSolveOptions

struct	InnerStatsAccumulator

struct	InnerStatsAccumulator< lbfgsb::LBFGSBStats >

struct	InnerStatsAccumulator< lbfgspp::LBFGSBStats< Conf > >

struct	InnerStatsAccumulator< PANOCOCPStats< Conf > >

struct	InnerStatsAccumulator< PANOCStats< Conf > >

struct	InnerStatsAccumulator< PANTRStats< Conf > >

struct	InnerStatsAccumulator< ZeroFPRStats< Conf > >

class	IpoptAdapter
	Based on https://coin-or.github.io/Ipopt/INTERFACES.html. More...

struct	is_complex_float

struct	is_complex_float< std::complex< T > >

struct	is_config

struct	is_config< EigenConfigd >

struct	is_config< EigenConfigf >

struct	is_config< EigenConfigl >

struct	is_config< EigenConfigq >

struct	KKTError

class	LBFGS
	Limited memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm. More...

struct	LBFGSDirection

struct	LBFGSDirectionParams
	Parameters for the LBFGSDirection class. More...

struct	LBFGSParams
	Parameters for the LBFGS class. More...

struct	LBFGSStorage
	Layout: More...

class	LimitedMemoryQR
	Incremental QR factorization using modified Gram-Schmidt with reorthogonalization. More...

struct	LipschitzEstimateParams

class	MaxHistory
	Keep track of the maximum value over a specified horizon length. More...

struct	NewtonTRDirection

struct	NewtonTRDirectionParams
	Parameters for the NewtonTRDirection class. More...

struct	NoopDirection
	Direction provider that provides no directions (apply always returns false). More...

struct	not_implemented_error

struct	OCPDim

struct	OCPEvalCounter

struct	OCPEvaluator

struct	OCPVariables

struct	OwningQPALMData

struct	PANOCDirection
	This class outlines the interface for direction providers used by PANOC-like algorithms. More...

struct	PANOCOCPParams
	Tuning parameters for the PANOC algorithm. More...

struct	PANOCOCPProgressInfo

class	PANOCOCPSolver

struct	PANOCOCPStats

struct	PANOCParams
	Tuning parameters for the PANOC algorithm. More...

struct	PANOCProgressInfo

class	PANOCSolver
	PANOC solver for ALM. More...

struct	PANOCStats

struct	PANTRDirection
	This class outlines the interface for direction providers used by PANTR-like algorithms. More...

struct	PANTRParams
	Tuning parameters for the PANTR algorithm. More...

struct	PANTRProgressInfo

class	PANTRSolver
	PANTR solver for ALM. More...

struct	PANTRStats

struct	ProblemVTable
	Struct containing function pointers to all problem functions (like the objective and constraint functions, with their derivatives, and more). More...

struct	ProblemWithCounters
	Problem wrapper that keeps track of the number of evaluations and the run time of each function. More...

struct	prox_fn
	Proximal mapping customization point. More...

struct	prox_step_fn
	Proximal mapping customization point for forward-backward steps. More...

struct	ReverseCircularIndexIterator

class	ReverseCircularRange

struct	ScopedMallocAllower

struct	ScopedMallocBlocker

struct	ScopedMallocChecker

struct	StatefulLQRFactor

struct	SteihaugCG
	Steihaug conjugate gradients procedure based on https://github.com/scipy/scipy/blob/583e70a50573169fc352b5dc6d94588a97c7389a/scipy/optimize/_trustregion_ncg.py#L44. More...

struct	SteihaugCGParams

struct	StructuredLBFGSDirection

struct	StructuredLBFGSDirectionParams
	Parameters for the StructuredLBFGSDirection class. More...

struct	StructuredNewtonDirection

struct	StructuredNewtonDirectionParams
	Parameters for the StructuredNewtonDirection class. More...

struct	StructuredNewtonRegularizationParams
	Parameters for the StructuredNewtonDirection class. More...

class	TypeErasedControlProblem
	Nonlinear optimal control problem with finite horizon \( N \). More...

class	TypeErasedProblem
	The main polymorphic minimization problem interface. More...

class	UnconstrProblem
	Implements common problem functions for minimization problems without constraints. More...

struct	ZeroFPRParams
	Tuning parameters for the ZeroFPR algorithm. More...

struct	ZeroFPRProgressInfo

class	ZeroFPRSolver
	ZeroFPR solver for ALM. More...

struct	ZeroFPRStats

Concepts
concept	Config

concept	VectorRefLike

concept	float_or_complex_float

concept	tag_invocable

concept	nothrow_tag_invocable

Typedefs
using	DefaultConfig = EigenConfigd

template<Config Conf = DefaultConfig>
using	real_t = typename Conf::real_t

template<Config Conf = DefaultConfig>
using	vec = typename Conf::vec

template<Config Conf = DefaultConfig>
using	mvec = typename Conf::mvec

template<Config Conf = DefaultConfig>
using	cmvec = typename Conf::cmvec

template<Config Conf = DefaultConfig>
using	rvec = typename Conf::rvec

template<Config Conf = DefaultConfig>
using	crvec = typename Conf::crvec

template<Config Conf = DefaultConfig>
using	mat = typename Conf::mat

template<Config Conf = DefaultConfig>
using	mmat = typename Conf::mmat

template<Config Conf = DefaultConfig>
using	cmmat = typename Conf::cmmat

template<Config Conf = DefaultConfig>
using	rmat = typename Conf::rmat

template<Config Conf = DefaultConfig>
using	crmat = typename Conf::crmat

template<Config Conf = DefaultConfig>
using	length_t = typename Conf::length_t

template<Config Conf = DefaultConfig>
using	index_t = typename Conf::index_t

template<Config Conf = DefaultConfig>
using	indexvec = typename Conf::indexvec

template<Config Conf = DefaultConfig>
using	rindexvec = typename Conf::rindexvec

template<Config Conf = DefaultConfig>
using	crindexvec = typename Conf::crindexvec

template<Config Conf = DefaultConfig>
using	mindexvec = typename Conf::mindexvec

template<Config Conf = DefaultConfig>
using	cmindexvec = typename Conf::cmindexvec

template<typename Tag , typename... Args>
using	tag_invoke_result = std::invoke_result< decltype(::alpaqa::alpaqa_tag_invoke), Tag, Args... >

template<typename Tag , typename... Args>
using	tag_invoke_result_t = std::invoke_result_t< decltype(::alpaqa::alpaqa_tag_invoke), Tag, Args... >

template<auto & Tag>
using	tag_t = std::decay_t< decltype(Tag)>

using	function_dict_t = alpaqa_function_dict_t

using	problem_register_t = alpaqa_problem_register_t

using	control_problem_register_t = alpaqa_control_problem_register_t

using	problem_functions_t = alpaqa_problem_functions_t

using	control_problem_functions_t = alpaqa_control_problem_functions_t

Enumerations
enum class	LBFGSStepSize { BasedOnExternalStepSize = 0 , BasedOnCurvature = 1 , BasedOnGradientStepSize }
	Which method to use to select the L-BFGS step size. More...

enum class	PANOCStopCrit { ApproxKKT = 0 , ApproxKKT2 , ProjGradNorm , ProjGradNorm2 , ProjGradUnitNorm , ProjGradUnitNorm2 , FPRNorm , FPRNorm2 , Ipopt , LBFGSBpp }

enum class	SolverStatus { Busy = 0 , Converged , MaxTime , MaxIter , NotFinite , NoProgress , Interrupted , Exception }
	Exit status of a numerical solver such as ALM or PANOC. More...

Functions
template<Config Conf = DefaultConfig>
void	minimize_update_anderson (LimitedMemoryQR< Conf > &qr, rmat< Conf > G̃, crvec< Conf > rₖ, crvec< Conf > rₗₐₛₜ, crvec< Conf > gₖ, real_t< Conf > min_div_fac, rvec< Conf > γ_LS, rvec< Conf > xₖ_aa)
	Solve one step of Anderson acceleration to find a fixed point of a function g(x):

std::string_view	float_to_str_vw_snprintf (auto &&print, auto &buf, std::floating_point auto value, int precision, const char *fmt)

std::string_view	float_to_str_vw (auto &buf, double value, int precision=std::numeric_limits< double >::max_digits10)

std::string_view	float_to_str_vw (auto &buf, float value, int precision=std::numeric_limits< float >::max_digits10)

std::string_view	float_to_str_vw (auto &buf, long double value, int precision=std::numeric_limits< long double >::max_digits10)

template<std::floating_point F>
std::string	float_to_str (F value, int precision)

template<std::floating_point F>
void	print_elem (auto &buf, F value, std::ostream &os)

template<std::integral I>
void	print_elem (auto &, I value, std::ostream &os)

template<std::floating_point F>
void	print_elem (auto &buf, std::complex< F > value, std::ostream &os)

template<Config Conf, VectorRefLike< Conf > V>
constexpr auto	const_or_mut_rvec (V &&v)

constexpr const char *	enum_name (PANOCStopCrit s)

std::ostream &	operator<< (std::ostream &os, PANOCStopCrit s)

template<Config Conf>
InnerStatsAccumulator< PANOCOCPStats< Conf > > &	operator+= (InnerStatsAccumulator< PANOCOCPStats< Conf > > &acc, const PANOCOCPStats< Conf > &s)

template<Config Conf>
InnerStatsAccumulator< PANOCStats< Conf > > &	operator+= (InnerStatsAccumulator< PANOCStats< Conf > > &acc, const PANOCStats< Conf > &s)

template<Config Conf>
InnerStatsAccumulator< PANTRStats< Conf > > &	operator+= (InnerStatsAccumulator< PANTRStats< Conf > > &acc, const PANTRStats< Conf > &s)

template<Config Conf>
InnerStatsAccumulator< ZeroFPRStats< Conf > > &	operator+= (InnerStatsAccumulator< ZeroFPRStats< Conf > > &acc, const ZeroFPRStats< Conf > &s)

template<Config Conf>
KKTError< Conf >	compute_kkt_error (const TypeErasedProblem< Conf > &problem, crvec< Conf > x, crvec< Conf > y)

std::ostream &	operator<< (std::ostream &, const OCPEvalCounter &)

