scipy.integrate.LSODA¶

class scipy.integrate.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)[source]¶

Adams/BDF method with automatic stiffness detection and switching.

This is a wrapper to the Fortran solver from ODEPACK [1]. It switches automatically between the nonstiff Adams method and the stiff BDF method. The method was originally detailed in [2].

Parameters:

Parameters:	fun : callable Right-hand side of the system. The calling signature is `fun(t, y)`. Here `t` is a scalar, and there are two options for the ndarray `y`: It can either have shape (n,); then `fun` must return array_like with shape (n,). Alternatively it can have shape (n, k); then `fun` must return an 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 a faster approximation of the Jacobian by finite differences (required for this solver). t0 : float Initial time. y0 : array_like, shape (n,) Initial state. 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 `None` which means that the algorithm should choose. min_step : float, optional Minimum allowed step size. Default is 0.0, i.e. the step size 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 size 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, 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), 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 Parameters defining the bandwidth of the Jacobian, i.e., `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 `n` columns and `uband + lband + 1` rows 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=None` to 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.

fun : callable: Right-hand side of the system. The calling signature is fun(t, y). Here t is a scalar, and there are two options for the ndarray y: It can either have shape (n,); then fun must return array_like with shape (n,). Alternatively it can have shape (n, k); then fun must return an 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 a faster approximation of the Jacobian by finite differences (required for this solver).
t0 : float: Initial time.
y0 : array_like, shape (n,): Initial state.
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 None which means that the algorithm should choose.
min_step : float, optional: Minimum allowed step size. Default is 0.0, i.e. the step size 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 size 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, 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), 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: Parameters defining the bandwidth of the Jacobian, i.e., 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 n columns and uband + lband + 1 rows 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=None to 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.

References

[1]	(1, 2) A. C. Hindmarsh, “ODEPACK, A Systematized Collection of ODE Solvers,” IMACS Transactions on Scientific Computation, Vol 1., pp. 55-64, 1983.

[2]	(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.

Attributes:

Attributes:	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 evaluations of the right-hand side. njev : int Number of evaluations of the Jacobian.

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 evaluations of the right-hand side.
njev : int: Number of evaluations of the Jacobian.

Methods

`dense_output`()	Compute a local interpolant over the last successful step.
`step`()	Perform one integration step.

Previous topic

scipy.integrate.BDF.step

Next topic

scipy.integrate.LSODA.dense_output

scipy.integrate.LSODA¶

Previous topic

Next topic

Quick search