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.

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

Create a copy

2. __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.

1. 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

2. 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.

1. 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

2. 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.

1. 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]], x̂: numpy.ndarray[numpy.float64[m, 1], flags.writeable], p: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) -> float

2. 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

class alpaqa._alpaqa.float64.Problem

C++ documentation: alpaqa::TypeErasedProblem

__init__(*args, **kwargs)

Overloaded function.

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

Create a copy

2. __init__(self: alpaqa._alpaqa.float64.Problem, arg0: alpaqa._alpaqa.float64.CasADiProblem) -> None

3. __init__(self: alpaqa._alpaqa.float64.Problem, arg0: object) -> 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(self: alpaqa._alpaqa.float64.Problem, x: numpy.ndarray[numpy.float64[m, 1]], grad_fx: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) → float

eval_g(*args, **kwargs)

Overloaded function.

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

2. 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.

1. 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

2. 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.

1. 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

2. 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.

1. 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

2. 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_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_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.

1. 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

2. 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_proj_diff_g(*args, **kwargs)

Overloaded function.

1. 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

2. 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.

1. 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]], x̂: numpy.ndarray[numpy.float64[m, 1], flags.writeable], p: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) -> float

2. 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.

1. 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

2. 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.

1. 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

2. 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

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_prod(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_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