Problem formulation#

class alpaqa._alpaqa.float64.BoxConstrProblem

C++ documentation: alpaqa::BoxConstrProblem

property C: Box constraints on \(x\)

property D: Box constraints on \(g(x)\)

__init__(*args, **kwargs)

Overloaded function.

__init__(self: alpaqa._alpaqa.float64.BoxConstrProblem, other: alpaqa._alpaqa.float64.BoxConstrProblem) -> None

Create a copy

__init__(self: alpaqa._alpaqa.float64.BoxConstrProblem, n: int, m: int) -> None

Parameters:

n – Number of unknowns
m – Number of constraints

eval_inactive_indices_res_lna(*args, **kwargs)

Overloaded function.

eval_inactive_indices_res_lna(self: alpaqa._alpaqa.float64.BoxConstrProblem, γ: float, x: numpy.ndarray[numpy.float64[m, 1]], grad_ψ: numpy.ndarray[numpy.float64[m, 1]], J: numpy.ndarray[numpy.int64[m, 1], flags.writeable]) -> int
eval_inactive_indices_res_lna(self: alpaqa._alpaqa.float64.BoxConstrProblem, γ: float, x: numpy.ndarray[numpy.float64[m, 1]], grad_ψ: numpy.ndarray[numpy.float64[m, 1]]) -> numpy.ndarray[numpy.int64[m, 1]]

eval_proj_diff_g(*args, **kwargs)

Overloaded function.

eval_proj_diff_g(self: alpaqa._alpaqa.float64.BoxConstrProblem, z: numpy.ndarray[numpy.float64[m, 1]], e: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) -> None
eval_proj_diff_g(self: alpaqa._alpaqa.float64.BoxConstrProblem, z: numpy.ndarray[numpy.float64[m, 1]]) -> numpy.ndarray[numpy.float64[m, 1]]

eval_proj_multipliers(self: alpaqa._alpaqa.float64.BoxConstrProblem, y: numpy.ndarray[numpy.float64[m, 1], flags.writeable], M: float) → None

eval_prox_grad_step(*args, **kwargs)

Overloaded function.

eval_prox_grad_step(self: alpaqa._alpaqa.float64.BoxConstrProblem, γ: float, x: numpy.ndarray[numpy.float64[m, 1]], grad_ψ: numpy.ndarray[numpy.float64[m, 1]], x_hat: numpy.ndarray[numpy.float64[m, 1], flags.writeable], p: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) -> float
eval_prox_grad_step(self: alpaqa._alpaqa.float64.BoxConstrProblem, γ: float, x: numpy.ndarray[numpy.float64[m, 1]], grad_ψ: numpy.ndarray[numpy.float64[m, 1]]) -> Tuple[numpy.ndarray[numpy.float64[m, 1]], numpy.ndarray[numpy.float64[m, 1]], float]

get_box_C(self: alpaqa._alpaqa.float64.BoxConstrProblem) → alpaqa._alpaqa.float64.Box

get_box_D(self: alpaqa._alpaqa.float64.BoxConstrProblem) → alpaqa._alpaqa.float64.Box

property l1_reg: \(\ell_1\) regularization on \(x\)

property m: Number of general constraints, dimension of \(g(x)\)

property n: Number of decision variables, dimension of \(x\)

property penalty_alm_split: Index between quadratic penalty and augmented Lagrangian constraints

resize(self: alpaqa._alpaqa.float64.BoxConstrProblem, n: int, m: int) → None

class alpaqa._alpaqa.float64.Problem

C++ documentation: alpaqa::TypeErasedProblem

__init__(*args, **kwargs)

Overloaded function.

__init__(self: alpaqa._alpaqa.float64.Problem, other: alpaqa._alpaqa.float64.Problem) -> None

Create a copy

__init__(self: alpaqa._alpaqa.float64.Problem, problem: alpaqa._alpaqa.float64.CasADiProblem) -> None

Explicit conversion.

__init__(self: alpaqa._alpaqa.float64.Problem, problem: alpaqa._alpaqa.float64.CUTEstProblem) -> None

Explicit conversion.