OCPEvalCounter::OCPEvalTimer &	operator+= (OCPEvalCounter::OCPEvalTimer &a, const OCPEvalCounter::OCPEvalTimer &b)

OCPEvalCounter &	operator+= (OCPEvalCounter &a, const OCPEvalCounter &b)

OCPEvalCounter	operator+ (OCPEvalCounter a, const OCPEvalCounter &b)

void	check_finiteness (const auto &v, std::string_view msg)
	If the given vector `v` is not finite, break or throw an exception with the given message `msg`.

void	check_finiteness (const std::floating_point auto &v, std::string_view msg)

template<class Problem >
auto	ocproblem_with_counters (Problem &&p)

template<class Problem >
auto	ocproblem_with_counters_ref (Problem &p)

std::ostream &	operator<< (std::ostream &, const EvalCounter &)

EvalCounter::EvalTimer &	operator+= (EvalCounter::EvalTimer &a, const EvalCounter::EvalTimer &b)

EvalCounter &	operator+= (EvalCounter &a, const EvalCounter &b)

EvalCounter	operator+ (EvalCounter a, const EvalCounter &b)

template<class Problem >
auto	problem_with_counters (Problem &&p)
	Wraps the given problem into a ProblemWithCounters and keeps track of how many times each function is called, and how long these calls took.

template<class Problem >
auto	problem_with_counters_ref (Problem &p)
	Wraps the given problem into a ProblemWithCounters and keeps track of how many times each function is called, and how long these calls took.

template<Config Conf>
void	print_provided_functions (std::ostream &os, const TypeErasedProblem< Conf > &problem)

template<class Derived , class... Args>
std::ostream &	print_csv (std::ostream &os, const Eigen::DenseBase< Derived > &M, Args &&...args)

template<class Derived , class... Args>
std::ostream &	print_matlab (std::ostream &os, const Eigen::DenseBase< Derived > &M, Args &&...args)

template<class Derived , class... Args>
std::ostream &	print_python (std::ostream &os, const Eigen::DenseBase< Derived > &M, Args &&...args)

template<class Solver >
auto	attach_cancellation (Solver &solver)
	Attach SIGINT and SIGTERM handlers to stop the given solver.

std::ostream &	operator<< (std::ostream &os, SolverStatus s)

template<Config Conf>
Sparsity< Conf >	convert_csc (const auto &sp, sparsity::Symmetry symmetry)

template<class Func >
void	register_function (function_dict_t *&extra_functions, std::string name, Func &&func)
	Make the given function available to alpaqa.

template<class Func >
void	register_function (problem_register_t &result, std::string name, Func &&func)

template<class Func >
void	register_function (control_problem_register_t &result, std::string name, Func &&func)

template<class Result , class T , class Ret , class... Args>
void	register_member_function (Result &result, std::string name, Ret(T::*member)(Args...))

template<auto Member>
static auto	member_caller ()
	Wrap the given member function or variable into a (possibly generic) lambda function that accepts the instance as a type-erased void pointer.

void	unregister_functions (function_dict_t *&extra_functions)
	Cleans up the extra functions registered by register_function.

InnerStatsAccumulator< lbfgsb::LBFGSBStats > &	operator+= (InnerStatsAccumulator< lbfgsb::LBFGSBStats > &acc, const lbfgsb::LBFGSBStats &s)

template<Config Conf>
InnerStatsAccumulator< lbfgspp::LBFGSBStats< Conf > > &	operator+= (InnerStatsAccumulator< lbfgspp::LBFGSBStats< Conf > > &acc, const lbfgspp::LBFGSBStats< Conf > &s)

OwningQPALMData	build_qpalm_problem (const TypeErasedProblem< EigenConfigd > &problem)

Variables
template<class T >
constexpr bool	is_config_v = is_config<T>::value

template<Config Conf>
constexpr const auto	inf = std::numeric_limits<real_t<Conf>>::infinity()

template<Config Conf>
constexpr const auto	NaN = std::numeric_limits<real_t<Conf>>::quiet_NaN()

template<Config Conf>
const rvec< Conf >	null_vec = mvec<Conf>{nullptr, 0}
	Global empty vector for convenience.

template<Config Conf>
const rindexvec< Conf >	null_indexvec = mindexvec<Conf>{nullptr, 0}
	Global empty index vector for convenience.

struct alpaqa::prox_fn	prox
	Compute the proximal mapping.

struct alpaqa::prox_step_fn	prox_step
	Compute a generalized forward-backward step.

template<class T >
constexpr bool	is_complex_float_v = is_complex_float<T>::value

constexpr tag_invoke_fn_ns::tag_invoke_fn	alpaqa_tag_invoke = {}

template<typename Tag , typename... Args>
constexpr bool	is_tag_invocable_v = tag_invocable<Tag, Args...>

template<typename Tag , typename... Args>
constexpr bool	is_nothrow_tag_invocable_v

Class Documentation

◆ alpaqa::ALMParams

struct alpaqa::ALMParams

Collaboration diagram for ALMParams< Conf >:

Class Members
real_t	tolerance = real_t(1e-5)	Primal tolerance (used for stopping criterion of inner solver).
real_t	dual_tolerance = real_t(1e-5)	Dual tolerance (constraint violation and complementarity).
real_t	penalty_update_factor = 10	Factor used in updating the penalty parameters.
real_t	penalty_update_factor_lower = real_t(0.8)	Factor to reduce penalty_update_factor when inner convergence fails.
real_t	min_penalty_update_factor = real_t(1.1)	Minimum value for penalty_update_factor after reduction because of inner convergence failure.
real_t	initial_penalty = 1	Initial penalty parameter. When set to zero (which is the default), it is computed automatically, based on the constraint violation in the starting point and the parameter initial_penalty_factor.
real_t	initial_penalty_factor = 20	Initial penalty parameter factor. Active if initial_penalty is set to zero.
real_t	initial_penalty_lower = real_t(0.6)	Factor to reduce the initial penalty factor by if convergence fails in in the first iteration.
real_t	initial_tolerance = 1	Initial primal tolerance.
real_t	initial_tolerance_increase = real_t(1.1)	Factor to increase the initial primal tolerance if convergence fails in the first iteration.
real_t	tolerance_update_factor = real_t(1e-1)	Update factor for primal tolerance.
real_t	ρ_increase = real_t(2)	Factor to increase the primal tolerance update factor by if convergence fails.
real_t	ρ_max = real_t(0.5)	Maximum value of tolerance_update_factor after increase because of inner convergence failure.
real_t	rel_penalty_increase_threshold = real_t(0.1)	Error tolerance for penalty increase.
real_t	max_multiplier = real_t(1e9)	Lagrange multiplier bound.
real_t	max_penalty = real_t(1e9)	Maximum penalty factor.
real_t	min_penalty = real_t(1e-9)	Minimum penalty factor (used during initialization).
unsigned int	max_iter = 100	Maximum number of outer ALM iterations.
nanoseconds	max_time = std::chrono::minutes(5)	Maximum duration.
unsigned	max_num_initial_retries = 0	How many times can the initial penalty initial_penalty or initial_penalty_factor and the initial primal tolerance initial_tolerance be reduced.
unsigned	max_num_retries = 0	How many times can the penalty update factor penalty_update_factor and the primal tolerance factor tolerance_update_factor be reduced.
unsigned	max_total_num_retries = 0	Combined limit for max_num_initial_retries and max_num_retries.
unsigned	print_interval = 0	When to print progress. If set to zero, nothing will be printed. If set to N != 0, progress is printed every N iterations.
int	print_precision = std::numeric_limits<real_t>::max_digits10 / 2	The precision of the floating point values printed by the solver.
bool	single_penalty_factor = false	Use one penalty factor for all m constraints.

◆ alpaqa::AndersonAccelParams

struct alpaqa::AndersonAccelParams

Collaboration diagram for AndersonAccelParams< Conf >:

Class Members
length_t	memory = 10	Length of the history to keep (the number of columns in the QR factorization). If this number is greater than the problem dimension, the memory is set to the problem dimension (otherwise the system is underdetermined).
real_t	min_div_fac = real_t(1e2) * std::numeric_limits<real_t>::epsilon()	Minimum divisor when solving close to singular systems, scaled by the maximum eigenvalue of R.

