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LoweredProblem dataclass - container for all lowering outputs.

LoweredProblem dataclass

Container for all outputs from symbolic problem lowering.

This dataclass holds all the results of lowering symbolic expressions to executable JAX and CVXPy code. It provides a clean, typed interface for accessing the various components needed for optimization.

Note

CVXPy optimization variables (ocp_vars) are owned by the solver, not stored here. Access them via solver.ocp_vars.

Attributes:

Name Type Description
dynamics Dynamics

Optimization dynamics with fields f, A, B (JAX functions)
dynamics_prop Dynamics

Propagation dynamics with fields f, A, B
jax_constraints LoweredJaxConstraints

Non-convex constraints lowered to JAX with gradients
cvxpy_constraints LoweredCvxpyConstraints

Convex constraints lowered to CVXPy
x_unified UnifiedState

Aggregated optimization state interface
u_unified UnifiedControl

Aggregated optimization control interface
x_prop_unified UnifiedState

Aggregated propagation state interface
cvxpy_params Dict[str, Parameter]

Dict mapping user parameter names to CVXPy Parameter objects
algebraic_prop Optional[Dict[str, Callable]]

Dict mapping output names to vmapped JAX functions (evaluated, not integrated)
Example

After lowering a symbolic problem::

solver = CVXPySolver()
lowered = lower_symbolic_problem(problem, solver, settings)

# Access components
dx_dt = lowered.dynamics.f(x, u, node, params)
jacobian_A = lowered.dynamics.A(x, u, node, params)

# Solver owns CVXPy variables
ocp_vars = solver.ocp_vars
Source code in openscvx/lowered/problem.py 
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@dataclass
class LoweredProblem:
    """Container for all outputs from symbolic problem lowering.

    This dataclass holds all the results of lowering symbolic expressions
    to executable JAX and CVXPy code. It provides a clean, typed interface
    for accessing the various components needed for optimization.

    Note:
        CVXPy optimization variables (``ocp_vars``) are owned by the solver,
        not stored here. Access them via ``solver.ocp_vars``.

    Attributes:
        dynamics: Optimization dynamics with fields f, A, B (JAX functions)
        dynamics_prop: Propagation dynamics with fields f, A, B
        jax_constraints: Non-convex constraints lowered to JAX with gradients
        cvxpy_constraints: Convex constraints lowered to CVXPy
        x_unified: Aggregated optimization state interface
        u_unified: Aggregated optimization control interface
        x_prop_unified: Aggregated propagation state interface
        cvxpy_params: Dict mapping user parameter names to CVXPy Parameter objects
        algebraic_prop: Dict mapping output names to vmapped JAX functions
            (evaluated, not integrated)

    Example:
        After lowering a symbolic problem::

            solver = CVXPySolver()
            lowered = lower_symbolic_problem(problem, solver, settings)

            # Access components
            dx_dt = lowered.dynamics.f(x, u, node, params)
            jacobian_A = lowered.dynamics.A(x, u, node, params)

            # Solver owns CVXPy variables
            ocp_vars = solver.ocp_vars
    """

    # JAX dynamics
    dynamics: Dynamics
    dynamics_prop: Dynamics

    # Lowered constraints (separate types for JAX vs CVXPy)
    jax_constraints: LoweredJaxConstraints
    cvxpy_constraints: LoweredCvxpyConstraints

    # Unified interfaces
    x_unified: UnifiedState
    u_unified: UnifiedControl
    x_prop_unified: UnifiedState

    # CVXPy constraint parameters (user-defined parameters lowered to CVXPy)
    cvxpy_params: Dict[str, "cp.Parameter"]

    # Algebraic outputs (vmapped JAX functions for propagation)
    algebraic_prop: Optional[Dict[str, Callable]] = field(default_factory=dict)