__init__(self: alpaqa._alpaqa.float64.Problem, problem: alpaqa._alpaqa.float64.DLProblem) -> None

Explicit conversion.

__init__(self: alpaqa._alpaqa.float64.Problem, problem: object) -> None

Explicit conversion from a custom Python class.

check(self: alpaqa._alpaqa.float64.Problem) → None

eval_f(self: alpaqa._alpaqa.float64.Problem, x: numpy.ndarray[numpy.float64[m, 1]]) → float

eval_f_g(self: alpaqa._alpaqa.float64.Problem, x: numpy.ndarray[numpy.float64[m, 1]], g: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) → float

eval_f_grad_f(*args, **kwargs)

Overloaded function.

eval_f_grad_f(self: alpaqa._alpaqa.float64.Problem, x: numpy.ndarray[numpy.float64[m, 1]], grad_fx: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) -> float
eval_f_grad_f(self: alpaqa._alpaqa.float64.Problem, x: numpy.ndarray[numpy.float64[m, 1]]) -> tuple

eval_g(*args, **kwargs)

Overloaded function.

eval_g(self: alpaqa._alpaqa.float64.Problem, x: numpy.ndarray[numpy.float64[m, 1]], gx: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) -> None
eval_g(self: alpaqa._alpaqa.float64.Problem, x: numpy.ndarray[numpy.float64[m, 1]]) -> numpy.ndarray[numpy.float64[m, 1]]

eval_grad_L(self: alpaqa._alpaqa.float64.Problem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]], grad_L: numpy.ndarray[numpy.float64[m, 1], flags.writeable], work_n: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) → None

eval_grad_f(*args, **kwargs)

Overloaded function.

eval_grad_f(self: alpaqa._alpaqa.float64.Problem, x: numpy.ndarray[numpy.float64[m, 1]], grad_fx: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) -> None
eval_grad_f(self: alpaqa._alpaqa.float64.Problem, x: numpy.ndarray[numpy.float64[m, 1]]) -> numpy.ndarray[numpy.float64[m, 1]]

eval_grad_f_grad_g_prod(self: alpaqa._alpaqa.float64.Problem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]], grad_f: numpy.ndarray[numpy.float64[m, 1], flags.writeable], grad_gxy: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) → None

eval_grad_g_prod(*args, **kwargs)

Overloaded function.

eval_grad_g_prod(self: alpaqa._alpaqa.float64.Problem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]], grad_gxy: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) -> None
eval_grad_g_prod(self: alpaqa._alpaqa.float64.Problem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]]) -> numpy.ndarray[numpy.float64[m, 1]]

eval_grad_gi(self: alpaqa._alpaqa.float64.Problem, x: numpy.ndarray[numpy.float64[m, 1]], i: int, grad_gi: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) → None

eval_grad_ψ(*args, **kwargs)

Overloaded function.

eval_grad_ψ(self: alpaqa._alpaqa.float64.Problem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]], Σ: numpy.ndarray[numpy.float64[m, 1]], grad_ψ: numpy.ndarray[numpy.float64[m, 1], flags.writeable], work_n: numpy.ndarray[numpy.float64[m, 1], flags.writeable], work_m: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) -> None
eval_grad_ψ(self: alpaqa._alpaqa.float64.Problem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]], Σ: numpy.ndarray[numpy.float64[m, 1]]) -> numpy.ndarray[numpy.float64[m, 1]]

eval_hess_L(self: alpaqa._alpaqa.float64.Problem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]], scale: float = 1.0) → Tuple[object, alpaqa._alpaqa.Symmetry]: Returns the Hessian of the Lagrangian and its symmetry.

eval_hess_L_prod(self: alpaqa._alpaqa.float64.Problem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]], scale: float, v: numpy.ndarray[numpy.float64[m, 1]], Hv: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) → None

eval_hess_ψ(self: alpaqa._alpaqa.float64.Problem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]], Σ: numpy.ndarray[numpy.float64[m, 1]], scale: float = 1.0) → Tuple[object, alpaqa._alpaqa.Symmetry]: Returns the Hessian of the augmented Lagrangian and its symmetry.

