467 lines
16 KiB
Python
467 lines
16 KiB
Python
|
import numpy as np
|
||
|
|
from scipy.linalg import lu_factor, lu_solve
|
||
|
|
from scipy.sparse import issparse, csc_matrix, eye
|
||
|
|
from scipy.sparse.linalg import splu
|
||
|
|
from scipy.optimize._numdiff import group_columns
|
||
|
|
from .common import (validate_max_step, validate_tol, select_initial_step,
|
||
|
|
norm, EPS, num_jac, validate_first_step,
|
||
|
|
warn_extraneous)
|
||
|
|
from .base import OdeSolver, DenseOutput
|
||
|
|
|
||
|
|
|
||
|
|
MAX_ORDER = 5
|
||
|
|
NEWTON_MAXITER = 4
|
||
|
|
MIN_FACTOR = 0.2
|
||
|
|
MAX_FACTOR = 10
|
||
|
|
|
||
|
|
|
||
|
|
def compute_R(order, factor):
|
||
|
|
"""Compute the matrix for changing the differences array."""
|
||
|
|
I = np.arange(1, order + 1)[:, None]
|
||
|
|
J = np.arange(1, order + 1)
|
||
|
|
M = np.zeros((order + 1, order + 1))
|
||
|
|
M[1:, 1:] = (I - 1 - factor * J) / I
|
||
|
|
M[0] = 1
|
||
|
|
return np.cumprod(M, axis=0)
|
||
|
|
|
||
|
|
|
||
|
|
def change_D(D, order, factor):
|
||
|
|
"""Change differences array in-place when step size is changed."""
|
||
|
|
R = compute_R(order, factor)
|
||
|
|
U = compute_R(order, 1)
|
||
|
|
RU = R.dot(U)
|
||
|
|
D[:order + 1] = np.dot(RU.T, D[:order + 1])
|
||
|
|
|
||
|
|
|
||
|
|
def solve_bdf_system(fun, t_new, y_predict, c, psi, LU, solve_lu, scale, tol):
|
||
|
|
"""Solve the algebraic system resulting from BDF method."""
|
||
|
|
d = 0
|
||
|
|
y = y_predict.copy()
|
||
|
|
dy_norm_old = None
|
||
|
|
converged = False
|
||
|
|
for k in range(NEWTON_MAXITER):
|
||
|
|
f = fun(t_new, y)
|
||
|
|
if not np.all(np.isfinite(f)):
|
||
|
|
break
|
||
|
|
|
||
|
|
dy = solve_lu(LU, c * f - psi - d)
|
||
|
|
dy_norm = norm(dy / scale)
|
||
|
|
|
||
|
|
if dy_norm_old is None:
|
||
|
|
rate = None
|
||
|
|
else:
|
||
|
|
rate = dy_norm / dy_norm_old
|
||
|
|
|
||
|
|
if (rate is not None and (rate >= 1 or
|
||
|
|
rate ** (NEWTON_MAXITER - k) / (1 - rate) * dy_norm > tol)):
|
||
|
|
break
|
||
|
|
|
||
|
|
y += dy
|
||
|
|
d += dy
|
||
|
|
|
||
|
|
if (dy_norm == 0 or
|
||
|
|
rate is not None and rate / (1 - rate) * dy_norm < tol):
|
||
|
|
converged = True
|
||
|
|
break
|
||
|
|
|
||
|
|
dy_norm_old = dy_norm
|
||
|
|
|
||
|
|
return converged, k + 1, y, d
|
||
|
|
|
||
|
|
|
||
|
|
class BDF(OdeSolver):
|
||
|
|
"""Implicit method based on backward-differentiation formulas.
|
||
|
|
|
||
|
|
This is a variable order method with the order varying automatically from
|
||
|
|
1 to 5. The general framework of the BDF algorithm is described in [1]_.
|
||
|
|
This class implements a quasi-constant step size as explained in [2]_.
|
||
|
|
The error estimation strategy for the constant-step BDF is derived in [3]_.
|
||
|
|
An accuracy enhancement using modified formulas (NDF) [2]_ is also implemented.
|
||
|
|
|
||
|
|
Can be applied in the complex domain.
|
||
|
|
|
||
|
|
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.