◆ alpaqa::AndersonDirectionParams

struct alpaqa::AndersonDirectionParams

Collaboration diagram for AndersonDirectionParams< Conf >:

Class Members
bool	rescale_on_step_size_changes = false

◆ alpaqa::EigenConfig

struct alpaqa::EigenConfig

Collaboration diagram for EigenConfig< RealT >:

Class Members
typedef RealT	real_t	Real scalar element type.
typedef VectorX< real_t >	vec	Dynamic vector type.
typedef Map< vec >	mvec	Map of vector type.
typedef Map< const vec >	cmvec	Immutable map of vector type.
typedef Ref< vec >	rvec	Reference to mutable vector.
typedef Ref< const vec >	crvec	Reference to immutable vector.
typedef MatrixX< real_t >	mat	Dynamic matrix type.
typedef Map< mat >	mmat	Map of matrix type.
typedef Map< const mat >	cmmat	Immutable map of matrix type.
typedef Ref< mat >	rmat	Reference to mutable matrix.
typedef Ref< const mat >	crmat	Reference to immutable matrix.
typedef Index	length_t	Type for lengths and sizes.
typedef Index	index_t	Type for vector and matrix indices.
typedef VectorX< index_t >	indexvec	Dynamic vector of indices.
typedef Ref< indexvec >	rindexvec	Reference to mutable index vector.
typedef Ref< const indexvec >	crindexvec	Reference to immutable index vector.
typedef Map< indexvec >	mindexvec	Map of index vector type.
typedef Map< const indexvec >	cmindexvec	Immutable map of index vector type.

◆ alpaqa::InnerSolveOptions

struct alpaqa::InnerSolveOptions

Collaboration diagram for InnerSolveOptions< Conf >:

Class Members
bool	always_overwrite_results = true	Return the final iterate and multipliers, even if the solver did not converge.
optional< nanoseconds >	max_time = std::nullopt	Maximum run time (in addition to the inner solver's own timeout). Zero means no timeout.
real_t	tolerance = 0	Desired tolerance (overrides the solver's own tolerance). Zero means no tolerance (use solver's own tolerance).
ostream *	os = nullptr	Output stream to print to.
unsigned	outer_iter = 0	The current iteration of the outer solver.
bool	check = true	Call TypeErasedProblem::check() before starting to solve.

◆ alpaqa::InnerStatsAccumulator

struct alpaqa::InnerStatsAccumulator

Collaboration diagram for InnerStatsAccumulator< InnerSolverStats >:

◆ alpaqa::InnerStatsAccumulator< lbfgsb::LBFGSBStats >

struct alpaqa::InnerStatsAccumulator< lbfgsb::LBFGSBStats >

Collaboration diagram for InnerStatsAccumulator< lbfgsb::LBFGSBStats >:

Class Members
nanoseconds	elapsed_time {}	Total elapsed time in the inner solver.
unsigned	iterations = 0	Total number of inner PANOC iterations.
real_t	final_ψ = 0	Final value of the smooth cost \( \psi(\hat x) \).
unsigned	lbfgs_rejected = 0	Total number of times that the L-BFGS update was rejected (i.e. it could have resulted in a non-positive definite Hessian estimate).

◆ alpaqa::InnerStatsAccumulator< lbfgspp::LBFGSBStats< Conf > >

struct alpaqa::InnerStatsAccumulator< lbfgspp::LBFGSBStats< Conf > >

Collaboration diagram for InnerStatsAccumulator< lbfgspp::LBFGSBStats< Conf > >:

Class Members
nanoseconds	elapsed_time {}	Total elapsed time in the inner solver.
unsigned	iterations = 0	Total number of inner PANOC iterations.
real_t	final_ψ = 0	Final value of the smooth cost \( \psi(\hat x) \).

◆ alpaqa::InnerStatsAccumulator< PANOCOCPStats< Conf > >

struct alpaqa::InnerStatsAccumulator< PANOCOCPStats< Conf > >

Collaboration diagram for InnerStatsAccumulator< PANOCOCPStats< Conf > >:

Class Members
nanoseconds	elapsed_time {}	Total elapsed time in the inner solver.
nanoseconds	time_prox {}	Total time spent computing proximal mappings.
nanoseconds	time_forward {}	Total time spent doing forward simulations.
nanoseconds	time_backward {}	Total time spent doing backward gradient evaluations.
nanoseconds	time_jacobians {}	Total time spent computing dynamics Jacobians.
nanoseconds	time_hessians {}	Total time spent computing cost Hessians and Hessian-vector products.
nanoseconds	time_indices {}	Total time spent determining active indices.
nanoseconds	time_lqr_factor {}	Total time spent performing LQR factorizations.
nanoseconds	time_lqr_solve {}	Total time spent solving the (factorized) LQR problem.
nanoseconds	time_lbfgs_indices {}	Total time spent determining active indices for L-BFGS applications.
nanoseconds	time_lbfgs_apply {}	Total time spent applying L-BFGS estimates.
nanoseconds	time_lbfgs_update {}	Total time spent updating the L-BFGS estimate.
nanoseconds	time_progress_callback {}	Total time spent in the user-provided progress callback.
unsigned	iterations = 0	Total number of inner PANOC iterations.
unsigned	linesearch_failures = 0	Total number of PANOC line search failures.
unsigned	linesearch_backtracks = 0	Total number of PANOC line search backtracking steps.
unsigned	stepsize_backtracks = 0	Total number of PANOC step size reductions.
unsigned	lbfgs_failures = 0	Total number of times that the L-BFGS direction was not finite.
unsigned	lbfgs_rejected = 0	Total number of times that the L-BFGS update was rejected (i.e. it could have resulted in a non-positive definite Hessian estimate).
unsigned	τ_1_accepted = 0	Total number of times that a line search parameter of \( \tau = 1 \) was accepted (i.e. no backtracking necessary).
unsigned	count_τ = 0	The total number of line searches performed (used for computing the average value of \( \tau \)).
real_t	sum_τ = 0	The sum of the line search parameter \( \tau \) in all iterations (used for computing the average value of \( \tau \)).
real_t	final_γ = 0	The final PANOC step size γ.
real_t	final_ψ = 0	Final value of the smooth cost \( \psi(\hat x) \).
real_t	final_h = 0	Final value of the nonsmooth cost \( h(\hat x) \).
real_t	final_φγ = 0	Final value of the forward-backward envelope, \( \varphi_\gamma(x) \) (note that this is in the point \( x \), not \( \hat x \)).

◆ alpaqa::InnerStatsAccumulator< PANOCStats< Conf > >

struct alpaqa::InnerStatsAccumulator< PANOCStats< Conf > >

Collaboration diagram for InnerStatsAccumulator< PANOCStats< Conf > >:

Class Members
nanoseconds	elapsed_time {}	Total elapsed time in the inner solver.
nanoseconds	time_progress_callback {}	Total time spent in the user-provided progress callback.
unsigned	iterations = 0	Total number of inner PANOC iterations.
unsigned	linesearch_failures = 0	Total number of PANOC line search failures.
unsigned	linesearch_backtracks = 0	Total number of PANOC line search backtracking steps.
unsigned	stepsize_backtracks = 0	Total number of PANOC step size reductions.
unsigned	lbfgs_failures = 0	Total number of times that the L-BFGS direction was not finite.
unsigned	lbfgs_rejected = 0	Total number of times that the L-BFGS update was rejected (i.e. it could have resulted in a non-positive definite Hessian estimate).
unsigned	τ_1_accepted = 0	Total number of times that a line search parameter of \( \tau = 1 \) was accepted (i.e. no backtracking necessary).
unsigned	count_τ = 0	The total number of line searches performed (used for computing the average value of \( \tau \)).
real_t	sum_τ = 0	The sum of the line search parameter \( \tau \) in all iterations (used for computing the average value of \( \tau \)).
real_t	final_γ = 0	The final PANOC step size γ.
real_t	final_ψ = 0	Final value of the smooth cost \( \psi(\hat x) \).
real_t	final_h = 0	Final value of the nonsmooth cost \( h(\hat x) \).
real_t	final_φγ = 0	Final value of the forward-backward envelope, \( \varphi_\gamma(x) \) (note that this is in the point \( x \), not \( \hat x \)).

◆ alpaqa::InnerStatsAccumulator< PANTRStats< Conf > >

struct alpaqa::InnerStatsAccumulator< PANTRStats< Conf > >

Collaboration diagram for InnerStatsAccumulator< PANTRStats< Conf > >:

Class Members
nanoseconds	elapsed_time {}	Total elapsed time in the inner solver.
nanoseconds	time_progress_callback {}	Total time spent in the user-provided progress callback.
unsigned	iterations = 0	Total number of inner PANTR iterations.
unsigned	accelerated_step_rejected = 0	Total number of PANTR rejected accelerated steps.
unsigned	stepsize_backtracks = 0	Total number of FB step size reductions.
unsigned	direction_failures = 0	Total number of times that the accelerated direction was not finite.
unsigned	direction_update_rejected = 0	Total number of times that the direction update was rejected (e.g. it could have resulted in a non-positive definite Hessian estimate).
real_t	final_γ = 0	The final FB step size γ.
real_t	final_ψ = 0	Final value of the smooth cost \( \psi(\hat x) \).
real_t	final_h = 0	Final value of the nonsmooth cost \( h(\hat x) \).
real_t	final_φγ = 0	Final value of the forward-backward envelope, \( \varphi_\gamma(x) \) (note that this is in the point \( x \), not \( \hat x \)).