eval_hess_ψ_prod(self: alpaqa._alpaqa.float64.Problem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]], Σ: numpy.ndarray[numpy.float64[m, 1]], scale: float, v: numpy.ndarray[numpy.float64[m, 1]], Hv: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) → None

eval_inactive_indices_res_lna(*args, **kwargs)

Overloaded function.

eval_inactive_indices_res_lna(self: alpaqa._alpaqa.float64.Problem, γ: float, x: numpy.ndarray[numpy.float64[m, 1]], grad_ψ: numpy.ndarray[numpy.float64[m, 1]], J: numpy.ndarray[numpy.int64[m, 1], flags.writeable]) -> int
eval_inactive_indices_res_lna(self: alpaqa._alpaqa.float64.Problem, γ: float, x: numpy.ndarray[numpy.float64[m, 1]], grad_ψ: numpy.ndarray[numpy.float64[m, 1]]) -> numpy.ndarray[numpy.int64[m, 1]]

eval_jac_g(self: alpaqa._alpaqa.float64.Problem, x: numpy.ndarray[numpy.float64[m, 1]]) → Tuple[object, alpaqa._alpaqa.Symmetry]: Returns the Jacobian of the constraints and its symmetry.

eval_proj_diff_g(*args, **kwargs)

Overloaded function.

eval_proj_diff_g(self: alpaqa._alpaqa.float64.Problem, z: numpy.ndarray[numpy.float64[m, 1]], e: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) -> None
eval_proj_diff_g(self: alpaqa._alpaqa.float64.Problem, z: numpy.ndarray[numpy.float64[m, 1]]) -> numpy.ndarray[numpy.float64[m, 1]]

eval_proj_multipliers(self: alpaqa._alpaqa.float64.Problem, y: numpy.ndarray[numpy.float64[m, 1], flags.writeable], M: float) → None

eval_prox_grad_step(*args, **kwargs)

Overloaded function.

eval_prox_grad_step(self: alpaqa._alpaqa.float64.Problem, γ: float, x: numpy.ndarray[numpy.float64[m, 1]], grad_ψ: numpy.ndarray[numpy.float64[m, 1]], x_hat: numpy.ndarray[numpy.float64[m, 1], flags.writeable], p: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) -> float
eval_prox_grad_step(self: alpaqa._alpaqa.float64.Problem, γ: float, x: numpy.ndarray[numpy.float64[m, 1]], grad_ψ: numpy.ndarray[numpy.float64[m, 1]]) -> Tuple[numpy.ndarray[numpy.float64[m, 1]], numpy.ndarray[numpy.float64[m, 1]], float]

eval_ψ(*args, **kwargs)

Overloaded function.

eval_ψ(self: alpaqa._alpaqa.float64.Problem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]], Σ: numpy.ndarray[numpy.float64[m, 1]], ŷ: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) -> float
eval_ψ(self: alpaqa._alpaqa.float64.Problem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]], Σ: numpy.ndarray[numpy.float64[m, 1]]) -> Tuple[float, numpy.ndarray[numpy.float64[m, 1]]]

eval_ψ_grad_ψ(*args, **kwargs)

Overloaded function.

eval_ψ_grad_ψ(self: alpaqa._alpaqa.float64.Problem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]], Σ: numpy.ndarray[numpy.float64[m, 1]], grad_ψ: numpy.ndarray[numpy.float64[m, 1], flags.writeable], work_n: numpy.ndarray[numpy.float64[m, 1], flags.writeable], work_m: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) -> float
eval_ψ_grad_ψ(self: alpaqa._alpaqa.float64.Problem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]], Σ: numpy.ndarray[numpy.float64[m, 1]]) -> Tuple[float, numpy.ndarray[numpy.float64[m, 1]]]

get_box_C(self: alpaqa._alpaqa.float64.Problem) → alpaqa._alpaqa.float64.Box

get_box_D(self: alpaqa._alpaqa.float64.Problem) → alpaqa._alpaqa.float64.Box

property m: Number of general constraints, dimension of \(g(x)\)

property n: Number of decision variables, dimension of \(x\)