|
||
|
|
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, array_like, sparse_matrix, callable}, optional
|
||
|
|
Jacobian matrix of the right-hand side of the system with respect to y,
|
||
|
|
required by this method. The Jacobian matrix has shape (n, n) and its
|
||
|
|
element (i, j) is equal to ``d f_i / d y_j``.
|
||
|
|
There are three ways to define the Jacobian:
|
||
|
|
|
||
|
|
* If array_like or sparse_matrix, the Jacobian is assumed to
|
||
|
|
be constant.
|
||
|
|
* If callable, the Jacobian is assumed to depend on both
|
||
|
|
t and y; it will be called as ``jac(t, y)`` as necessary.
|
||
|
|
For the 'Radau' and 'BDF' methods, the return value might be a
|
||
|
|
sparse matrix.
|
||
|
|
* 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.
|
||
|
|
jac_sparsity : {None, array_like, sparse matrix}, optional
|
||
|
|
Defines a sparsity structure of the Jacobian matrix for a
|
||
|
|
finite-difference approximation. Its shape must be (n, n). This argument
|
||
|
|
is ignored if `jac` is not `None`. If the Jacobian has only few non-zero
|
||
|
|
elements in *each* row, providing the sparsity structure will greatly
|
||
|
|
speed up the computations [4]_. A zero entry means that a corresponding
|
||
|
|
element in the Jacobian is always zero. If None (default), the Jacobian
|
||
|
|
is assumed to be dense.
|
||
|
|
vectorized : bool, optional
|
||
|
|
Whether `fun` is implemented in a vectorized fashion. Default is False.
|
||
|
|
|
||
|
|
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.
|
||
|
|
step_size : float
|
||
|
|
Size of the last successful step. 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.
|
||
|
|
nlu : int
|
||
|
|
Number of LU decompositions.
|
||
|
|
|
||
|
|
References
|
||
|
|
----------
|
||
|
|
.. [1] G. D. Byrne, A. C. Hindmarsh, "A Polyalgorithm for the Numerical
|
||
|
|
Solution of Ordinary Differential Equations", ACM Transactions on
|
||
|
|
Mathematical Software, Vol. 1, No. 1, pp. 71-96, March 1975.
|
||
|
|
.. [2] L. F. Shampine, M. W. Reichelt, "THE MATLAB ODE SUITE", SIAM J. SCI.
|
||
|
|
COMPUTE., Vol. 18, No. 1, pp. 1-22, January 1997.
|
||
|
|
.. [3] E. Hairer, G. Wanner, "Solving Ordinary Differential Equations I:
|
||
|
|
Nonstiff Problems", Sec. III.2.
|
||
|
|
.. [4] A. Curtis, M. J. D. Powell, and J. Reid, "On the estimation of
|
||
|
|
sparse Jacobian matrices", Journal of the Institute of Mathematics
|
||
|
|
and its Applications, 13, pp. 117-120, 1974.
|
||
|
|
"""
|
||
|
|
def __init__(self, fun, t0, y0, t_bound, max_step=np.inf,
|
||
|
|
rtol=1e-3, atol=1e-6, jac=None, jac_sparsity=None,
|
||
|
|
vectorized=False, first_step=None, **extraneous):
|
||
|
|
warn_extraneous(extraneous)
|
||
|
|
super(BDF, self).__init__(fun, t0, y0, t_bound, vectorized,
|
||
|
|
support_complex=True)
|
||
|
|
self.max_step = validate_max_step(max_step)
|
||
|
|
self.rtol, self.atol = validate_tol(rtol, atol, self.n)
|
||
|
|
f = self.fun(self.t, self.y)
|
||
|
|
if first_step is None:
|
||
|
|
self.h_abs = select_initial_step(self.fun, self.t, self.y, f,
