ivp¶

Base class for solving initial value problems (IVPs) of the form:

\[\frac{dy}{dt} = f(t,y),\ y(t_0) = y_0\]

using finite difference methods. The quantecon.ivp class uses various integrators from the scipy.integrate.ode module to perform the integration (i.e., solve the ODE) and parametric B-spline interpolation from scipy.interpolate to approximate the value of the solution between grid points. The quantecon.ivp module also provides a method for computing the residual of the solution which can be used for assessing the overall accuracy of the approximated solution.

class quantecon.ivp.IVP(f, jac=None)[source]¶

Bases: scipy.integrate._ode.ode

Creates an instance of the IVP class.

Parameters:

f : callable f(t, y, *f_args): Right hand side of the system of equations defining the ODE. The independent variable, t, is a scalar; y is an ndarray of dependent variables with y.shape == (n,). The function f should return a scalar, ndarray or list (but not a tuple).
jac : callable jac(t, y, *jac_args), optional(default=None): Jacobian of the right hand side of the system of equations defining the ODE.

Attributes:

y

Methods

`compute_residual`(traj, ti[, k, ext])	The residual is the difference between the derivative of the B-spline approximation of the solution trajectory and the right-hand side of the original ODE evaluated along the approximated solution trajectory.
`get_return_code`()	Extracts the return code for the integration to enable better control if the integration fails.
`integrate`(t[, step, relax])	Find y=y(t), set y as an initial condition, and return y.
`interpolate`(traj, ti[, k, der, ext])	Parametric B-spline interpolation in N-dimensions.
`set_f_params`(*args)	Set extra parameters for user-supplied function f.
`set_initial_value`(y[, t])	Set initial conditions y(t) = y.
`set_integrator`(name, **integrator_params)	Set integrator by name.
`set_jac_params`(*args)	Set extra parameters for user-supplied function jac.
`set_solout`(solout)	Set callable to be called at every successful integration step.
`solve`(t0, y0[, h, T, g, tol, integrator, …])	Solve the IVP by integrating the ODE given some initial condition.
`successful`()	Check if integration was successful.

compute_residual(traj, ti, k=3, ext=2)[source]¶

The residual is the difference between the derivative of the B-spline approximation of the solution trajectory and the right-hand side of the original ODE evaluated along the approximated solution trajectory.

Parameters:

traj : array_like (float): Solution trajectory providing the data points for constructing the B-spline representation.
ti : array_like (float): Array of values for the independent variable at which to interpolate the value of the B-spline.
k : int, optional(default=3): Degree of the desired B-spline. Degree must satisfy \(1 \le k \le 5\).
ext : int, optional(default=2): Controls the value of returned elements for outside the original knot sequence provided by traj. For extrapolation, set ext=0; ext=1 returns zero; ext=2 raises a ValueError.

Returns:

residual : array (float): Difference between the derivative of the B-spline approximation of the solution trajectory and the right-hand side of the ODE evaluated along the approximated solution trajectory.

interpolate(traj, ti, k=3, der=0, ext=2)[source]¶

Parametric B-spline interpolation in N-dimensions.

Parameters:

traj : array_like (float): Solution trajectory providing the data points for constructing the B-spline representation.
ti : array_like (float): Array of values for the independent variable at which to interpolate the value of the B-spline.
k : int, optional(default=3): Degree of the desired B-spline. Degree must satisfy \(1 \le k \le 5\).
der : int, optional(default=0): The order of derivative of the spline to compute (must be less than or equal to k).
ext : int, optional(default=2) Controls the value of returned elements: for outside the original knot sequence provided by traj. For extrapolation, set ext=0; ext=1 returns zero; ext=2 raises a ValueError.

Returns:

interp_traj: ndarray (float): The interpolated trajectory.

solve(t0, y0, h=1.0, T=None, g=None, tol=None, integrator='dopri5', step=False, relax=False, **kwargs)[source]¶

Solve the IVP by integrating the ODE given some initial condition.

Parameters:

t0 : float: Initial condition for the independent variable.
y0 : array_like (float, shape=(n,)): Initial condition for the dependent variables.
h : float, optional(default=1.0): Step-size for computing the solution. Can be positive or negative depending on the desired direction of integration.
T : int, optional(default=None): Terminal value for the independent variable. One of either T or g must be specified.
g : callable g(t, y, f_args), optional(default=None): Provides a stopping condition for the integration. If specified user must also specify a stopping tolerance, tol.
tol : float, optional (default=None): Stopping tolerance for the integration. Only required if g is also specifed.
integrator : str, optional(default=’dopri5’): Must be one of ‘vode’, ‘lsoda’, ‘dopri5’, or ‘dop853’
step : bool, optional(default=False): Allows access to internal steps for those solvers that use adaptive step size routines. Currently only ‘vode’, ‘zvode’, and ‘lsoda’ support step=True.
relax : bool, optional(default=False): Currently only ‘vode’, ‘zvode’, and ‘lsoda’ support relax=True.
**kwargs : dict, optional(default=None): Dictionary of integrator specific keyword arguments. See the Notes section of the docstring for scipy.integrate.ode for a complete description of solver specific keyword arguments.

Returns:

solution: ndarray (float): Simulated solution trajectory.