provides_check(self: alpaqa._alpaqa.float64.Problem) → bool

provides_eval_f_g(self: alpaqa._alpaqa.float64.Problem) → bool

provides_eval_f_grad_f(self: alpaqa._alpaqa.float64.Problem) → bool

provides_eval_grad_L(self: alpaqa._alpaqa.float64.Problem) → bool

provides_eval_grad_f_grad_g_prod(self: alpaqa._alpaqa.float64.Problem) → bool

provides_eval_grad_gi(self: alpaqa._alpaqa.float64.Problem) → bool

provides_eval_grad_ψ(self: alpaqa._alpaqa.float64.Problem) → bool

provides_eval_hess_L(self: alpaqa._alpaqa.float64.Problem) → bool

provides_eval_hess_L_prod(self: alpaqa._alpaqa.float64.Problem) → bool

provides_eval_hess_ψ(self: alpaqa._alpaqa.float64.Problem) → bool

provides_eval_hess_ψ_prod(self: alpaqa._alpaqa.float64.Problem) → bool

provides_eval_inactive_indices_res_lna(self: alpaqa._alpaqa.float64.Problem) → bool

provides_eval_jac_g(self: alpaqa._alpaqa.float64.Problem) → bool

provides_eval_ψ(self: alpaqa._alpaqa.float64.Problem) → bool

provides_eval_ψ_grad_ψ(self: alpaqa._alpaqa.float64.Problem) → bool

provides_get_box_C(self: alpaqa._alpaqa.float64.Problem) → bool

provides_get_box_D(self: alpaqa._alpaqa.float64.Problem) → bool

provides_get_hess_L_sparsity(self: alpaqa._alpaqa.float64.Problem) → bool

provides_get_hess_ψ_sparsity(self: alpaqa._alpaqa.float64.Problem) → bool

provides_get_jac_g_sparsity(self: alpaqa._alpaqa.float64.Problem) → bool

class alpaqa._alpaqa.float64.CUTEstProblem

C++ documentation: alpaqa::CUTEstProblem

See alpaqa._alpaqa.float64.Problem for the full documentation.

property C: Box constraints on \(x\)

property D: Box constraints on \(g(x)\)

class Report

class Calls

__init__(*args, **kwargs)

property constraints

property constraints_grad

property constraints_hess

property hessian_times_vector

property objective

property objective_grad

property objective_hess

__init__(*args, **kwargs)

property calls

property time

property time_setup

__init__(*args, **kwargs)

Overloaded function.

__init__(self: alpaqa._alpaqa.float64.CUTEstProblem, so_filename: str, outsdiff_filename: str = None, sparse: bool = False) -> None

Load a CUTEst problem from the given shared library and OUTSDIF.d file

__init__(self: alpaqa._alpaqa.float64.CUTEstProblem, other: alpaqa._alpaqa.float64.CUTEstProblem) -> None

Create a copy

check(self: alpaqa._alpaqa.float64.CUTEstProblem) → None

eval_f(self: alpaqa._alpaqa.float64.CUTEstProblem, x: numpy.ndarray[numpy.float64[m, 1]]) → float

eval_f_g(self: alpaqa._alpaqa.float64.CUTEstProblem, x: numpy.ndarray[numpy.float64[m, 1]], g: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) → float

eval_f_grad_f(*args, **kwargs)

Overloaded function.

eval_f_grad_f(self: alpaqa._alpaqa.float64.CUTEstProblem, x: numpy.ndarray[numpy.float64[m, 1]], grad_fx: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) -> float
eval_f_grad_f(self: alpaqa._alpaqa.float64.CUTEstProblem, x: numpy.ndarray[numpy.float64[m, 1]]) -> tuple

eval_g(*args, **kwargs)

Overloaded function.

eval_g(self: alpaqa._alpaqa.float64.CUTEstProblem, x: numpy.ndarray[numpy.float64[m, 1]], gx: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) -> None
eval_g(self: alpaqa._alpaqa.float64.CUTEstProblem, x: numpy.ndarray[numpy.float64[m, 1]]) -> numpy.ndarray[numpy.float64[m, 1]]

eval_grad_L(self: alpaqa._alpaqa.float64.CUTEstProblem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]], grad_L: numpy.ndarray[numpy.float64[m, 1], flags.writeable], work_n: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) → None

eval_grad_f(*args, **kwargs)

