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Solver_QP

Source: Distributed_Design_Optimizer/subsystem/optimization/solver/Solver_QP.py

QP solver implementation for ALADIN controller optimization subproblems.

Classes

Solver_QP

Inherits from: SolverInterface

This class implements the solution of a QP optimization subproblem.

Methods

init(self, maxevals: int | None, constraint_tol: float)

Initialize the QP solver.

Initialize the quantities - P (QP matrix) - q (QP linear term) - A (QP linear equality constraints)

The derived QP is solved via the python package.

Args:

maxevals: Maximum number of function evaluations, or None for unlimited.
constraint_tol: Tolerance for constraint validation.

get_P(self) → np.typing.ArrayLike | None

Returns the QP matrix P.

Returns:

np.typing.ArrayLike | None: The QP matrix P, or None if not set.

set_P(self, P_in: np.typing.ArrayLike) → None

Sets the QP matrix P.

Args:

P_in (np.typing.ArrayLike): The QP matrix P to set.

get_q(self) → np.typing.ArrayLike | None

Returns the QP linear term q.

Returns:

np.typing.ArrayLike | None: The QP linear term q, or None if not set.

set_q(self, q_in: np.typing.ArrayLike) → None

Sets the QP linear term q.

Args:

q_in (np.typing.ArrayLike): The QP linear term q to set.

get_A(self) → np.typing.ArrayLike | None

Returns the QP linear equality constraints A.

Returns:

np.typing.ArrayLike | None: The QP linear equality constraints A, or None if not set.

set_A(self, A_in: np.typing.ArrayLike) → None

Sets the QP linear equality constraints A.

Args:

A_in (np.typing.ArrayLike): The QP linear equality constraints A to set.

get_G(self) → np.typing.ArrayLike | None

Returns the QP linear inequality constraints G.

Returns:

np.typing.ArrayLike | None: The QP linear inequality constraints G, or None if not set.

set_G(self, G_in: np.typing.ArrayLike) → None

Sets the QP linear inequality constraints G.

Args:

G_in (np.typing.ArrayLike): The QP linear inequality constraint matrix G to set.

get_h(self) → np.typing.ArrayLike | None

Returns the QP inequality constraint RHS vector h.

Returns:

np.typing.ArrayLike | None: The RHS vector h for G x <= h, or None if not set.

set_h(self, h_in: np.typing.ArrayLike) → None

Sets the QP inequality constraint RHS vector h.

Args:

h_in (np.typing.ArrayLike): The RHS vector h for G x <= h to set.

execute(self, subsystem: SubSystemInterface) → ControllerOptimData

Execute the QP solver to solve the QP.

Args:

subsystem (SubSystemInterface): The subsystem containing the optimization problem.

Returns:

The optimization results for the ALADIN controller.