|
||
|
|
self.direction, 1,
|
||
|
|
self.rtol, self.atol)
|
||
|
|
else:
|
||
|
|
self.h_abs = validate_first_step(first_step, t0, t_bound)
|
||
|
|
self.h_abs_old = None
|
||
|
|
self.error_norm_old = None
|
||
|
|
|
||
|
|
self.newton_tol = max(10 * EPS / rtol, min(0.03, rtol ** 0.5))
|
||
|
|
|
||
|
|
self.jac_factor = None
|
||
|
|
self.jac, self.J = self._validate_jac(jac, jac_sparsity)
|
||
|
|
if issparse(self.J):
|
||
|
|
def lu(A):
|
||
|
|
self.nlu += 1
|
||
|
|
return splu(A)
|
||
|
|
|
||
|
|
def solve_lu(LU, b):
|
||
|
|
return LU.solve(b)
|
||
|
|
|
||
|
|
I = eye(self.n, format='csc', dtype=self.y.dtype)
|
||
|
|
else:
|
||
|
|
def lu(A):
|
||
|
|
self.nlu += 1
|
||
|
|
return lu_factor(A, overwrite_a=True)
|
||
|
|
|
||
|
|
def solve_lu(LU, b):
|
||
|
|
return lu_solve(LU, b, overwrite_b=True)
|
||
|
|
|
||
|
|
I = np.identity(self.n, dtype=self.y.dtype)
|
||
|
|
|
||
|
|
self.lu = lu
|
||
|
|
self.solve_lu = solve_lu
|
||
|
|
self.I = I
|
||
|
|
|
||
|
|
kappa = np.array([0, -0.1850, -1/9, -0.0823, -0.0415, 0])
|
||
|
|
self.gamma = np.hstack((0, np.cumsum(1 / np.arange(1, MAX_ORDER + 1))))
|
||
|
|
self.alpha = (1 - kappa) * self.gamma
|
||
|
|
self.error_const = kappa * self.gamma + 1 / np.arange(1, MAX_ORDER + 2)
|
||
|
|
|
||
|
|
D = np.empty((MAX_ORDER + 3, self.n), dtype=self.y.dtype)
|
||
|
|
D[0] = self.y
|
||
|
|
D[1] = f * self.h_abs * self.direction
|
||
|
|
self.D = D
|
||
|
|
|
||
|
|
self.order = 1
|
||
|
|
self.n_equal_steps = 0
|
||
|
|
self.LU = None
|
||
|
|
|
||
|
|
def _validate_jac(self, jac, sparsity):
|
||
|
|
t0 = self.t
|
||
|
|
y0 = self.y
|
||
|
|
|
||
|
|
if jac is None:
|
||
|
|
if sparsity is not None:
|
||
|
|
if issparse(sparsity):
|
||
|
|
sparsity = csc_matrix(sparsity)
|
||
|
|
groups = group_columns(sparsity)
|
||
|
|
sparsity = (sparsity, groups)
|
||
|
|
|
||
|
|
def jac_wrapped(t, y):
|
||
|
|
self.njev += 1
|
||
|
|
f = self.fun_single(t, y)
|
||
|
|
J, self.jac_factor = num_jac(self.fun_vectorized, t, y, f,
|
||
|
|
self.atol, self.jac_factor,
|
||
|
|
sparsity)
|
||
|
|
return J
|
||
|
|
J = jac_wrapped(t0, y0)
|
||
|
|
elif callable(jac):
|
||
|
|
J = jac(t0, y0)
|
||
|
|
self.njev += 1
|
||
|
|
if issparse(J):
|
||
|
|
J = csc_matrix(J, dtype=y0.dtype)
|
||
|
|
|
||
|
|
def jac_wrapped(t, y):
|
||
|
|
self.njev += 1
|
||
|
|
return csc_matrix(jac(t, y), dtype=y0.dtype)
|
||
|
|
else:
|
||
|
|
J = np.asarray(J, dtype=y0.dtype)
|
||
|
|
|
||
|
|
def jac_wrapped(t, y):
|
||
|
|
self.njev += 1
|
||
|
|
return np.asarray(jac(t, y), dtype=y0.dtype)
|
||
|
|
|
||
|
|
if J.shape != (self.n, self.n):
|
||
|
|
raise ValueError("`jac` is expected to have shape {}, but "
|
||
|
|
"actually has {}."
|
||
|
|
.format((self.n, self.n), J.shape))
|
||
|
|
else:
|
||
|
|
if issparse(jac):
|
||
|
|
J = csc_matrix(jac, dtype=y0.dtype)
|
||
|
|
else:
|
||
|
|
J = np.asarray(jac, dtype=y0.dtype)
|
||
|
|
|
||
|
|
if J.shape != (self.n, self.n):
|
||
|
|
raise ValueError("`jac` is expected to have shape {}, but "
|
||
|
|
"actually has {}."