Overloaded function.

eval_grad_f(self: alpaqa._alpaqa.float64.CUTEstProblem, x: numpy.ndarray[numpy.float64[m, 1]], grad_fx: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) -> None
eval_grad_f(self: alpaqa._alpaqa.float64.CUTEstProblem, x: numpy.ndarray[numpy.float64[m, 1]]) -> numpy.ndarray[numpy.float64[m, 1]]

eval_grad_g_prod(*args, **kwargs)

Overloaded function.

eval_grad_g_prod(self: alpaqa._alpaqa.float64.CUTEstProblem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]], grad_gxy: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) -> None
eval_grad_g_prod(self: alpaqa._alpaqa.float64.CUTEstProblem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]]) -> numpy.ndarray[numpy.float64[m, 1]]

eval_grad_gi(self: alpaqa._alpaqa.float64.CUTEstProblem, x: numpy.ndarray[numpy.float64[m, 1]], i: int, grad_gi: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) → None

eval_hess_L(self: alpaqa._alpaqa.float64.CUTEstProblem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]], scale: float = 1.0) → Tuple[object, alpaqa._alpaqa.Symmetry]: Returns the Hessian of the Lagrangian and its symmetry.

eval_hess_L_prod(self: alpaqa._alpaqa.float64.CUTEstProblem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]], scale: float, v: numpy.ndarray[numpy.float64[m, 1]], Hv: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) → None

eval_hess_ψ_prod(self: alpaqa._alpaqa.float64.CUTEstProblem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]], Σ: numpy.ndarray[numpy.float64[m, 1]], scale: float, v: numpy.ndarray[numpy.float64[m, 1]], Hv: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) → None

eval_inactive_indices_res_lna(*args, **kwargs)

Overloaded function.

eval_inactive_indices_res_lna(self: alpaqa._alpaqa.float64.CUTEstProblem, γ: float, x: numpy.ndarray[numpy.float64[m, 1]], grad_ψ: numpy.ndarray[numpy.float64[m, 1]], J: numpy.ndarray[numpy.int64[m, 1], flags.writeable]) -> int
eval_inactive_indices_res_lna(self: alpaqa._alpaqa.float64.CUTEstProblem, γ: float, x: numpy.ndarray[numpy.float64[m, 1]], grad_ψ: numpy.ndarray[numpy.float64[m, 1]]) -> numpy.ndarray[numpy.int64[m, 1]]

eval_jac_g(self: alpaqa._alpaqa.float64.CUTEstProblem, x: numpy.ndarray[numpy.float64[m, 1]]) → Tuple[object, alpaqa._alpaqa.Symmetry]: Returns the Jacobian of the constraints and its symmetry.

eval_proj_diff_g(*args, **kwargs)

Overloaded function.

eval_proj_diff_g(self: alpaqa._alpaqa.float64.CUTEstProblem, z: numpy.ndarray[numpy.float64[m, 1]], e: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) -> None
eval_proj_diff_g(self: alpaqa._alpaqa.float64.CUTEstProblem, z: numpy.ndarray[numpy.float64[m, 1]]) -> numpy.ndarray[numpy.float64[m, 1]]

eval_proj_multipliers(self: alpaqa._alpaqa.float64.CUTEstProblem, y: numpy.ndarray[numpy.float64[m, 1], flags.writeable], M: float) → None

eval_prox_grad_step(*args, **kwargs)

Overloaded function.

eval_prox_grad_step(self: alpaqa._alpaqa.float64.CUTEstProblem, γ: float, x: numpy.ndarray[numpy.float64[m, 1]], grad_ψ: numpy.ndarray[numpy.float64[m, 1]], x_hat: numpy.ndarray[numpy.float64[m, 1], flags.writeable], p: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) -> float
eval_prox_grad_step(self: alpaqa._alpaqa.float64.CUTEstProblem, γ: float, x: numpy.ndarray[numpy.float64[m, 1]], grad_ψ: numpy.ndarray[numpy.float64[m, 1]]) -> Tuple[numpy.ndarray[numpy.float64[m, 1]], numpy.ndarray[numpy.float64[m, 1]], float]

format_report(self: alpaqa._alpaqa.float64.CUTEstProblem, report: alpaqa._alpaqa.float64.CUTEstProblem.Report | None = None) → str: Convert the given report to a string.