◆ alpaqa::InnerStatsAccumulator< ZeroFPRStats< Conf > >

struct alpaqa::InnerStatsAccumulator< ZeroFPRStats< Conf > >

Collaboration diagram for InnerStatsAccumulator< ZeroFPRStats< Conf > >:

Class Members
nanoseconds	elapsed_time {}	Total elapsed time in the inner solver.
nanoseconds	time_progress_callback {}	Total time spent in the user-provided progress callback.
unsigned	iterations = 0	Total number of inner ZeroFPR iterations.
unsigned	linesearch_failures = 0	Total number of ZeroFPR line search failures.
unsigned	linesearch_backtracks = 0	Total number of ZeroFPR line search backtracking steps.
unsigned	stepsize_backtracks = 0	Total number of ZeroFPR step size reductions.
unsigned	lbfgs_failures = 0	Total number of times that the L-BFGS direction was not finite.
unsigned	lbfgs_rejected = 0	Total number of times that the L-BFGS update was rejected (i.e. it could have resulted in a non-positive definite Hessian estimate).
unsigned	τ_1_accepted = 0	Total number of times that a line search parameter of \( \tau = 1 \) was accepted (i.e. no backtracking necessary).
unsigned	count_τ = 0	The total number of line searches performed (used for computing the average value of \( \tau \)).
real_t	sum_τ = 0	The sum of the line search parameter \( \tau \) in all iterations (used for computing the average value of \( \tau \)).
real_t	final_γ = 0	The final ZeroFPR step size γ.
real_t	final_ψ = 0	Final value of the smooth cost \( \psi(\hat x) \).
real_t	final_h = 0	Final value of the nonsmooth cost \( h(\hat x) \).
real_t	final_φγ = 0	Final value of the forward-backward envelope, \( \varphi_\gamma(x) \) (note that this is in the point \( x \), not \( \hat x \)).

◆ alpaqa::LBFGSDirectionParams

struct alpaqa::LBFGSDirectionParams

Collaboration diagram for LBFGSDirectionParams< Conf >:

Class Members
bool	rescale_on_step_size_changes = false

◆ alpaqa::LBFGSParams

struct alpaqa::LBFGSParams

Collaboration diagram for LBFGSParams< Conf >:

Class Members
length_t	memory = 10	Length of the history to keep.
real_t	min_div_fac = std::numeric_limits<real_t>::epsilon()	Reject update if \( y^\top s \le \text{min_div_fac} \cdot s^\top s \).
real_t	min_abs_s	Reject update if \( s^\top s \le \text{min_abs_s} \).
CBFGSParams< config_t >	cbfgs = {}	Parameters in the cautious BFGS update condition. \[ \frac{y^\top s}{s^\top s} \ge \epsilon \\| g \\|^\alpha \] See also https://epubs.siam.org/doi/10.1137/S1052623499354242
bool	force_pos_def = true	If set to true, the inverse Hessian estimate should remain definite, i.e. a check is performed that rejects the update if \( y^\top s \le \text{min_div_fac} \cdot s^\top s \). If set to false, just try to prevent a singular Hessian by rejecting the update if \( \left\| y^\top s \right\| \le \text{min_div_fac} \cdot s^\top s \).
LBFGSStepSize	stepsize = LBFGSStepSize::BasedOnCurvature	See also LBFGSStepSize

◆ alpaqa::NewtonTRDirectionParams

struct alpaqa::NewtonTRDirectionParams

Collaboration diagram for NewtonTRDirectionParams< Conf >:

Class Members
bool	rescale_on_step_size_changes = false
real_t	hessian_vec_factor = real_t(0.5)
bool	finite_diff = false
real_t	finite_diff_stepsize

◆ alpaqa::OCPDim

struct alpaqa::OCPDim

Collaboration diagram for OCPDim< Conf >:

Class Members
length_t	N
length_t	nx
length_t	nu
length_t	nh
length_t	nc

◆ alpaqa::PANOCOCPParams

struct alpaqa::PANOCOCPParams

Collaboration diagram for PANOCOCPParams< Conf >:

Class Members
LipschitzEstimateParams< config_t >	Lipschitz	Parameters related to the Lipschitz constant estimate and step size.
unsigned	max_iter = 100	Maximum number of inner PANOC iterations.
nanoseconds	max_time = std::chrono::minutes(5)	Maximum duration.
real_t	min_linesearch_coefficient = real_t(1. / 256)	Minimum weight factor between Newton step and projected gradient step, line search parameter.
real_t	linesearch_strictness_factor = real_t(0.95)	Parameter β used in the line search (see Algorithm 2 in [1]). \( 0 < \beta < 1 \)
real_t	L_min = real_t(1e-5)	Minimum Lipschitz constant estimate.
real_t	L_max = real_t(1e20)	Maximum Lipschitz constant estimate.
unsigned	L_max_inc = 16	Maximum number of times to double the Lipschitz constant estimate per iteration.
PANOCStopCrit	stop_crit = PANOCStopCrit::ApproxKKT	What stopping criterion to use.
unsigned	max_no_progress = 10	Maximum number of iterations without any progress before giving up.
unsigned	gn_interval = 1	How often to use a Gauss-Newton step. Zero to disable GN entirely.
bool	gn_sticky = true
bool	reset_lbfgs_on_gn_step = false
bool	lqr_factor_cholesky = true
LBFGSParams< config_t >	lbfgs_params	L-BFGS parameters (e.g. memory).
unsigned	print_interval = 0	When to print progress. If set to zero, nothing will be printed. If set to N != 0, progress is printed every N iterations.
int	print_precision = std::numeric_limits<real_t>::max_digits10 / 2	The precision of the floating point values printed by the solver.
real_t	quadratic_upperbound_tolerance_factor
real_t	linesearch_tolerance_factor
bool	disable_acceleration = false

◆ alpaqa::PANOCOCPStats

struct alpaqa::PANOCOCPStats

Collaboration diagram for PANOCOCPStats< Conf >:

Class Members
SolverStatus	status = SolverStatus::Busy
real_t	ε = inf<config_t>
nanoseconds	elapsed_time {}
nanoseconds	time_prox {}
nanoseconds	time_forward {}
nanoseconds	time_backward {}
nanoseconds	time_jacobians {}
nanoseconds	time_hessians {}
nanoseconds	time_indices {}
nanoseconds	time_lqr_factor {}
nanoseconds	time_lqr_solve {}
nanoseconds	time_lbfgs_indices {}
nanoseconds	time_lbfgs_apply {}
nanoseconds	time_lbfgs_update {}
nanoseconds	time_progress_callback {}
unsigned	iterations = 0
unsigned	linesearch_failures = 0
unsigned	linesearch_backtracks = 0
unsigned	stepsize_backtracks = 0
unsigned	lbfgs_failures = 0
unsigned	lbfgs_rejected = 0
unsigned	τ_1_accepted = 0
unsigned	count_τ = 0
real_t	sum_τ = 0
real_t	final_γ = 0
real_t	final_ψ = 0
real_t	final_h = 0
real_t	final_φγ = 0

◆ alpaqa::PANOCParams

struct alpaqa::PANOCParams

Collaboration diagram for PANOCParams< Conf >:

Class Members
LipschitzEstimateParams< config_t >	Lipschitz	Parameters related to the Lipschitz constant estimate and step size.
unsigned	max_iter = 100	Maximum number of inner PANOC iterations.
nanoseconds	max_time = std::chrono::minutes(5)	Maximum duration.
real_t	min_linesearch_coefficient = real_t(1. / 256)	Minimum weight factor between Newton step and projected gradient step.
bool	force_linesearch = false	Ignore the line search condition and always accept the accelerated step. (For testing purposes only).
real_t	linesearch_strictness_factor = real_t(0.95)	Parameter β used in the line search (see Algorithm 2 in [1]). \( 0 < \beta < 1 \)
real_t	L_min = real_t(1e-5)	Minimum Lipschitz constant estimate.
real_t	L_max = real_t(1e20)	Maximum Lipschitz constant estimate.
PANOCStopCrit	stop_crit = PANOCStopCrit::ApproxKKT	What stopping criterion to use.
unsigned	max_no_progress = 10	Maximum number of iterations without any progress before giving up.
unsigned	print_interval = 0	When to print progress. If set to zero, nothing will be printed. If set to N != 0, progress is printed every N iterations.
int	print_precision = std::numeric_limits<real_t>::max_digits10 / 2	The precision of the floating point values printed by the solver.
real_t	quadratic_upperbound_tolerance_factor
real_t	linesearch_tolerance_factor
bool	update_direction_in_candidate = false
bool	recompute_last_prox_step_after_lbfgs_flush = false
bool	eager_gradient_eval = false	When evaluating ψ(x̂) in a candidate point, always evaluate ∇ψ(x̂) as well. Can be beneficial if computing ∇ψ(x̂) is not much more expensive than computing just ψ(x), and if ∇ψ(x̂) is required in the next iteration (e.g. for the stopping criterion, or when using the NoopDirection).

