602 lines
22 KiB
Python
602 lines
22 KiB
Python
import numpy as np
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from .base import OdeSolver, DenseOutput
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from .common import (validate_max_step, validate_tol, select_initial_step,
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norm, warn_extraneous, validate_first_step)
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from . import dop853_coefficients
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# Multiply steps computed from asymptotic behaviour of errors by this.
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SAFETY = 0.9
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MIN_FACTOR = 0.2 # Minimum allowed decrease in a step size.
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MAX_FACTOR = 10 # Maximum allowed increase in a step size.
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def rk_step(fun, t, y, f, h, A, B, C, K):
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"""Perform a single Runge-Kutta step.
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This function computes a prediction of an explicit Runge-Kutta method and
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also estimates the error of a less accurate method.
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Notation for Butcher tableau is as in [1]_.
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Parameters
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----------
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fun : callable
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Right-hand side of the system.
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t : float
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Current time.
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y : ndarray, shape (n,)
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Current state.
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f : ndarray, shape (n,)
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Current value of the derivative, i.e., ``fun(x, y)``.
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h : float
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Step to use.
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A : ndarray, shape (n_stages, n_stages)
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Coefficients for combining previous RK stages to compute the next
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stage. For explicit methods the coefficients at and above the main
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diagonal are zeros.
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B : ndarray, shape (n_stages,)
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Coefficients for combining RK stages for computing the final
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prediction.
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C : ndarray, shape (n_stages,)
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Coefficients for incrementing time for consecutive RK stages.
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The value for the first stage is always zero.
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K : ndarray, shape (n_stages + 1, n)
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Storage array for putting RK stages here. Stages are stored in rows.
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The last row is a linear combination of the previous rows with
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coefficients
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Returns
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-------
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y_new : ndarray, shape (n,)
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Solution at t + h computed with a higher accuracy.
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f_new : ndarray, shape (n,)
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Derivative ``fun(t + h, y_new)``.
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References
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----------
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.. [1] E. Hairer, S. P. Norsett G. Wanner, "Solving Ordinary Differential
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Equations I: Nonstiff Problems", Sec. II.4.
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"""
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K[0] = f
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for s, (a, c) in enumerate(zip(A[1:], C[1:]), start=1):
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dy = np.dot(K[:s].T, a[:s]) * h
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K[s] = fun(t + c * h, y + dy)
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y_new = y + h * np.dot(K[:-1].T, B)
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f_new = fun(t + h, y_new)
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K[-1] = f_new
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return y_new, f_new
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class RungeKutta(OdeSolver):
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"""Base class for explicit Runge-Kutta methods."""
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C: np.ndarray = NotImplemented
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A: np.ndarray = NotImplemented
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B: np.ndarray = NotImplemented
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E: np.ndarray = NotImplemented
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P: np.ndarray = NotImplemented
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order: int = NotImplemented
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error_estimator_order: int = NotImplemented
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n_stages: int = NotImplemented
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def __init__(self, fun, t0, y0, t_bound, max_step=np.inf,
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rtol=1e-3, atol=1e-6, vectorized=False,
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first_step=None, **extraneous):
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warn_extraneous(extraneous)
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super().__init__(fun, t0, y0, t_bound, vectorized,
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support_complex=True)
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self.y_old = None
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self.max_step = validate_max_step(max_step)
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self.rtol, self.atol = validate_tol(rtol, atol, self.n)
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self.f = self.fun(self.t, self.y)
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if first_step is None:
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self.h_abs = select_initial_step(
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self.fun, self.t, self.y, t_bound, max_step, self.f, self.direction,
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self.error_estimator_order, self.rtol, self.atol)
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else:
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self.h_abs = validate_first_step(first_step, t0, t_bound)
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self.K = np.empty((self.n_stages + 1, self.n), dtype=self.y.dtype)
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self.error_exponent = -1 / (self.error_estimator_order + 1)
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self.h_previous = None
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def _estimate_error(self, K, h):
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return np.dot(K.T, self.E) * h
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def _estimate_error_norm(self, K, h, scale):
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return norm(self._estimate_error(K, h) / scale)
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def _step_impl(self):