get_box_C(self: alpaqa._alpaqa.float64.CUTEstProblem) → alpaqa._alpaqa.float64.Box

get_box_D(self: alpaqa._alpaqa.float64.CUTEstProblem) → alpaqa._alpaqa.float64.Box

get_report(self: alpaqa._alpaqa.float64.CUTEstProblem) → alpaqa._alpaqa.float64.CUTEstProblem.Report: Get the report generated by cutest_creport.

property l1_reg: \(\ell_1\) regularization on \(x\)

property m: Number of general constraints, dimension of \(g(x)\)

property n: Number of decision variables, dimension of \(x\)

property name: CUTEst problem name.

property penalty_alm_split: Index between quadratic penalty and augmented Lagrangian constraints

resize(self: alpaqa._alpaqa.float64.BoxConstrProblem, n: int, m: int) → None

property x0: Initial guess for decision variables.

property y0: Initial guess for multipliers.

class alpaqa._alpaqa.float64.DLProblem

C++ documentation: alpaqa::dl::DLProblem

See alpaqa._alpaqa.float64.Problem for the full documentation.

property C: Box constraints on \(x\)

property D: Box constraints on \(g(x)\)

__init__(*args, **kwargs)

Overloaded function.

__init__(self: alpaqa._alpaqa.float64.DLProblem, so_filename: str, *args, function_name: str = ‘register_alpaqa_problem’, user_param_str: bool = False, **kwargs) -> None

Load a problem from the given shared library file. By default, extra arguments are passed to the problem as a void pointer to a std::any which contains a std::tuple<pybind11::args, pybind11::kwargs>. If the keyword argument user_param_str=True is used, the args is converted to a list of strings, and passed as a void pointer to a std::any containing a std::span<std::string_view>.

__init__(self: alpaqa._alpaqa.float64.DLProblem, other: alpaqa._alpaqa.float64.DLProblem) -> None

Create a copy

call_extra_func(self: alpaqa._alpaqa.float64.DLProblem, name: str, *args, **kwargs) → object: Call the given extra function registered by the problem, with the signature pybind11::object(pybind11::args, pybind11::kwargs).

check(self: alpaqa._alpaqa.float64.DLProblem) → None

eval_f(self: alpaqa._alpaqa.float64.DLProblem, x: numpy.ndarray[numpy.float64[m, 1]]) → float

eval_f_g(self: alpaqa._alpaqa.float64.DLProblem, x: numpy.ndarray[numpy.float64[m, 1]], g: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) → float

eval_f_grad_f(*args, **kwargs)

Overloaded function.

eval_f_grad_f(self: alpaqa._alpaqa.float64.DLProblem, x: numpy.ndarray[numpy.float64[m, 1]], grad_fx: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) -> float
eval_f_grad_f(self: alpaqa._alpaqa.float64.DLProblem, x: numpy.ndarray[numpy.float64[m, 1]]) -> tuple

eval_g(*args, **kwargs)

Overloaded function.

eval_g(self: alpaqa._alpaqa.float64.DLProblem, x: numpy.ndarray[numpy.float64[m, 1]], gx: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) -> None
eval_g(self: alpaqa._alpaqa.float64.DLProblem, x: numpy.ndarray[numpy.float64[m, 1]]) -> numpy.ndarray[numpy.float64[m, 1]]

eval_grad_L(self: alpaqa._alpaqa.float64.DLProblem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]], grad_L: numpy.ndarray[numpy.float64[m, 1], flags.writeable], work_n: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) → None

eval_grad_f(*args, **kwargs)

Overloaded function.

eval_grad_f(self: alpaqa._alpaqa.float64.DLProblem, x: numpy.ndarray[numpy.float64[m, 1]], grad_fx: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) -> None
eval_grad_f(self: alpaqa._alpaqa.float64.DLProblem, x: numpy.ndarray[numpy.float64[m, 1]]) -> numpy.ndarray[numpy.float64[m, 1]]

eval_grad_f_grad_g_prod(self: alpaqa._alpaqa.float64.DLProblem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]], grad_f: numpy.ndarray[numpy.float64[m, 1], flags.writeable], grad_gxy: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) → None

eval_grad_g_prod(*args, **kwargs)