◆ alpaqa::PANOCProgressInfo

struct alpaqa::PANOCProgressInfo

Collaboration diagram for PANOCProgressInfo< Conf >:

Class Members
unsigned	k
SolverStatus	status
crvec	x
crvec	p
real_t	norm_sq_p
crvec	x̂
real_t	φγ
real_t	ψ
crvec	grad_ψ
real_t	ψ_hat
crvec	grad_ψ_hat
crvec	q
real_t	L
real_t	γ
real_t	τ
real_t	ε
crvec	Σ
crvec	y
unsigned	outer_iter
const TypeErasedProblem< config_t > *	problem
const PANOCParams< config_t > *	params

◆ alpaqa::PANOCStats

struct alpaqa::PANOCStats

Collaboration diagram for PANOCStats< Conf >:

Class Members
SolverStatus	status = SolverStatus::Busy
real_t	ε = inf<config_t>
nanoseconds	elapsed_time {}
nanoseconds	time_progress_callback {}
unsigned	iterations = 0
unsigned	linesearch_failures = 0
unsigned	linesearch_backtracks = 0
unsigned	stepsize_backtracks = 0
unsigned	lbfgs_failures = 0
unsigned	lbfgs_rejected = 0
unsigned	τ_1_accepted = 0
unsigned	count_τ = 0
real_t	sum_τ = 0
real_t	final_γ = 0
real_t	final_ψ = 0
real_t	final_h = 0
real_t	final_φγ = 0

◆ alpaqa::PANTRParams

struct alpaqa::PANTRParams

Collaboration diagram for PANTRParams< Conf >:

Class Members
LipschitzEstimateParams< config_t >	Lipschitz	Parameters related to the Lipschitz constant estimate and step size.
unsigned	max_iter = 100	Maximum number of inner PANTR iterations.
nanoseconds	max_time = std::chrono::minutes(5)	Maximum duration.
real_t	L_min = real_t(1e-5)	Minimum Lipschitz constant estimate.
real_t	L_max = real_t(1e20)	Maximum Lipschitz constant estimate.
PANOCStopCrit	stop_crit = PANOCStopCrit::ApproxKKT	What stopping criterion to use.
unsigned	max_no_progress = 10	Maximum number of iterations without any progress before giving up.
unsigned	print_interval = 0	When to print progress. If set to zero, nothing will be printed. If set to N != 0, progress is printed every N iterations.
int	print_precision = std::numeric_limits<real_t>::max_digits10 / 2	The precision of the floating point values printed by the solver.
real_t	quadratic_upperbound_tolerance_factor
real_t	TR_tolerance_factor = 10 * std::numeric_limits<real_t>::epsilon()
real_t	ratio_threshold_acceptable = real_t(0.2)	Minimal TR ratio to be accepted (successful).
real_t	ratio_threshold_good = real_t(0.8)	Minimal TR ratio to increase radius (very successful).
real_t	radius_factor_rejected = real_t(0.35)	TR radius decrease coefficient when unsuccessful.
real_t	radius_factor_acceptable = real_t(0.999)	TR radius decrease coefficient when successful.
real_t	radius_factor_good = real_t(2.5)	TR radius increase coefficient when very successful.
real_t	initial_radius = NaN<config_t>	Initial trust radius.
real_t	min_radius = 100 * std::numeric_limits<real_t>::epsilon()	Minimum trust radius.
bool	compute_ratio_using_new_stepsize = false	Check the quadratic upperbound and update γ before computing the reduction of the TR step.
bool	update_direction_on_prox_step = true
bool	recompute_last_prox_step_after_direction_reset = false
bool	disable_acceleration = false	Don't compute accelerated steps, fall back to forward-backward splitting. For testing purposes.
bool	ratio_approx_fbe_quadratic_model = true	Compute the trust-region ratio using an approximation of the quadratic model of the FBE, rather than the quadratic model of the subproblem. Specifically, when set to false, the quadratic model used is \[ q(d) = \tfrac12 \inprod{\mathcal R_\gamma(\hat x) d}{d} + \inprod{R_\gamma(\hat x)}{d}. \] When set to true, the quadratic model used is \[ q_\mathrm{approx}(d) = \inv{(1-\alpha)} q(d), \] where \( \alpha = \) LipschitzEstimateParams::Lγ_factor. This is an approximation of the quadratic model of the FBE, \[ q_{\varphi_\gamma}(d) = \tfrac12 \inprod{\mathcal Q_\gamma(\hat x) \mathcal R_\gamma(\hat x) d}{d} + \inprod{\mathcal Q_\gamma(\hat x) R_\gamma(\hat x)}{d}, \] with \( \mathcal Q_\gamma(x) = \Id - \gamma \nabla^2 \psi(x) \).

◆ alpaqa::PANTRProgressInfo

struct alpaqa::PANTRProgressInfo

Collaboration diagram for PANTRProgressInfo< Conf >:

Class Members
unsigned	k
SolverStatus	status
crvec	x
crvec	p
real_t	norm_sq_p
crvec	x̂
real_t	φγ
real_t	ψ
crvec	grad_ψ
real_t	ψ_hat
crvec	grad_ψ_hat
crvec	q
real_t	L
real_t	γ
real_t	Δ
real_t	ρ
real_t	ε
crvec	Σ
crvec	y
unsigned	outer_iter
const TypeErasedProblem< config_t > *	problem
const PANTRParams< config_t > *	params

◆ alpaqa::PANTRStats

struct alpaqa::PANTRStats

Collaboration diagram for PANTRStats< Conf >:

Class Members
SolverStatus	status = SolverStatus::Busy
real_t	ε = inf<config_t>
nanoseconds	elapsed_time {}
nanoseconds	time_progress_callback {}
unsigned	iterations = 0
unsigned	accelerated_step_rejected = 0
unsigned	stepsize_backtracks = 0
unsigned	direction_failures = 0
unsigned	direction_update_rejected = 0
real_t	final_γ = 0
real_t	final_ψ = 0
real_t	final_h = 0
real_t	final_φγ = 0

◆ alpaqa::ScopedMallocChecker

struct alpaqa::ScopedMallocChecker

Collaboration diagram for ScopedMallocChecker< bool >:

◆ alpaqa::SteihaugCGParams

struct alpaqa::SteihaugCGParams

Collaboration diagram for SteihaugCGParams< Conf >:

Class Members
real_t	tol_scale = 1
real_t	tol_scale_root = real_t(0.5)
real_t	tol_max = inf<config_t>
real_t	max_iter_factor = 1

◆ alpaqa::StructuredNewtonDirectionParams

struct alpaqa::StructuredNewtonDirectionParams

Collaboration diagram for StructuredNewtonDirectionParams< Conf >:

Class Members
real_t	hessian_vec_factor = 0	Set this option to a nonzero value to include the Hessian-vector product \( \nabla^2_{x_\mathcal{J}x_\mathcal{K}}\psi(x) q_\mathcal{K} \) from equation 12b in [2], scaled by this parameter. Set it to zero to leave out that term.

◆ alpaqa::StructuredNewtonRegularizationParams

struct alpaqa::StructuredNewtonRegularizationParams

Collaboration diagram for StructuredNewtonRegularizationParams< Conf >:

Class Members
real_t	min_eig = std::cbrt(std::numeric_limits<real_t>::epsilon())	Minimum eigenvalue of the Hessian, scaled by \( 1 + \|\lambda_\mathrm{max}\| \), enforced by regularization using a multiple of identity.
bool	print_eig = false	Print the minimum and maximum eigenvalue of the Hessian.

◆ alpaqa::ZeroFPRParams

struct alpaqa::ZeroFPRParams

Collaboration diagram for ZeroFPRParams< Conf >:

Class Members
LipschitzEstimateParams< config_t >	Lipschitz	Parameters related to the Lipschitz constant estimate and step size.
unsigned	max_iter = 100	Maximum number of inner ZeroFPR iterations.
nanoseconds	max_time = std::chrono::minutes(5)	Maximum duration.
real_t	min_linesearch_coefficient = real_t(1. / 256)	Minimum weight factor between Newton step and projected gradient step.
bool	force_linesearch = false	Ignore the line search condition and always accept the accelerated step. (For testing purposes only).
real_t	linesearch_strictness_factor = real_t(0.95)	Parameter β used in the line search (see Algorithm 2 in [1]). \( 0 < \beta < 1 \)
real_t	L_min = real_t(1e-5)	Minimum Lipschitz constant estimate.
real_t	L_max = real_t(1e20)	Maximum Lipschitz constant estimate.
PANOCStopCrit	stop_crit = PANOCStopCrit::ApproxKKT	What stopping criterion to use.
unsigned	max_no_progress = 10	Maximum number of iterations without any progress before giving up.
unsigned	print_interval = 0	When to print progress. If set to zero, nothing will be printed. If set to N != 0, progress is printed every N iterations.
int	print_precision = std::numeric_limits<real_t>::max_digits10 / 2	The precision of the floating point values printed by the solver.
real_t	quadratic_upperbound_tolerance_factor
real_t	linesearch_tolerance_factor
bool	update_direction_in_candidate = false
bool	recompute_last_prox_step_after_lbfgs_flush = false
bool	update_direction_from_prox_step = false

◆ alpaqa::ZeroFPRProgressInfo

struct alpaqa::ZeroFPRProgressInfo

Collaboration diagram for ZeroFPRProgressInfo< Conf >:

Class Members
unsigned	k
SolverStatus	status
crvec	x
crvec	p
real_t	norm_sq_p
crvec	x̂
real_t	φγ
real_t	ψ
crvec	grad_ψ
real_t	ψ_hat
crvec	grad_ψ_hat
crvec	q
real_t	L
real_t	γ
real_t	τ
real_t	ε
crvec	Σ
crvec	y
unsigned	outer_iter
const TypeErasedProblem< config_t > *	problem
const ZeroFPRParams< config_t > *	params

◆ alpaqa::ZeroFPRStats

struct alpaqa::ZeroFPRStats

Collaboration diagram for ZeroFPRStats< Conf >:

Class Members
SolverStatus	status = SolverStatus::Busy
real_t	ε = inf<config_t>
nanoseconds	elapsed_time {}
nanoseconds	time_progress_callback {}
unsigned	iterations = 0
unsigned	linesearch_failures = 0
unsigned	linesearch_backtracks = 0
unsigned	stepsize_backtracks = 0
unsigned	lbfgs_failures = 0
unsigned	lbfgs_rejected = 0
unsigned	τ_1_accepted = 0
unsigned	count_τ = 0
real_t	sum_τ = 0
real_t	final_γ = 0
real_t	final_ψ = 0
real_t	final_h = 0
real_t	final_φγ = 0

Typedef Documentation

◆ DefaultConfig

using DefaultConfig = EigenConfigd

Definition at line 25 of file config.hpp.