|
||
|
|
.format((self.n, self.n), J.shape))
|
||
|
|
jac_wrapped = None
|
||
|
|
|
||
|
|
return jac_wrapped, J
|
||
|
|
|
||
|
|
def _step_impl(self):
|
||
|
|
t = self.t
|
||
|
|
D = self.D
|
||
|
|
|
||
|
|
max_step = self.max_step
|
||
|
|
min_step = 10 * np.abs(np.nextafter(t, self.direction * np.inf) - t)
|
||
|
|
if self.h_abs > max_step:
|
||
|
|
h_abs = max_step
|
||
|
|
change_D(D, self.order, max_step / self.h_abs)
|
||
|
|
self.n_equal_steps = 0
|
||
|
|
elif self.h_abs < min_step:
|
||
|
|
h_abs = min_step
|
||
|
|
change_D(D, self.order, min_step / self.h_abs)
|
||
|
|
self.n_equal_steps = 0
|
||
|
|
else:
|
||
|
|
h_abs = self.h_abs
|
||
|
|
|
||
|
|
atol = self.atol
|
||
|
|
rtol = self.rtol
|
||
|
|
order = self.order
|
||
|
|
|
||
|
|
alpha = self.alpha
|
||
|
|
gamma = self.gamma
|
||
|
|
error_const = self.error_const
|
||
|
|
|
||
|
|
J = self.J
|
||
|
|
LU = self.LU
|
||
|
|
current_jac = self.jac is None
|
||
|
|
|
||
|
|
step_accepted = False
|
||
|
|
while not step_accepted:
|
||
|
|
if h_abs < min_step:
|
||
|
|
return False, self.TOO_SMALL_STEP
|
||
|
|
|
||
|
|
h = h_abs * self.direction
|
||
|
|
t_new = t + h
|
||
|
|
|
||
|
|
if self.direction * (t_new - self.t_bound) > 0:
|
||
|
|
t_new = self.t_bound
|
||
|
|
change_D(D, order, np.abs(t_new - t) / h_abs)
|
||
|
|
self.n_equal_steps = 0
|
||
|
|
LU = None
|
||
|
|
|
||
|
|
h = t_new - t
|
||
|
|
h_abs = np.abs(h)
|
||
|
|
|
||
|
|
y_predict = np.sum(D[:order + 1], axis=0)
|
||
|
|
|
||
|
|
scale = atol + rtol * np.abs(y_predict)
|
||
|
|
psi = np.dot(D[1: order + 1].T, gamma[1: order + 1]) / alpha[order]
|
||
|
|
|
||
|
|
converged = False
|
||
|
|
c = h / alpha[order]
|
||
|
|
while not converged:
|
||
|
|
if LU is None:
|
||
|
|
LU = self.lu(self.I - c * J)
|
||
|
|
|
||
|
|
converged, n_iter, y_new, d = solve_bdf_system(
|
||
|
|
self.fun, t_new, y_predict, c, psi, LU, self.solve_lu,
|
||
|
|
scale, self.newton_tol)
|
||
|
|
|
||
|
|
if not converged:
|
||
|
|
if current_jac:
|
||
|
|
break
|
||
|
|
J = self.jac(t_new, y_predict)
|
||
|
|
LU = None
|
||
|
|
current_jac = True
|
||
|
|
|
||
|
|
if not converged:
|
||
|
|
factor = 0.5
|
||
|
|
h_abs *= factor
|
||
|
|
change_D(D, order, factor)
|
||
|
|
self.n_equal_steps = 0
|
||
|
|
LU = None
|
||
|
|
continue
|
||
|
|
|
||
|
|
safety = 0.9 * (2 * NEWTON_MAXITER + 1) / (2 * NEWTON_MAXITER
|
||
|
|
+ n_iter)
|
||
|
|
|
||
|
|
scale = atol + rtol * np.abs(y_new)
|
||
|
|
error = error_const[order] * d
|
||
|
|
error_norm = norm(error / scale)
|
||
|
|
|
||
|
|
if error_norm > 1:
|
||
|
|
factor = max(MIN_FACTOR,
|
||
|
|
safety * error_norm ** (-1 / (order + 1)))
|
||
|
|
h_abs *= factor
|
||
|
|
change_D(D, order, factor)