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t = self.t
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y = self.y
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max_step = self.max_step
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rtol = self.rtol
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atol = self.atol
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min_step = 10 * np.abs(np.nextafter(t, self.direction * np.inf) - t)
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if self.h_abs > max_step:
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h_abs = max_step
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elif self.h_abs < min_step:
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h_abs = min_step
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else:
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h_abs = self.h_abs
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step_accepted = False
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step_rejected = False
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while not step_accepted:
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if h_abs < min_step:
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return False, self.TOO_SMALL_STEP
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h = h_abs * self.direction
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t_new = t + h
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if self.direction * (t_new - self.t_bound) > 0:
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t_new = self.t_bound
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h = t_new - t
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h_abs = np.abs(h)
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y_new, f_new = rk_step(self.fun, t, y, self.f, h, self.A,
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self.B, self.C, self.K)
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scale = atol + np.maximum(np.abs(y), np.abs(y_new)) * rtol
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error_norm = self._estimate_error_norm(self.K, h, scale)
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if error_norm < 1:
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if error_norm == 0:
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factor = MAX_FACTOR
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else:
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factor = min(MAX_FACTOR,
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SAFETY * error_norm ** self.error_exponent)
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if step_rejected:
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factor = min(1, factor)
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h_abs *= factor
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step_accepted = True
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else:
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h_abs *= max(MIN_FACTOR,
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SAFETY * error_norm ** self.error_exponent)
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step_rejected = True
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self.h_previous = h
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self.y_old = y
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self.t = t_new
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self.y = y_new
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self.h_abs = h_abs
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self.f = f_new
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return True, None
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def _dense_output_impl(self):
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Q = self.K.T.dot(self.P)
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return RkDenseOutput(self.t_old, self.t, self.y_old, Q)
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class RK23(RungeKutta):
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"""Explicit Runge-Kutta method of order 3(2).
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This uses the Bogacki-Shampine pair of formulas [1]_. The error is controlled
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assuming accuracy of the second-order method, but steps are taken using the
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third-order accurate formula (local extrapolation is done). A cubic Hermite
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polynomial is used for the dense output.
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Can be applied in the complex domain.
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Parameters
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----------
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fun : callable
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Right-hand side of the system: the time derivative of the state ``y``
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at time ``t``. The calling signature is ``fun(t, y)``, where ``t`` is a
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scalar and ``y`` is an ndarray with ``len(y) = len(y0)``. ``fun`` must
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return an array of the same shape as ``y``. See `vectorized` for more
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information.
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t0 : float
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Initial time.
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y0 : array_like, shape (n,)
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Initial state.
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t_bound : float
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Boundary time - the integration won't continue beyond it. It also
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determines the direction of the integration.
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first_step : float or None, optional
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Initial step size. Default is ``None`` which means that the algorithm
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should choose.
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max_step : float, optional
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Maximum allowed step size. Default is np.inf, i.e., the step size is not
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bounded and determined solely by the solver.
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rtol, atol : float and array_like, optional
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Relative and absolute tolerances. The solver keeps the local error
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estimates less than ``atol + rtol * abs(y)``. Here `rtol` controls a
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relative accuracy (number of correct digits), while `atol` controls
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absolute accuracy (number of correct decimal places). To achieve the
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desired `rtol`, set `atol` to be smaller than the smallest value that
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can be expected from ``rtol * abs(y)`` so that `rtol` dominates the
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allowable error. If `atol` is larger than ``rtol * abs(y)`` the
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number of correct digits is not guaranteed. Conversely, to achieve the
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desired `atol` set `rtol` such that ``rtol * abs(y)`` is always smaller
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than `atol`. If components of y have different scales, it might be
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beneficial to set different `atol` values for different components by
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passing array_like with shape (n,) for `atol`. Default values are
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1e-3 for `rtol` and 1e-6 for `atol`.