◆ real_t

template<Config Conf = DefaultConfig>

using real_t = typename Conf::real_t

Definition at line 65 of file config.hpp.

◆ vec

template<Config Conf = DefaultConfig>

using vec = typename Conf::vec

Definition at line 66 of file config.hpp.

◆ mvec

template<Config Conf = DefaultConfig>

using mvec = typename Conf::mvec

Definition at line 67 of file config.hpp.

◆ cmvec

template<Config Conf = DefaultConfig>

using cmvec = typename Conf::cmvec

Definition at line 68 of file config.hpp.

◆ rvec

template<Config Conf = DefaultConfig>

using rvec = typename Conf::rvec

Definition at line 69 of file config.hpp.

◆ crvec

template<Config Conf = DefaultConfig>

using crvec = typename Conf::crvec

Definition at line 70 of file config.hpp.

◆ mat

template<Config Conf = DefaultConfig>

using mat = typename Conf::mat

Definition at line 71 of file config.hpp.

◆ mmat

template<Config Conf = DefaultConfig>

using mmat = typename Conf::mmat

Definition at line 72 of file config.hpp.

◆ cmmat

template<Config Conf = DefaultConfig>

using cmmat = typename Conf::cmmat

Definition at line 73 of file config.hpp.

◆ rmat

template<Config Conf = DefaultConfig>

using rmat = typename Conf::rmat

Definition at line 74 of file config.hpp.

◆ crmat

template<Config Conf = DefaultConfig>

using crmat = typename Conf::crmat

Definition at line 75 of file config.hpp.

◆ length_t

template<Config Conf = DefaultConfig>

using length_t = typename Conf::length_t

Definition at line 76 of file config.hpp.

◆ index_t

template<Config Conf = DefaultConfig>

using index_t = typename Conf::index_t

Definition at line 77 of file config.hpp.

◆ indexvec

template<Config Conf = DefaultConfig>

using indexvec = typename Conf::indexvec

Definition at line 78 of file config.hpp.

◆ rindexvec

template<Config Conf = DefaultConfig>

using rindexvec = typename Conf::rindexvec

Definition at line 79 of file config.hpp.

◆ crindexvec

template<Config Conf = DefaultConfig>

using crindexvec = typename Conf::crindexvec

Definition at line 80 of file config.hpp.

◆ mindexvec

template<Config Conf = DefaultConfig>

using mindexvec = typename Conf::mindexvec

Definition at line 81 of file config.hpp.

◆ cmindexvec

template<Config Conf = DefaultConfig>

using cmindexvec = typename Conf::cmindexvec

Definition at line 82 of file config.hpp.

◆ tag_invoke_result

template<typename Tag , typename... Args>

using tag_invoke_result = std::invoke_result<decltype(::alpaqa::alpaqa_tag_invoke), Tag, Args...>

Definition at line 66 of file tag-invoke.hpp.

◆ tag_invoke_result_t

template<typename Tag , typename... Args>

using tag_invoke_result_t = std::invoke_result_t<decltype(::alpaqa::alpaqa_tag_invoke), Tag, Args...>

Definition at line 70 of file tag-invoke.hpp.

◆ tag_t

template<auto & Tag>

using tag_t = std::decay_t<decltype(Tag)>

Definition at line 74 of file tag-invoke.hpp.

◆ function_dict_t

using function_dict_t = alpaqa_function_dict_t

Definition at line 601 of file dl-problem.h.

◆ problem_register_t

using problem_register_t = alpaqa_problem_register_t

Definition at line 602 of file dl-problem.h.

◆ control_problem_register_t

using control_problem_register_t = alpaqa_control_problem_register_t

Definition at line 603 of file dl-problem.h.

◆ problem_functions_t

using problem_functions_t = alpaqa_problem_functions_t

Definition at line 604 of file dl-problem.h.

◆ control_problem_functions_t

using control_problem_functions_t = alpaqa_control_problem_functions_t

Definition at line 605 of file dl-problem.h.

Enumeration Type Documentation

◆ LBFGSStepSize

enum class LBFGSStepSize

strong

Which method to use to select the L-BFGS step size.

Enumerator
BasedOnExternalStepSize	Initial inverse Hessian approximation is set to \( H_0 = \gamma I \).
BasedOnCurvature	Initial inverse Hessian approximation is set to \( H_0 = \frac{s^\top y}{y^\top y} I \).
BasedOnGradientStepSize

Definition at line 25 of file lbfgs.hpp.

◆ PANOCStopCrit

enum class PANOCStopCrit

strong

Enumerator
ApproxKKT	Find an ε-approximate KKT point in the ∞-norm: \[ \varepsilon = \left\\| \gamma_k^{-1} (x^k - \hat x^k) + \nabla \psi(\hat x^k) - \nabla \psi(x^k) \right\\|_\infty \]
ApproxKKT2	Find an ε-approximate KKT point in the 2-norm: \[ \varepsilon = \left\\| \gamma_k^{-1} (x^k - \hat x^k) + \nabla \psi(\hat x^k) - \nabla \psi(x^k) \right\\|_2 \]
ProjGradNorm	∞-norm of the projected gradient with step size γ: \[ \varepsilon = \left\\| x^k - \Pi_C\left(x^k - \gamma_k \nabla \psi(x^k)\right) \right\\|_\infty \]
ProjGradNorm2	2-norm of the projected gradient with step size γ: \[ \varepsilon = \left\\| x^k - \Pi_C\left(x^k - \gamma_k \nabla \psi(x^k)\right) \right\\|_2 \] This is the same criterion as used by OpEn.
ProjGradUnitNorm	∞-norm of the projected gradient with unit step size: \[ \varepsilon = \left\\| x^k - \Pi_C\left(x^k - \nabla \psi(x^k)\right) \right\\|_\infty \]
ProjGradUnitNorm2	2-norm of the projected gradient with unit step size: \[ \varepsilon = \left\\| x^k - \Pi_C\left(x^k - \nabla \psi(x^k)\right) \right\\|_2 \]
FPRNorm	∞-norm of fixed point residual: \[ \varepsilon = \gamma_k^{-1} \left\\| x^k - \Pi_C\left(x^k - \gamma_k \nabla \psi(x^k)\right) \right\\|_\infty \]
FPRNorm2	2-norm of fixed point residual: \[ \varepsilon = \gamma_k^{-1} \left\\| x^k - \Pi_C\left(x^k - \gamma_k \nabla \psi(x^k)\right) \right\\|_2 \]
Ipopt	The stopping criterion used by Ipopt, see https://link.springer.com/article/10.1007/s10107-004-0559-y equation (5). Given a candidate iterate \( \hat x^k \) and the corresponding candidate Lagrange multipliers \( \hat y^k \) for the general constraints \( g(x)\in D \), the multipliers \( w \) for the box constraints \( x\in C \) (that are otherwise not computed explicitly) are given by \[ w^k = v^k - \Pi_C(v^k), \] where \[ \begin{aligned} v^k &\triangleq \hat x^k - \nabla f(\hat x^k) - \nabla g(\hat x^k)\, \hat y^k \\ &= \hat x^k - \nabla \psi(\hat x^k) \end{aligned} \] The quantity that is compared to the (scaled) tolerance is then given by \[ \begin{aligned} \varepsilon' &= \left\\| \nabla f(\hat x^k) + \nabla g(\hat x^k)\, \hat y^k + w^k \right\\|_\infty \\ &= \left\\| \hat x^k - \Pi_C\left(v^k\right) \right\\|_\infty \end{aligned} \] Finally, the quantity is scaled by the factor \[ s_d \triangleq \max\left\{ s_\text{max},\;\frac{\\|\hat y^k\\|_1 + \\|w^k\\|_1}{2m + 2n} \right\} / s_\text{max}, \] i.e. \( \varepsilon = \varepsilon' / s_d \).
LBFGSBpp	The stopping criterion used by LBFGS++, see https://lbfgspp.statr.me/doc/classLBFGSpp_1_1LBFGSBParam.html#afb20e8fd6c6808c1f736218841ba6947. \[ \varepsilon = \frac{\left\\| x^k - \Pi_C\left(x^k - \nabla \psi(x^k)\right) \right\\|_\infty} {\max\left\{1, \\|x\\|_2 \right\}} \]

Definition at line 8 of file panoc-stop-crit.hpp.

◆ SolverStatus

enum class SolverStatus

strong

Exit status of a numerical solver such as ALM or PANOC.

Enumerator
Busy	In progress.
Converged	Converged and reached given tolerance.
MaxTime	Maximum allowed execution time exceeded.
MaxIter	Maximum number of iterations exceeded.
NotFinite	Intermediate results were infinite or not-a-number.
NoProgress	No progress was made in the last iteration.
Interrupted	Solver was interrupted by the user.
Exception	An unexpected exception was thrown.

Definition at line 11 of file solverstatus.hpp.

