LSODA(fun, t0, y0, t_bound, first_step=None, min_step=0.0, max_step=inf, rtol=0.001, atol=1e-06, jac=None, lband=None, uband=None, vectorized=False, **extraneous)¶
Adams/BDF method with automatic stiffness detection and switching.
fun : callable
Right-hand side of the system. The calling signature is
fun(t, y). Here
tis a scalar and there are two options for ndarray
y. It can either have shape (n,), then
funmust return array_like with shape (n,). Or alternatively it can have shape (n, k), then
funmust return array_like with shape (n, k), i.e. each column corresponds to a single column in
y. The choice between the two options is determined by vectorized argument (see below). The vectorized implementation allows faster approximation of the Jacobian by finite differences.
t0 : float
y0 : array_like, shape (n,)
t_bound : float
Boundary time — the integration won’t continue beyond it. It also determines the direction of the integration.
first_step : float or None, optional
Initial step size. Default is
Nonewhich means that the algorithm should choose.
min_step : float, optional
Minimum allowed step size. Default is 0.0, i.e. the step is not bounded and determined solely by the solver.
max_step : float, optional
Maximum allowed step size. Default is
np.inf, i.e. the step is not bounded and determined solely by the solver.
rtol, atol : float and array_like, optional
Relative and absolute tolerances. The solver keeps the local error estimates less than
atol + rtol * abs(y). Here rtol controls a relative accuracy (number of correct digits). But if a component of y is approximately below atol then the error only needs to fall within the same atol threshold, and the number of correct digits is not guaranteed. If components of y have different scales, it might be beneficial to set different atol values for different components by passing array_like with shape (n,) for atol. Default values are 1e-3 for rtol and 1e-6 for atol.
jac : None or callable, optional
Jacobian matrix of the right-hand side of the system with respect to
y. The Jacobian matrix has shape (n, n) and its element (i, j) is equal to
d f_i / d y_j. The function will be called as
jac(t, y). If None (default), then the Jacobian will be approximated by finite differences. It is generally recommended to provide the Jacobian rather than relying on a finite difference approximation.
lband, uband : int or None, optional
Jacobian band width:
jac[i, j] != 0 only for i - lband <= j <= i + uband. Setting these requires your jac routine to return the Jacobian in the packed format: the returned array must have
uband + lband + 1rows in which Jacobian diagonals are written. Specifically
jac_packed[uband + i - j , j] = jac[i, j]. The same format is used in
scipy.linalg.solve_banded(check for an illustration). These parameters can be also used with
jac=Noneto reduce the number of Jacobian elements estimated by finite differences.
vectorized : bool, optional
Whether fun is implemented in a vectorized fashion. A vectorized implementation offers no advantages for this solver. Default is False.
[R56] (1, 2) A. C. Hindmarsh, “ODEPACK, A Systematized Collection of ODE Solvers,” IMACS Transactions on Scientific Computation, Vol 1., pp. 55-64, 1983. [R57] (1, 2) L. Petzold, “Automatic selection of methods for solving stiff and nonstiff systems of ordinary differential equations”, SIAM Journal on Scientific and Statistical Computing, Vol. 4, No. 1, pp. 136-148, 1983.
n (int) Number of equations. status (string) Current status of the solver: ‘running’, ‘finished’ or ‘failed’. t_bound (float) Boundary time. direction (float) Integration direction: +1 or -1. t (float) Current time. y (ndarray) Current state. t_old (float) Previous time. None if no steps were made yet. nfev (int) Number of the system’s rhs evaluations. njev (int) Number of the Jacobian evaluations.
Compute a local interpolant over the last successful step.
Perform one integration step.