`scipy.integrate._ivp.common:select_initial_step`

source: /scipy/integrate/_ivp/common.py :68

Signature

   def     select_initial_step (    fun    ,    t0    ,    y0    ,    t_bound    ,    max_step    ,    f0    ,    direction    ,    order    ,    rtol    ,    atol      )

Summary

Empirically select a good initial step.

Extended Summary

The algorithm is described in ^[1].

Parameters

fun : callable: Right-hand side of the system.
t0 : float: Initial value of the independent variable.
y0 : ndarray, shape (n,): Initial value of the dependent variable.
t_bound : float: End-point of integration interval; used to ensure that t0+step<=tbound and that fun is only evaluated in the interval [t0,tbound]
max_step : float: Maximum allowable step size.
f0 : ndarray, shape (n,): Initial value of the derivative, i.e., fun(t0, y0).
direction : float: Integration direction.
order : float: Error estimator order. It means that the error controlled by the algorithm is proportional to ``step_size ** (order + 1)`.
rtol : float: Desired relative tolerance.
atol : float: Desired absolute tolerance.

Returns

h_abs : float: Absolute value of the suggested initial step.

Aliases

scipy.integrate._ivp.bdf.select_initial_step