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vectorized : bool, optional
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Whether `fun` may be called in a vectorized fashion. False (default)
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is recommended for this solver.
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If ``vectorized`` is False, `fun` will always be called with ``y`` of
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shape ``(n,)``, where ``n = len(y0)``.
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If ``vectorized`` is True, `fun` may be called with ``y`` of shape
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``(n, k)``, where ``k`` is an integer. In this case, `fun` must behave
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such that ``fun(t, y)[:, i] == fun(t, y[:, i])`` (i.e. each column of
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the returned array is the time derivative of the state corresponding
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with a column of ``y``).
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Setting ``vectorized=True`` allows for faster finite difference
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approximation of the Jacobian by methods 'Radau' and 'BDF', but
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will result in slower execution for this solver.
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Attributes
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----------
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n : int
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Number of equations.
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status : string
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Current status of the solver: 'running', 'finished' or 'failed'.
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t_bound : float
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Boundary time.
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direction : float
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Integration direction: +1 or -1.
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t : float
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Current time.
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y : ndarray
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Current state.
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t_old : float
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Previous time. None if no steps were made yet.
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step_size : float
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Size of the last successful step. None if no steps were made yet.
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nfev : int
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Number evaluations of the system's right-hand side.
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njev : int
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Number of evaluations of the Jacobian.
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Is always 0 for this solver as it does not use the Jacobian.
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nlu : int
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Number of LU decompositions. Is always 0 for this solver.
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References
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----------
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.. [1] P. Bogacki, L.F. Shampine, "A 3(2) Pair of Runge-Kutta Formulas",
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Appl. Math. Lett. Vol. 2, No. 4. pp. 321-325, 1989.
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"""
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order = 3
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error_estimator_order = 2
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n_stages = 3
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C = np.array([0, 1/2, 3/4])
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A = np.array([
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[0, 0, 0],
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[1/2, 0, 0],
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[0, 3/4, 0]
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])
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B = np.array([2/9, 1/3, 4/9])
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E = np.array([5/72, -1/12, -1/9, 1/8])
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P = np.array([[1, -4 / 3, 5 / 9],
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[0, 1, -2/3],
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[0, 4/3, -8/9],
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[0, -1, 1]])
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class RK45(RungeKutta):
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"""Explicit Runge-Kutta method of order 5(4).
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This uses the Dormand-Prince pair of formulas [1]_. The error is controlled
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assuming accuracy of the fourth-order method accuracy, but steps are taken
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using the fifth-order accurate formula (local extrapolation is done).
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A quartic interpolation polynomial is used for the dense output [2]_.
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Can be applied in the complex domain.
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Parameters
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----------
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fun : callable
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Right-hand side of the system. The calling signature is ``fun(t, y)``.
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Here ``t`` is a scalar, and there are two options for the ndarray ``y``:
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It can either have shape (n,); then ``fun`` must return array_like with
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shape (n,). Alternatively it can have shape (n, k); then ``fun``
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must return an array_like with shape (n, k), i.e., each column
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corresponds to a single column in ``y``. The choice between the two
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options is determined by `vectorized` argument (see below).
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t0 : float
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Initial time.
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y0 : array_like, shape (n,)
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Initial state.
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t_bound : float
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Boundary time - the integration won't continue beyond it. It also
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determines the direction of the integration.
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first_step : float or None, optional
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Initial step size. Default is ``None`` which means that the algorithm
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should choose.
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max_step : float, optional
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Maximum allowed step size. Default is np.inf, i.e., the step size is not
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bounded and determined solely by the solver.