Function Documentation

◆ minimize_update_anderson()

template<Config Conf = DefaultConfig>

void minimize_update_anderson	(	LimitedMemoryQR< Conf > &	qr,
		rmat< Conf >	G̃,
		crvec< Conf >	rₖ,
		crvec< Conf >	rₗₐₛₜ,
		crvec< Conf >	gₖ,
		real_t< Conf >	min_div_fac,
		rvec< Conf >	γ_LS,
		rvec< Conf >	xₖ_aa
	)

inline

Solve one step of Anderson acceleration to find a fixed point of a function g(x):

\( g(x^\star) - x^\star = 0 \)

Updates the QR factorization of \( \mathcal{R}_k = QR \), solves the least squares problem to find \( \gamma_\text{LS} \), computes the next iterate \( x_{k+1} \), and stores the current function value \( g_k \) in the matrix \( G \), which is used as a circular buffer.

\[ \begin{aligned} \def\gammaLS{\gamma_\text{LS}} m_k &= \min \{k, m\} \\ g_i &= g(x_i) \\ r_i &= r(x_i) g_i - x_i \\ \Delta r_i &= r_i - r_{i-1} \\ \mathcal{R}_k &= \begin{pmatrix} \Delta r_{k-m_k+1} & \dots & \Delta r_k \end{pmatrix} \in \R^{n\times m_k} \\ \gammaLS &= \argmin_{\gamma \in \R^{m_k}}\; \norm{\mathcal{R}_k \gamma - r_k}_2 \\ \alpha_i &= \begin{cases} \gammaLS[0] & i = 0 \\ \gammaLS[i] - \gammaLS[i-1] & 0 \lt i \lt m_k \\ 1 - \gammaLS[m_k - 1] & i = m_k \end{cases} \\ \tilde G_k &= \begin{pmatrix} g_{k - m_k} & \dots & g_{k-1} \end{pmatrix} \\ G_k &= \begin{pmatrix} g_{k - m_k} & \dots & g_{k} \end{pmatrix} \\ &= \begin{pmatrix} \tilde G_k & g_{k} \end{pmatrix} \\ x_{k+1} &= \sum_{i=0}^{m_k} \alpha_i\,g_{k - m_k + i} \\ &= G_k \alpha \\ \end{aligned} \]

Parameters

[in,out]	qr	QR factorization of \( \mathcal{R}_k \)
[in,out]	G̃	Matrix of previous function values \( \tilde G_k \) (stored as ring buffer with the same indices as `qr`)
[in]	rₖ	Current residual \( r_k \)
[in]	rₗₐₛₜ	Previous residual \( r_{k-1} \)
[in]	gₖ	Current function value \( g_k \)
[in]	min_div_fac	Minimum divisor when solving close to singular systems, scaled by the maximum eigenvalue of R
[out]	γ_LS	Solution to the least squares system
[out]	xₖ_aa	Next Anderson iterate

Definition at line 39 of file anderson-helpers.hpp.

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◆ float_to_str_vw_snprintf()

std::string_view float_to_str_vw_snprintf	(	auto &&	print,
		auto &	buf,
		std::floating_point auto	value,
		int	precision,
		const char *	fmt
	)

inline

Definition at line 14 of file print.tpp.

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◆ float_to_str_vw() [1/3]

std::string_view float_to_str_vw	(	auto &	buf,
		double	value,
		int	precision = `std::numeric_limits<double>::max_digits10`
	)

inline

Definition at line 39 of file print.tpp.

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◆ float_to_str_vw() [2/3]

std::string_view float_to_str_vw	(	auto &	buf,
		float	value,
		int	precision = `std::numeric_limits<float>::max_digits10`
	)

inline

Definition at line 45 of file print.tpp.

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◆ float_to_str_vw() [3/3]

std::string_view float_to_str_vw	(	auto &	buf,
		long double	value,
		int	precision = `std::numeric_limits<long double>::max_digits10`
	)

inline

Definition at line 49 of file print.tpp.

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◆ float_to_str()

template<std::floating_point F>

std::string float_to_str	(	F	value,
		int	precision
	)

Examples: C++/Advanced/lasso-fbs.cpp, and C++/CustomControlCppProblem/main.cpp.

Definition at line 67 of file print.tpp.

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◆ print_elem() [1/3]

template<std::floating_point F>

void print_elem	(	auto &	buf,
		F	value,
		std::ostream &	os
	)

Definition at line 73 of file print.tpp.

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◆ print_elem() [2/3]

template<std::integral I>

void print_elem	(	auto &	,
		I	value,
		std::ostream &	os
	)

Definition at line 78 of file print.tpp.

◆ print_elem() [3/3]

template<std::floating_point F>

void print_elem	(	auto &	buf,
		std::complex< F >	value,
		std::ostream &	os
	)

Definition at line 83 of file print.tpp.

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◆ const_or_mut_rvec()

template<Config Conf, VectorRefLike< Conf > V>

constexpr auto const_or_mut_rvec ( V && v )

constexpr

Definition at line 15 of file ocp-vars.hpp.

◆ enum_name()

constexpr const char * enum_name ( PANOCStopCrit s )

inlineconstexpr

Definition at line 105 of file panoc-stop-crit.hpp.

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◆ operator<<() [1/4]

std::ostream & operator<<	(	std::ostream &	os,
		PANOCStopCrit	s
	)

Definition at line 7 of file panoc-stop-crit.cpp.

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◆ operator+=() [1/10]

template<Config Conf>

InnerStatsAccumulator< PANOCOCPStats< Conf > > & operator+=	(	InnerStatsAccumulator< PANOCOCPStats< Conf > > &	acc,
		const PANOCOCPStats< Conf > &	s
	)

Definition at line 255 of file panoc-ocp.hpp.

◆ operator+=() [2/10]

template<Config Conf>

InnerStatsAccumulator< PANOCStats< Conf > > & operator+=	(	InnerStatsAccumulator< PANOCStats< Conf > > &	acc,
		const PANOCStats< Conf > &	s
	)

Definition at line 235 of file panoc.hpp.

◆ operator+=() [3/10]

template<Config Conf>

InnerStatsAccumulator< PANTRStats< Conf > > & operator+=	(	InnerStatsAccumulator< PANTRStats< Conf > > &	acc,
		const PANTRStats< Conf > &	s
	)

Definition at line 244 of file pantr.hpp.

◆ operator+=() [4/10]

template<Config Conf>

InnerStatsAccumulator< ZeroFPRStats< Conf > > & operator+=	(	InnerStatsAccumulator< ZeroFPRStats< Conf > > &	acc,
		const ZeroFPRStats< Conf > &	s
	)

Definition at line 230 of file zerofpr.hpp.

◆ compute_kkt_error()

template<Config Conf>

KKTError< Conf > compute_kkt_error	(	const TypeErasedProblem< Conf > &	problem,
		crvec< Conf >	x,
		crvec< Conf >	y
	)

Definition at line 17 of file kkt-error.hpp.

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◆ operator<<() [2/4]

std::ostream & operator<<	(	std::ostream &	os,
		const OCPEvalCounter &	c
	)

Definition at line 32 of file ocproblem-counters.cpp.

◆ operator+=() [5/10]

OCPEvalCounter::OCPEvalTimer & operator+=	(	OCPEvalCounter::OCPEvalTimer &	a,
		const OCPEvalCounter::OCPEvalTimer &	b
	)

inline

Definition at line 62 of file ocproblem-counters.hpp.

◆ operator+=() [6/10]

OCPEvalCounter & operator+=	(	OCPEvalCounter &	a,
		const OCPEvalCounter &	b
	)

inline

Definition at line 88 of file ocproblem-counters.hpp.

◆ operator+() [1/2]

OCPEvalCounter operator+	(	OCPEvalCounter	a,
		const OCPEvalCounter &	b
	)

inline

Definition at line 114 of file ocproblem-counters.hpp.

◆ check_finiteness() [1/2]

void check_finiteness	(	const auto &	v,
		std::string_view	msg
	)

inline

If the given vector v is not finite, break or throw an exception with the given message msg.

Definition at line 508 of file ocproblem.hpp.

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◆ check_finiteness() [2/2]

void check_finiteness	(	const std::floating_point auto &	v,
		std::string_view	msg
	)

inline

Definition at line 516 of file ocproblem.hpp.

◆ ocproblem_with_counters()

template<class Problem >

auto ocproblem_with_counters ( Problem && p )

Definition at line 648 of file ocproblem.hpp.

◆ ocproblem_with_counters_ref()

template<class Problem >

auto ocproblem_with_counters_ref ( Problem & p )

Definition at line 655 of file ocproblem.hpp.

◆ operator<<() [3/4]

std::ostream & operator<<	(	std::ostream &	os,
		const EvalCounter &	c
	)

Definition at line 32 of file problem-counters.cpp.

◆ operator+=() [7/10]

EvalCounter::EvalTimer & operator+=	(	EvalCounter::EvalTimer &	a,
		const EvalCounter::EvalTimer &	b
	)

inline

Definition at line 62 of file problem-counters.hpp.

◆ operator+=() [8/10]

EvalCounter & operator+=	(	EvalCounter &	a,
		const EvalCounter &	b
	)

inline

Definition at line 88 of file problem-counters.hpp.

◆ operator+() [2/2]

EvalCounter operator+	(	EvalCounter	a,
		const EvalCounter &	b
	)

inline

Definition at line 114 of file problem-counters.hpp.

