leap_c.diff_mpc.utils.create_solver
===================================

.. py:module:: leap_c.diff_mpc.utils.create_solver

.. autoapi-nested-parse::

   Utilities for creating an AcadosOcpBatchSolver from an AcadosOcp object.



Functions
---------

.. autoapisummary::

   leap_c.diff_mpc.utils.create_solver.create_batch_solver
   leap_c.diff_mpc.utils.create_solver.create_forward_backward_batch_solvers
   leap_c.diff_mpc.utils.create_solver.make_ocp_sensitivity_compatible


Module Contents
---------------

.. py:function:: create_batch_solver(ocp: acados_template.AcadosOcp, export_directory: str | pathlib.Path | None = None, discount_factor: float | None = None, n_batch_init: int = 256, num_threads: int = 4, verbose: bool = True) -> acados_template.AcadosOcpBatchSolver

   Create an AcadosOcpBatchSolver from an AcadosOcp object.

   :param ocp: Acados optimal control problem formulation.
   :param export_directory: Directory to export the generated code. If None, a
                            temporary directory is created and the directory is cleaned afterwards.
   :param discount_factor: Discount factor. If None, acados default cost
                           scaling is used, i.e. dt for intermediate stages, 1 for terminal stage.
   :param n_batch_init: Initially supported batch size of the batch OCP solver.
                        Using larger batches will trigger a delay for the creation of more solvers.
   :param num_threads: Number of threads used in the batch solver.
   :param verbose: Whether to print the code generation output.


.. py:function:: create_forward_backward_batch_solvers(ocp: acados_template.AcadosOcp, sensitivity_ocp: acados_template.AcadosOcp | None = None, export_directory: str | pathlib.Path | None = None, discount_factor: float | None = None, n_batch_init: int = 256, num_threads: int = 4, verbose: bool = True) -> tuple[acados_template.AcadosOcpBatchSolver, acados_template.AcadosOcpBatchSolver]

   Create a batch solver for solving the MPC problems (forward solver).

   If this solver is suitable for computing sensitivities, it will also be returned as backward
   solver (the solver for computing sensitivities). Otherwise,
   a second batch solver will be created, which is suitable for computing sensitivities.

   :param ocp: Acados optimal control problem formulation for the forward solver.
   :param sensitivity_ocp: Acados optimal control problem formulation for the backward solver.
                           If None, this will be derived from the given `ocp`.
   :param export_directory: Directory to export generated code. If none,
                            a unique temporary directory is created.
   :param discount_factor: Discount factor for the solver. If not provided,
                           acados default weighting is used
                           (i.e., 1/N_horizon for intermediate stages, 1 for terminal stage).
   :param n_batch_init: Initially supported batch size of the batch OCP solver.
                        Using larger batches will trigger a delay for the creation of more solvers.
   :param num_threads: Number of threads used in the batch solver.
   :param verbose: Whether to print the code generation output.


.. py:function:: make_ocp_sensitivity_compatible(sensitivity_ocp: acados_template.AcadosOcp)

   Make the given ocp compatible with sensitivity computation.