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rtol, atol : float and array_like, optional
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Relative and absolute tolerances. The solver keeps the local error
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estimates less than ``atol + rtol * abs(y)``. Here `rtol` controls a
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relative accuracy (number of correct digits), while `atol` controls
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absolute accuracy (number of correct decimal places). To achieve the
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desired `rtol`, set `atol` to be smaller than the smallest value that
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can be expected from ``rtol * abs(y)`` so that `rtol` dominates the
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allowable error. If `atol` is larger than ``rtol * abs(y)`` the
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number of correct digits is not guaranteed. Conversely, to achieve the
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desired `atol` set `rtol` such that ``rtol * abs(y)`` is always smaller
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than `atol`. If components of y have different scales, it might be
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beneficial to set different `atol` values for different components by
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passing array_like with shape (n,) for `atol`. Default values are
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1e-3 for `rtol` and 1e-6 for `atol`.
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vectorized : bool, optional
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Whether `fun` is implemented in a vectorized fashion. Default is False.
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Attributes
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----------
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n : int
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Number of equations.
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status : string
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Current status of the solver: 'running', 'finished' or 'failed'.
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t_bound : float
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Boundary time.
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direction : float
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Integration direction: +1 or -1.
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t : float
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Current time.
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y : ndarray
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Current state.
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t_old : float
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Previous time. None if no steps were made yet.
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step_size : float
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Size of the last successful step. None if no steps were made yet.
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nfev : int
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Number evaluations of the system's right-hand side.
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njev : int
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Number of evaluations of the Jacobian.
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Is always 0 for this solver as it does not use the Jacobian.
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nlu : int
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Number of LU decompositions. Is always 0 for this solver.
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References
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----------
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.. [1] J. R. Dormand, P. J. Prince, "A family of embedded Runge-Kutta
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formulae", Journal of Computational and Applied Mathematics, Vol. 6,
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No. 1, pp. 19-26, 1980.
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.. [2] L. W. Shampine, "Some Practical Runge-Kutta Formulas", Mathematics
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of Computation,, Vol. 46, No. 173, pp. 135-150, 1986.
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"""
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order = 5
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error_estimator_order = 4
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n_stages = 6
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C = np.array([0, 1/5, 3/10, 4/5, 8/9, 1])
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A = np.array([
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[0, 0, 0, 0, 0],
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[1/5, 0, 0, 0, 0],
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[3/40, 9/40, 0, 0, 0],
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[44/45, -56/15, 32/9, 0, 0],
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[19372/6561, -25360/2187, 64448/6561, -212/729, 0],
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[9017/3168, -355/33, 46732/5247, 49/176, -5103/18656]
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])
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B = np.array([35/384, 0, 500/1113, 125/192, -2187/6784, 11/84])
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E = np.array([-71/57600, 0, 71/16695, -71/1920, 17253/339200, -22/525,
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1/40])
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# Corresponds to the optimum value of c_6 from [2]_.
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P = np.array([
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[1, -8048581381/2820520608, 8663915743/2820520608,
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-12715105075/11282082432],
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[0, 0, 0, 0],
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[0, 131558114200/32700410799, -68118460800/10900136933,
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87487479700/32700410799],
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[0, -1754552775/470086768, 14199869525/1410260304,
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-10690763975/1880347072],
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[0, 127303824393/49829197408, -318862633887/49829197408,
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701980252875 / 199316789632],
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[0, -282668133/205662961, 2019193451/616988883, -1453857185/822651844],
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[0, 40617522/29380423, -110615467/29380423, 69997945/29380423]])
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class DOP853(RungeKutta):
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"""Explicit Runge-Kutta method of order 8.
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This is a Python implementation of "DOP853" algorithm originally written
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in Fortran [1]_, [2]_. Note that this is not a literal translation, but
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the algorithmic core and coefficients are the same.
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Can be applied in the complex domain.
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Parameters
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----------
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fun : callable
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Right-hand side of the system. The calling signature is ``fun(t, y)``.
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Here, ``t`` is a scalar, and there are two options for the ndarray ``y``:
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It can either have shape (n,); then ``fun`` must return array_like with
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shape (n,). Alternatively it can have shape (n, k); then ``fun``
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must return an array_like with shape (n, k), i.e. each column
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corresponds to a single column in ``y``. The choice between the two
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options is determined by `vectorized` argument (see below).
