TrajOptProblem

openscvx.trajoptproblem.TrajOptProblem.__init__(dynamics: Dynamics, constraints: List[Union[CTCSConstraint, NodalConstraint]], x: State, u: Control, N: int, idx_time: int, params: Optional[dict] = None, dynamics_prop: Optional[callable] = None, x_prop: State = None, scp: Optional[ScpConfig] = None, dis: Optional[DiscretizationConfig] = None, prp: Optional[PropagationConfig] = None, sim: Optional[SimConfig] = None, dev: Optional[DevConfig] = None, cvx: Optional[ConvexSolverConfig] = None, licq_min=0.0, licq_max=0.0001, time_dilation_factor_min=0.3, time_dilation_factor_max=3.0)

The primary class in charge of compiling and exporting the solvers

Parameters:

Name	Type	Description	Default
dynamics	Dynamics	Dynamics function decorated with @dynamics	required
constraints	List[Union[CTCSConstraint, NodalConstraint]]	List of constraints decorated with @ctcs or @nodal	required
idx_time	int	Index of the time variable in the state vector	required
N	int	Number of segments in the trajectory	required
time_init	float	Initial time for the trajectory	required
x_guess	ndarray	Initial guess for the state trajectory	required
u_guess	ndarray	Initial guess for the control trajectory	required
initial_state	BoundaryConstraint	Initial state constraint	required
final_state	BoundaryConstraint	Final state constraint	required
x_max	ndarray	Upper bound on the state variables	required
x_min	ndarray	Lower bound on the state variables	required
u_max	ndarray	Upper bound on the control variables	required
u_min	ndarray	Lower bound on the control variables	required
dynamics_prop	Optional[callable]	Propagation dynamics function decorated with @dynamics	None
initial_state_prop		Propagation initial state constraint	required
scp	Optional[ScpConfig]	SCP configuration object	None
dis	Optional[DiscretizationConfig]	Discretization configuration object	None
prp	Optional[PropagationConfig]	Propagation configuration object	None
sim	Optional[SimConfig]	Simulation configuration object	None
dev	Optional[DevConfig]	Development configuration object	None
cvx	Optional[ConvexSolverConfig]	Convex solver configuration object	None

Returns:

Type	Description
	None

ScpConfig

openscvx.config.ScpConfig.__init__(n: Optional[int] = None, k_max: int = 200, w_tr: float = 1.0, lam_vc: float = 1.0, ep_tr: float = 0.0001, ep_vb: float = 0.0001, ep_vc: float = 1e-08, lam_cost: float = 0.0, lam_vb: float = 0.0, uniform_time_grid: bool = False, cost_drop: int = -1, cost_relax: float = 1.0, w_tr_adapt: float = 1.0, w_tr_max: Optional[float] = None, w_tr_max_scaling_factor: Optional[float] = None)

Configuration class for Sequential Convex Programming (SCP).

This class defines the parameters used to configure the SCP solver. You will very likely need to modify the weights for your problem. Please refer to my guide here for more information.

Attributes:

Name	Type	Description
n	int	The number of discretization nodes. Defaults to None.
k_max	int	The maximum number of SCP iterations. Defaults to 200.
w_tr	float	The trust region weight. Defaults to 1.0.
lam_vc	float	The penalty weight for virtual control. Defaults to 1.0.
ep_tr	float	The trust region convergence tolerance. Defaults to 1e-4.
ep_vb	float	The boundary constraint convergence tolerance. Defaults to 1e-4.
ep_vc	float	The virtual constraint convergence tolerance. Defaults to 1e-8.
lam_cost	float	The weight for original cost. Defaults to 0.0.
lam_vb	float	The weight for virtual buffer. This is only used if there are nonconvex nodal constraints present. Defaults to 0.0.
uniform_time_grid	bool	Whether to use a uniform time grid. Defaults to False.
cost_drop	int	The number of iterations to allow for cost stagnation before termination. Defaults to -1 (disabled).
cost_relax	float	The relaxation factor for cost reduction. Defaults to 1.0.
w_tr_adapt	float	The adaptation factor for the trust region weight. Defaults to 1.0.
w_tr_max	float	The maximum allowable trust region weight. Defaults to None.
w_tr_max_scaling_factor	float	The scaling factor for the maximum trust region weight. Defaults to None.

DiscretizationConfig

openscvx.config.DiscretizationConfig.__init__(dis_type: str = 'FOH', custom_integrator: bool = False, solver: str = 'Tsit5', args: Optional[dict] = None, atol: float = 0.001, rtol: float = 1e-06)

Configuration class for discretization settings.

This class defines the parameters required for discretizing system dynamics.

Main arguments: These are the arguments most commonly used day-to-day.

Parameters:

Name	Type	Description	Default
dis_type	str	The type of discretization to use (e.g., "FOH" for First-Order Hold). Defaults to "FOH".	'FOH'
custom_integrator	bool	This enables our custom fixed-step RK45 algorithm. This tends to be faster than Diffrax but unless you're going for speed, it's recommended to stick with Diffrax for robustness and other solver options. Defaults to False.	False
solver	str	Not used if custom_integrator is enabled. Any choice of solver in Diffrax is valid, please refer here, How to Choose a Solver. Defaults to "Tsit5".	'Tsit5'

Other arguments: These arguments are less frequently used, and for most purposes you shouldn't need to understand these.