Overloaded function.

eval_grad_g_prod(self: alpaqa._alpaqa.float64.DLProblem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]], grad_gxy: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) -> None
eval_grad_g_prod(self: alpaqa._alpaqa.float64.DLProblem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]]) -> numpy.ndarray[numpy.float64[m, 1]]

eval_grad_gi(self: alpaqa._alpaqa.float64.DLProblem, x: numpy.ndarray[numpy.float64[m, 1]], i: int, grad_gi: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) → None

eval_grad_ψ(*args, **kwargs)

Overloaded function.

eval_grad_ψ(self: alpaqa._alpaqa.float64.DLProblem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]], Σ: numpy.ndarray[numpy.float64[m, 1]], grad_ψ: numpy.ndarray[numpy.float64[m, 1], flags.writeable], work_n: numpy.ndarray[numpy.float64[m, 1], flags.writeable], work_m: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) -> None
eval_grad_ψ(self: alpaqa._alpaqa.float64.DLProblem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]], Σ: numpy.ndarray[numpy.float64[m, 1]]) -> numpy.ndarray[numpy.float64[m, 1]]

eval_hess_L(self: alpaqa._alpaqa.float64.DLProblem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]], scale: float = 1.0) → Tuple[object, alpaqa._alpaqa.Symmetry]: Returns the Hessian of the Lagrangian and its symmetry.

eval_hess_L_prod(self: alpaqa._alpaqa.float64.DLProblem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]], scale: float, v: numpy.ndarray[numpy.float64[m, 1]], Hv: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) → None

eval_hess_ψ(self: alpaqa._alpaqa.float64.DLProblem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]], Σ: numpy.ndarray[numpy.float64[m, 1]], scale: float = 1.0) → Tuple[object, alpaqa._alpaqa.Symmetry]: Returns the Hessian of the augmented Lagrangian and its symmetry.

eval_hess_ψ_prod(self: alpaqa._alpaqa.float64.DLProblem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]], Σ: numpy.ndarray[numpy.float64[m, 1]], scale: float, v: numpy.ndarray[numpy.float64[m, 1]], Hv: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) → None

eval_inactive_indices_res_lna(*args, **kwargs)

Overloaded function.

eval_inactive_indices_res_lna(self: alpaqa._alpaqa.float64.DLProblem, γ: float, x: numpy.ndarray[numpy.float64[m, 1]], grad_ψ: numpy.ndarray[numpy.float64[m, 1]], J: numpy.ndarray[numpy.int64[m, 1], flags.writeable]) -> int
eval_inactive_indices_res_lna(self: alpaqa._alpaqa.float64.DLProblem, γ: float, x: numpy.ndarray[numpy.float64[m, 1]], grad_ψ: numpy.ndarray[numpy.float64[m, 1]]) -> numpy.ndarray[numpy.int64[m, 1]]

eval_jac_g(self: alpaqa._alpaqa.float64.DLProblem, x: numpy.ndarray[numpy.float64[m, 1]]) → Tuple[object, alpaqa._alpaqa.Symmetry]: Returns the Jacobian of the constraints and its symmetry.

eval_proj_diff_g(*args, **kwargs)

Overloaded function.

eval_proj_diff_g(self: alpaqa._alpaqa.float64.DLProblem, z: numpy.ndarray[numpy.float64[m, 1]], e: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) -> None
eval_proj_diff_g(self: alpaqa._alpaqa.float64.DLProblem, z: numpy.ndarray[numpy.float64[m, 1]]) -> numpy.ndarray[numpy.float64[m, 1]]

eval_proj_multipliers(self: alpaqa._alpaqa.float64.DLProblem, y: numpy.ndarray[numpy.float64[m, 1], flags.writeable], M: float) → None

eval_prox_grad_step(*args, **kwargs)

Overloaded function.