◆ print_csv()

template<class Derived , class... Args>

std::ostream & print_csv	(	std::ostream &	os,
		const Eigen::DenseBase< Derived > &	M,
		Args &&...	args
	)

Definition at line 67 of file print.hpp.

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◆ print_matlab()

template<class Derived , class... Args>

std::ostream & print_matlab	(	std::ostream &	os,
		const Eigen::DenseBase< Derived > &	M,
		Args &&...	args
	)

Definition at line 68 of file print.hpp.

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◆ print_python()

template<class Derived , class... Args>

std::ostream & print_python	(	std::ostream &	os,
		const Eigen::DenseBase< Derived > &	M,
		Args &&...	args
	)

Examples: C++/Advanced/lasso-fbs.cpp, and C++/CustomControlCppProblem/main.cpp.

Definition at line 69 of file print.hpp.

◆ attach_cancellation()

template<class Solver >

auto attach_cancellation ( Solver & solver )

Attach SIGINT and SIGTERM handlers to stop the given solver.

Parameters

solver The solver that should be stopped by the handler.

Returns: A RAII object that detaches the handler when destroyed.

Definition at line 21 of file cancel.hpp.

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◆ operator<<() [4/4]

std::ostream & operator<<	(	std::ostream &	os,
		SolverStatus	s
	)

Definition at line 7 of file solverstatus.cpp.

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◆ convert_csc()

template<Config Conf>

Sparsity< Conf > convert_csc	(	const auto &	sp,
		sparsity::Symmetry	symmetry
	)

Definition at line 295 of file CasADiProblem.tpp.

◆ register_function() [1/3]

template<class Func >

void register_function	(	function_dict_t *&	extra_functions,
		std::string	name,
		Func &&	func
	)

Make the given function available to alpaqa.

See also: alpaqa::dl::DLProblem::call_extra_func; alpaqa::dl::DLControlProblem::call_extra_func

Definition at line 611 of file dl-problem.h.

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◆ register_function() [2/3]

template<class Func >

void register_function	(	problem_register_t &	result,
		std::string	name,
		Func &&	func
	)

Definition at line 620 of file dl-problem.h.

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◆ register_function() [3/3]

template<class Func >

void register_function	(	control_problem_register_t &	result,
		std::string	name,
		Func &&	func
	)

Definition at line 627 of file dl-problem.h.

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◆ register_member_function()

template<class Result , class T , class Ret , class... Args>

void register_member_function	(	Result &	result,
		std::string	name,
		Ret(T::*)(Args...)	member
	)

Examples: problems/sparse-logistic-regression.cpp.

Definition at line 634 of file dl-problem.h.

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◆ member_caller()

template<auto Member>

static auto member_caller ( )

static

Wrap the given member function or variable into a (possibly generic) lambda function that accepts the instance as a type-erased void pointer.

The signature of the resulting function depends on the signature of the member function:

Ret Class::member(args...) → Ret(void *self, args...)
Ret Class::member(args...) const → Ret(void *self, args...)
Ret Class::member(args...) const → Ret(const void *self, args...)

If the given member is a variable:

Type Class::member → Type &(void *self)
Type Class::member → const Type &(const void *self)

Examples: problems/sparse-logistic-regression.cpp.

Definition at line 704 of file dl-problem.h.

◆ unregister_functions()

void unregister_functions ( function_dict_t *& extra_functions )

inline

Cleans up the extra functions registered by register_function.

Note: This does not need to be called for the functions returned by the registration function, those functions will be cleaned up by alpaqa.; The alpaqa_problem_register_t and alpaqa_control_problem_register_t structs are part of the C API and do not automatically clean up their resources when destroyed, you have to do it manually by calling this function.

Definition at line 715 of file dl-problem.h.

◆ operator+=() [9/10]

InnerStatsAccumulator< lbfgsb::LBFGSBStats > & operator+=	(	InnerStatsAccumulator< lbfgsb::LBFGSBStats > &	acc,
		const lbfgsb::LBFGSBStats &	s
	)

inline

Definition at line 102 of file lbfgsb-adapter.hpp.

◆ operator+=() [10/10]

template<Config Conf>

InnerStatsAccumulator< lbfgspp::LBFGSBStats< Conf > > & operator+=	(	InnerStatsAccumulator< lbfgspp::LBFGSBStats< Conf > > &	acc,
		const lbfgspp::LBFGSBStats< Conf > &	s
	)

Definition at line 102 of file lbfgsb-adapter.hpp.

◆ build_qpalm_problem()

OwningQPALMData build_qpalm_problem ( const TypeErasedProblem< EigenConfigd > & problem )

Definition at line 125 of file qpalm-adapter.cpp.

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Variable Documentation

◆ is_config_v

template<class T >

constexpr bool is_config_v = is_config<T>::value

inlineconstexpr

Definition at line 16 of file config.hpp.

◆ inf

template<Config Conf>

constexpr const auto inf = std::numeric_limits<real_t<Conf>>::infinity()

constexpr

Examples: C++/CasADi/Rosenbrock/main.cpp, C++/CustomCppProblem/main.cpp, C++/DLProblem/main.cpp, C++/FortranProblem/main.cpp, and C++/SimpleUnconstrProblem/main.cpp.

Definition at line 85 of file config.hpp.

◆ NaN

template<Config Conf>

constexpr const auto NaN = std::numeric_limits<real_t<Conf>>::quiet_NaN()

constexpr

Definition at line 87 of file config.hpp.

◆ null_vec

template<Config Conf>

const rvec<Conf> null_vec = mvec<Conf>{nullptr, 0}

inline

Global empty vector for convenience.

Definition at line 152 of file config.hpp.

◆ null_indexvec

template<Config Conf>

const rindexvec<Conf> null_indexvec = mindexvec<Conf>{nullptr, 0}

inline

Global empty index vector for convenience.

Definition at line 155 of file config.hpp.

◆ is_complex_float_v

template<class T >

constexpr bool is_complex_float_v = is_complex_float<T>::value

inlineconstexpr

Definition at line 19 of file float.hpp.

◆ is_tag_invocable_v

template<typename Tag , typename... Args>

constexpr bool is_tag_invocable_v = tag_invocable<Tag, Args...>

inlineconstexpr

Definition at line 59 of file tag-invoke.hpp.

◆ is_nothrow_tag_invocable_v

template<typename Tag , typename... Args>

constexpr bool is_nothrow_tag_invocable_v

inlineconstexpr

Initial value:

=

nothrow_tag_invocable<Tag, Args...>

alpaqa::nothrow_tag_invocable

Definition tag-invoke.hpp:50

Definition at line 62 of file tag-invoke.hpp.

Namespaces

Classes

Concepts

Typedefs

Enumerations

Functions

Variables

Class Documentation

◆ alpaqa::ALMParams

◆ alpaqa::AndersonAccelParams

◆ alpaqa::AndersonDirectionParams

◆ alpaqa::EigenConfig

◆ alpaqa::InnerSolveOptions

◆ alpaqa::InnerStatsAccumulator

◆ alpaqa::InnerStatsAccumulator< lbfgsb::LBFGSBStats >

◆ alpaqa::InnerStatsAccumulator< lbfgspp::LBFGSBStats< Conf > >

◆ alpaqa::InnerStatsAccumulator< PANOCOCPStats< Conf > >

◆ alpaqa::InnerStatsAccumulator< PANOCStats< Conf > >

◆ alpaqa::InnerStatsAccumulator< PANTRStats< Conf > >

◆ alpaqa::InnerStatsAccumulator< ZeroFPRStats< Conf > >

◆ alpaqa::LBFGSDirectionParams

◆ alpaqa::LBFGSParams

◆ alpaqa::NewtonTRDirectionParams

◆ alpaqa::OCPDim

◆ alpaqa::PANOCOCPParams

◆ alpaqa::PANOCOCPStats

◆ alpaqa::PANOCParams

◆ alpaqa::PANOCProgressInfo

◆ alpaqa::PANOCStats

◆ alpaqa::PANTRParams

◆ alpaqa::PANTRProgressInfo

◆ alpaqa::PANTRStats

◆ alpaqa::ScopedMallocChecker

◆ alpaqa::SteihaugCGParams

◆ alpaqa::StructuredNewtonDirectionParams

◆ alpaqa::StructuredNewtonRegularizationParams

◆ alpaqa::ZeroFPRParams

◆ alpaqa::ZeroFPRProgressInfo

◆ alpaqa::ZeroFPRStats

Typedef Documentation

◆ DefaultConfig

◆ real_t

◆ vec

◆ mvec

◆ cmvec

◆ rvec

◆ crvec

◆ mat

◆ mmat

◆ cmmat

◆ rmat

◆ crmat

◆ length_t

◆ index_t

◆ indexvec

◆ rindexvec

◆ crindexvec

◆ mindexvec

◆ cmindexvec

◆ tag_invoke_result

◆ tag_invoke_result_t

◆ tag_t

◆ function_dict_t

◆ problem_register_t

◆ control_problem_register_t

◆ problem_functions_t

◆ control_problem_functions_t

Enumeration Type Documentation

◆ LBFGSStepSize

◆ PANOCStopCrit

◆ SolverStatus

Function Documentation

◆ minimize_update_anderson()

◆ float_to_str_vw_snprintf()

◆ float_to_str_vw() [1/3]

◆ float_to_str_vw() [2/3]

◆ float_to_str_vw() [3/3]

◆ float_to_str()

◆ print_elem() [1/3]

◆ print_elem() [2/3]