|
||
|
|
self.n_equal_steps = 0
|
||
|
|
# As we didn't have problems with convergence, we don't
|
||
|
|
# reset LU here.
|
||
|
|
else:
|
||
|
|
step_accepted = True
|
||
|
|
|
||
|
|
self.n_equal_steps += 1
|
||
|
|
|
||
|
|
self.t = t_new
|
||
|
|
self.y = y_new
|
||
|
|
|
||
|
|
self.h_abs = h_abs
|
||
|
|
self.J = J
|
||
|
|
self.LU = LU
|
||
|
|
|
||
|
|
# Update differences. The principal relation here is
|
||
|
|
# D^{j + 1} y_n = D^{j} y_n - D^{j} y_{n - 1}. Keep in mind that D
|
||
|
|
# contained difference for previous interpolating polynomial and
|
||
|
|
# d = D^{k + 1} y_n. Thus this elegant code follows.
|
||
|
|
D[order + 2] = d - D[order + 1]
|
||
|
|
D[order + 1] = d
|
||
|
|
for i in reversed(range(order + 1)):
|
||
|
|
D[i] += D[i + 1]
|
||
|
|
|
||
|
|
if self.n_equal_steps < order + 1:
|
||
|
|
return True, None
|
||
|
|
|
||
|
|
if order > 1:
|
||
|
|
error_m = error_const[order - 1] * D[order]
|
||
|
|
error_m_norm = norm(error_m / scale)
|
||
|
|
else:
|
||
|
|
error_m_norm = np.inf
|
||
|
|
|
||
|
|
if order < MAX_ORDER:
|
||
|
|
error_p = error_const[order + 1] * D[order + 2]
|
||
|
|
error_p_norm = norm(error_p / scale)
|
||
|
|
else:
|
||
|
|
error_p_norm = np.inf
|
||
|
|
|
||
|
|
error_norms = np.array([error_m_norm, error_norm, error_p_norm])
|
||
|
|
with np.errstate(divide='ignore'):
|
||
|
|
factors = error_norms ** (-1 / np.arange(order, order + 3))
|
||
|
|
|
||
|
|
delta_order = np.argmax(factors) - 1
|
||
|
|
order += delta_order
|
||
|
|
self.order = order
|
||
|
|
|
||
|
|
factor = min(MAX_FACTOR, safety * np.max(factors))
|
||
|
|
self.h_abs *= factor
|
||
|
|
change_D(D, order, factor)
|
||
|
|
self.n_equal_steps = 0
|
||
|
|
self.LU = None
|
||
|
|
|
||
|
|
return True, None
|
||
|
|
|
||
|
|
def _dense_output_impl(self):
|
||
|
|
return BdfDenseOutput(self.t_old, self.t, self.h_abs * self.direction,
|
||
|
|
self.order, self.D[:self.order + 1].copy())
|
||
|
|
|
||
|
|
|
||
|
|
class BdfDenseOutput(DenseOutput):
|
||
|
|
def __init__(self, t_old, t, h, order, D):
|
||
|
|
super(BdfDenseOutput, self).__init__(t_old, t)
|
||
|
|
self.order = order
|
||
|
|
self.t_shift = self.t - h * np.arange(self.order)
|
||
|
|
self.denom = h * (1 + np.arange(self.order))
|
||
|
|
self.D = D
|
||
|
|
|
||
|
|
def _call_impl(self, t):
|
||
|
|
if t.ndim == 0:
|
||
|
|
x = (t - self.t_shift) / self.denom
|
||
|
|
p = np.cumprod(x)
|
||
|
|
else:
|
||
|
|
x = (t - self.t_shift[:, None]) / self.denom[:, None]
|
||
|
|
p = np.cumprod(x, axis=0)
|
||
|
|
|
||
|
|
y = np.dot(self.D[1:].T, p)
|
||
|
|
if y.ndim == 1:
|
||
|
|
y += self.D[0]
|
||
|
|
else:
|
||
|
|
y += self.D[0, :, None]
|
||
|
|
|
||
|
|
return y
|