leap_c.parameters.manager

Classes

AcadosParameterManager

acados parameter management.

Module Contents

class leap_c.parameters.manager.AcadosParameterManager(N_horizon: int, casadi_type: Literal['SX', 'MX'] = 'SX')

acados parameter management.

Handles parameter registration, validation, CasADi symbol creation, and provides default numpy implementations for combining parameters. Framework-specific methods (e.g. combine_differentiable_parameters_torch()) preserve differentiability through the composition.

Stage-varying differentiable parameters (splits are not "global") are implemented via a one-hot indicator vector that is appended to the non-differentiable parameters. At stage k only indicator[k] is 1; get() returns a weighted sum over all stage blocks so the same symbolic expression evaluates to the correct block value at every stage.

Warning

If you forget to set the indicator correctly in combine_non_differentiable_parameters(), every stage will silently evaluate to zero for all stage-varying differentiable parameters.

Variables:

• parameters – Dictionary of parameter names to _AcadosParameter instances.

• N_horizon – The horizon length for the ocp.

• casadi_type – The CasADi symbolic type used for the parameters, either “SX” or “MX”.

• differentiable_default_flat – Differentiable parameters’ default values as a flattened NDArray.

• non_differentiable_default_flat – Non-differentiable parameters’ default values as a flattened NDArray.

• differentiable_symbols – CasADi SX/MX expression for the differentiable parameters.

• non_differentiable_symbols – CasADi SX/MX expression for the non-differentiable parameters.

assign_to_ocp(ocp: acados_template.AcadosOcp) → None

Synchronize the parameter manager’s symbols and defaults onto the OCP.

Sets model.p, model.p_global, parameter_values, and p_global_values on the provided acados OCP instance. Should be called before solver code generation.

Parameters:

ocp – An acados AcadosOcp instance.

combine_differentiable_parameters_jax(batch_size: int | None = None, **overwrites: Any) → Any

Combine differentiable parameters into a flat JAX array, preserving differentiability.

Note

This method is a placeholder. Contributions implementing JAX-native operations (e.g. using jax.numpy) are welcome.

Parameters:

• batch_size – Batch size.

• **overwrites – Named parameter overrides as JAX or numpy arrays.

Returns:

JAX array of shape (batch_size, N_differentiable).

Raises:

• ImportError – If jax is not installed.

• NotImplementedError – Until a JAX implementation is contributed.

combine_differentiable_parameters_torch(batch_size: int | None = None, device: AcadosParameterManager.combine_differentiable_parameters_torch.torch = None, dtype: AcadosParameterManager.combine_differentiable_parameters_torch.torch = None, **overwrites: AcadosParameterManager.combine_differentiable_parameters_torch.torch) → AcadosParameterManager.combine_differentiable_parameters_torch.torch

Combine differentiable parameters into a flat tensor, preserving differentiability.

Uses torch.cat, indexing, and reshaping — all differentiable — so gradients flow back to the original parameter tensors in the overwrites dict.

Parameters:

• batch_size – Batch size. Required.

• device – Target device for the output tensor. Required.

• dtype – Target dtype for the output tensor. Required.

• **overwrites – Named parameter overrides as tensors or numpy arrays.

Returns:

Tensor of shape (batch_size, N_differentiable).

Raises:

ImportError – If torch is not installed.

combine_non_differentiable_parameters(batch_size: int | None = None, **overwrite: numpy.ndarray) → numpy.ndarray

Combine all non-differentiable parameters into a single numpy array.

Parameters:

• batch_size – The batch size for the parameters. Not needed if overwrite is provided.

• **overwrite – Overwrite values for specific parameters. The keys should correspond to the parameter names to overwrite. The values need to be np.ndarray with shape (batch_size, N_horizon, pdim), where pdim is the number of dimensions of the parameter to overwrite. Expects the time dimension to be N_horizon + 1 (N stages 0..N).

Returns:

shape (batch_size, N_horizon + 1, np) with np being the number of parameter_values.

Return type:

np.ndarray

get(name: str) → casadi.SX | casadi.MX | numpy.ndarray

Get the symbolic variable for a parameter.

For stage-varying differentiable parameters (those with splits), the returned expression is a weighted sum over all stage blocks, gated by the indicator vector in the non-differentiable parameters. The expression evaluates to the correct block value at each stage, but only if the indicator is set correctly via combine_non_differentiable_parameters(). If the indicator is all-zero (e.g. the default), every stage silently evaluates to zero for these parameters.

Parameters:

name – The name of the parameter to retrieve.

Returns:

• CasADi SX/MX expression for "differentiable" and "non-differentiable" parameters.

Raises:

ValueError – If name is not registered with this manager.

register_parameter(name: str, default: numpy.ndarray, differentiable: bool = False, splits: leap_c.parameters.utils.ParamSplits = 'global') → casadi.SX | casadi.MX

Register a parameter and return a CasADi symbolic for immediate use.

The returned symbolic is a real CasADi SX (or MX) expression (not a placeholder). It can be used directly in cost, dynamics, and constraint expressions.

Parameters:

• name – The name of the parameter.

• default – The default value(s) for the parameter.

• differentiable – If True, the parameter supports sensitivities (differentiable). If False, the parameter is changeable at runtime but not differentiable (non-differentiable). Defaults to False.

• splits – Defines how the parameter varies across stages. See _AcadosParameter for details. Defaults to "global".

Returns:

A CasADi symbolic expression for the parameter.

N_horizon: int

casadi_type: Literal['SX', 'MX']

property differentiable_default_flat: numpy.ndarray

property differentiable_parameter_names: list[str]

property differentiable_symbols: casadi.SX | casadi.MX

property non_differentiable_default_flat: numpy.ndarray

property non_differentiable_parameter_names: list[str]

property non_differentiable_symbols: casadi.SX | casadi.MX

parameters: dict[str, leap_c.parameters.data._AcadosParameter]