Parameters:

Name	Type	Description	Default
args	Dict	Additional arguments to pass to the solver which can be found here. Defaults to an empty dictionary.	None
atol	float	Absolute tolerance for the solver. Defaults to 1e-3.	0.001
rtol	float	Relative tolerance for the solver. Defaults to 1e-6.	1e-06

PropagationConfig

openscvx.config.PropagationConfig.__init__(inter_sample: int = 30, dt: float = 0.01, solver: str = 'Dopri8', max_tau_len: int = 1000, args: Optional[dict] = None, atol: float = 0.001, rtol: float = 1e-06)

Configuration class for propagation settings.

This class defines the parameters required for propagating the nonlinear system dynamics using the optimal control sequence.

Main arguments: These are the arguments most commonly used day-to-day.

Other arguments: The solver should likely not be changed as it is a high accuracy 8th-order Runge-Kutta method.

Parameters:

Name	Type	Description	Default
inter_sample	int	How dense the propagation within multishot discretization should be. Defaults to 30.	30
dt	float	The time step for propagation. Defaults to 0.1.	0.01
solver	str	The numerical solver to use for propagation (e.g., "Dopri8"). Defaults to "Dopri8".	'Dopri8'
max_tau_len	int	The maximum length of the time vector for propagation. Defaults to 1000.	1000
args	Dict	Additional arguments to pass to the solver. Defaults to an empty dictionary.	None
atol	float	Absolute tolerance for the solver. Defaults to 1e-3.	0.001
rtol	float	Relative tolerance for the solver. Defaults to 1e-6.	1e-06

SimConfig

openscvx.config.SimConfig.__init__(x: State, x_prop: State, u: Control, total_time: float, idx_x_true: slice, idx_x_true_prop: slice, idx_u_true: slice, idx_t: slice, idx_y: slice, idx_y_prop: slice, idx_s: slice, save_compiled: bool = True, ctcs_node_intervals: Optional[list] = None, constraints_ctcs: Optional[list[Callable]] = None, constraints_nodal: Optional[list[Callable]] = None, n_states: Optional[int] = None, n_states_prop: Optional[int] = None, n_controls: Optional[int] = None, scaling_x_overrides: Optional[list] = None, scaling_u_overrides: Optional[list] = None)

Configuration class for simulation settings.

This class defines the parameters required for simulating a trajectory optimization problem.

Main arguments: These are the arguments most commonly used day-to-day.

Parameters:

Name	Type	Description	Default
x	State	State object, must have .min and .max attributes for bounds.	required
x_prop	State	Propagation state object, must have .min and .max attributes for bounds.	required
u	Control	Control object, must have .min and .max attributes for bounds.	required
total_time	float	The total simulation time.	required
idx_x_true	slice	Slice for true state indices.	required
idx_x_true_prop	slice	Slice for true propagation state indices.	required
idx_u_true	slice	Slice for true control indices.	required
idx_t	slice	Slice for time index.	required
idx_y	slice	Slice for constraint violation indices.	required
idx_y_prop	slice	Slice for propagation constraint violation indices.	required
idx_s	slice	Slice for time dilation index.	required
save_compiled	bool	If True, save and reuse compiled solver functions. Defaults to True.	True
ctcs_node_intervals	list	Node intervals for CTCS constraints.	None
constraints_ctcs	list	List of CTCS constraints.	None
constraints_nodal	list	List of nodal constraints.	None
n_states	int	The number of state variables. Defaults to None (inferred from x.max).	None
n_states_prop	int	The number of propagation state variables. Defaults to None (inferred from x_prop.max).	None
n_controls	int	The number of control variables. Defaults to None (inferred from u.max).	None
scaling_x_overrides	list	List of (upper_bound, lower_bound, idx) for custom state scaling. Each can be scalar or array, idx can be int, list, or slice.	None
scaling_u_overrides	list	List of (upper_bound, lower_bound, idx) for custom control scaling. Each can be scalar or array, idx can be int, list, or slice.	None

Note

You can specify custom scaling for specific states/controls using scaling_x_overrides and scaling_u_overrides. Any indices not covered by overrides will use the default min/max bounds.

ConvexSolverConfig

openscvx.config.ConvexSolverConfig.__init__(solver: str = 'QOCO', solver_args: Optional[dict] = None, cvxpygen: bool = False, cvxpygen_override: bool = False)

Configuration class for convex solver settings.

This class defines the parameters required for configuring a convex solver.

These are the arguments most commonly used day-to-day. Generally I have found QOCO to be the most performant of the CVXPY solvers for these types of problems (I do have a bias as the author is from my group) and can handle up to SOCP's. CLARABEL is also a great option with feasibility checking and can handle a few more problem types. CVXPYGen is also great if your problem isn't too large. I have found qocogen to be the most performant of the CVXPYGen solvers.

Parameters:

Name	Type	Description	Default
solver	str	The name of the CVXPY solver to use. A list of options can be found here. Defaults to "QOCO".	'QOCO'
solver_args	dict	Ensure you are using the correct arguments for your solver as they are not all common. Additional arguments to configure the solver, such as tolerances. Defaults to {"abstol": 1e-6, "reltol": 1e-9}.	None
cvxpygen	bool	Whether to enable CVXPY code generation for the solver. Defaults to False.	False

DevConfig

openscvx.config.DevConfig.__init__(profiling: bool = False, debug: bool = False, printing: bool = True)

Configuration class for development settings.

This class defines the parameters used for development and debugging purposes.

Main arguments: These are the arguments most commonly used day-to-day.

Parameters:

Name	Type	Description	Default
profiling	bool	Whether to enable profiling for performance analysis. Defaults to False.	False
debug	bool	Disables all precompilation so you can place breakpoints and inspect values. Defaults to False.	False
printing	bool	Whether to enable printing during development. Defaults to True.	True