eval_prox_grad_step(self: alpaqa._alpaqa.float64.DLProblem, γ: float, x: numpy.ndarray[numpy.float64[m, 1]], grad_ψ: numpy.ndarray[numpy.float64[m, 1]], x_hat: numpy.ndarray[numpy.float64[m, 1], flags.writeable], p: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) -> float
eval_prox_grad_step(self: alpaqa._alpaqa.float64.DLProblem, γ: float, x: numpy.ndarray[numpy.float64[m, 1]], grad_ψ: numpy.ndarray[numpy.float64[m, 1]]) -> Tuple[numpy.ndarray[numpy.float64[m, 1]], numpy.ndarray[numpy.float64[m, 1]], float]

eval_ψ(*args, **kwargs)

Overloaded function.

eval_ψ(self: alpaqa._alpaqa.float64.DLProblem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]], Σ: numpy.ndarray[numpy.float64[m, 1]], ŷ: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) -> float
eval_ψ(self: alpaqa._alpaqa.float64.DLProblem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]], Σ: numpy.ndarray[numpy.float64[m, 1]]) -> Tuple[float, numpy.ndarray[numpy.float64[m, 1]]]

eval_ψ_grad_ψ(*args, **kwargs)

Overloaded function.

eval_ψ_grad_ψ(self: alpaqa._alpaqa.float64.DLProblem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]], Σ: numpy.ndarray[numpy.float64[m, 1]], grad_ψ: numpy.ndarray[numpy.float64[m, 1], flags.writeable], work_n: numpy.ndarray[numpy.float64[m, 1], flags.writeable], work_m: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) -> float
eval_ψ_grad_ψ(self: alpaqa._alpaqa.float64.DLProblem, x: numpy.ndarray[numpy.float64[m, 1]], y: numpy.ndarray[numpy.float64[m, 1]], Σ: numpy.ndarray[numpy.float64[m, 1]]) -> Tuple[float, numpy.ndarray[numpy.float64[m, 1]]]

get_box_C(self: alpaqa._alpaqa.float64.DLProblem) → alpaqa._alpaqa.float64.Box

get_box_D(self: alpaqa._alpaqa.float64.DLProblem) → alpaqa._alpaqa.float64.Box

property l1_reg: \(\ell_1\) regularization on \(x\)

property m: Number of general constraints, dimension of \(g(x)\)

property n: Number of decision variables, dimension of \(x\)

property penalty_alm_split: Index between quadratic penalty and augmented Lagrangian constraints

provides_eval_f_g(self: alpaqa._alpaqa.float64.DLProblem) → bool

provides_eval_f_grad_f(self: alpaqa._alpaqa.float64.DLProblem) → bool

provides_eval_grad_L(self: alpaqa._alpaqa.float64.DLProblem) → bool

provides_eval_grad_f_grad_g_prod(self: alpaqa._alpaqa.float64.DLProblem) → bool

provides_eval_grad_gi(self: alpaqa._alpaqa.float64.DLProblem) → bool

provides_eval_grad_ψ(self: alpaqa._alpaqa.float64.DLProblem) → bool

provides_eval_hess_L(self: alpaqa._alpaqa.float64.DLProblem) → bool

provides_eval_hess_L_prod(self: alpaqa._alpaqa.float64.DLProblem) → bool

provides_eval_hess_ψ(self: alpaqa._alpaqa.float64.DLProblem) → bool

provides_eval_hess_ψ_prod(self: alpaqa._alpaqa.float64.DLProblem) → bool

provides_eval_jac_g(self: alpaqa._alpaqa.float64.DLProblem) → bool

provides_eval_ψ(self: alpaqa._alpaqa.float64.DLProblem) → bool

provides_eval_ψ_grad_ψ(self: alpaqa._alpaqa.float64.DLProblem) → bool

provides_get_box_C(self: alpaqa._alpaqa.float64.DLProblem) → bool

provides_get_hess_L_sparsity(self: alpaqa._alpaqa.float64.DLProblem) → bool

provides_get_hess_ψ_sparsity(self: alpaqa._alpaqa.float64.DLProblem) → bool

provides_get_jac_g_sparsity(self: alpaqa._alpaqa.float64.DLProblem) → bool

resize(self: alpaqa._alpaqa.float64.BoxConstrProblem, n: int, m: int) → None