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t0 : float
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Initial time.
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y0 : array_like, shape (n,)
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Initial state.
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t_bound : float
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Boundary time - the integration won't continue beyond it. It also
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determines the direction of the integration.
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first_step : float or None, optional
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Initial step size. Default is ``None`` which means that the algorithm
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should choose.
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max_step : float, optional
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Maximum allowed step size. Default is np.inf, i.e. the step size is not
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bounded and determined solely by the solver.
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rtol, atol : float and array_like, optional
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Relative and absolute tolerances. The solver keeps the local error
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|
estimates less than ``atol + rtol * abs(y)``. Here `rtol` controls a
|
|
relative accuracy (number of correct digits), while `atol` controls
|
|
absolute accuracy (number of correct decimal places). To achieve the
|
|
desired `rtol`, set `atol` to be smaller than the smallest value that
|
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can be expected from ``rtol * abs(y)`` so that `rtol` dominates the
|
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allowable error. If `atol` is larger than ``rtol * abs(y)`` the
|
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number of correct digits is not guaranteed. Conversely, to achieve the
|
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desired `atol` set `rtol` such that ``rtol * abs(y)`` is always smaller
|
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than `atol`. If components of y have different scales, it might be
|
|
beneficial to set different `atol` values for different components by
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passing array_like with shape (n,) for `atol`. Default values are
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1e-3 for `rtol` and 1e-6 for `atol`.
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vectorized : bool, optional
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Whether `fun` is implemented in a vectorized fashion. Default is False.
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Attributes
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----------
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n : int
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Number of equations.
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status : string
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Current status of the solver: 'running', 'finished' or 'failed'.
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t_bound : float
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Boundary time.
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direction : float
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Integration direction: +1 or -1.
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t : float
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Current time.
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y : ndarray
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Current state.
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t_old : float
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Previous time. None if no steps were made yet.
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step_size : float
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Size of the last successful step. None if no steps were made yet.
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nfev : int
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Number evaluations of the system's right-hand side.
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njev : int
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Number of evaluations of the Jacobian. Is always 0 for this solver
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as it does not use the Jacobian.
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nlu : int
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Number of LU decompositions. Is always 0 for this solver.
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References
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----------
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.. [1] E. Hairer, S. P. Norsett G. Wanner, "Solving Ordinary Differential
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Equations I: Nonstiff Problems", Sec. II.
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.. [2] `Page with original Fortran code of DOP853
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<http://www.unige.ch/~hairer/software.html>`_.
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"""
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n_stages = dop853_coefficients.N_STAGES
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order = 8
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error_estimator_order = 7
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A = dop853_coefficients.A[:n_stages, :n_stages]
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B = dop853_coefficients.B
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C = dop853_coefficients.C[:n_stages]
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E3 = dop853_coefficients.E3
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E5 = dop853_coefficients.E5
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D = dop853_coefficients.D
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A_EXTRA = dop853_coefficients.A[n_stages + 1:]
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C_EXTRA = dop853_coefficients.C[n_stages + 1:]
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def __init__(self, fun, t0, y0, t_bound, max_step=np.inf,
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rtol=1e-3, atol=1e-6, vectorized=False,
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first_step=None, **extraneous):
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super().__init__(fun, t0, y0, t_bound, max_step, rtol, atol,
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vectorized, first_step, **extraneous)
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self.K_extended = np.empty((dop853_coefficients.N_STAGES_EXTENDED,
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self.n), dtype=self.y.dtype)
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self.K = self.K_extended[:self.n_stages + 1]
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def _estimate_error(self, K, h): # Left for testing purposes.
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err5 = np.dot(K.T, self.E5)
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err3 = np.dot(K.T, self.E3)
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denom = np.hypot(np.abs(err5), 0.1 * np.abs(err3))
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correction_factor = np.ones_like(err5)
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mask = denom > 0
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correction_factor[mask] = np.abs(err5[mask]) / denom[mask]
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return h * err5 * correction_factor
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def _estimate_error_norm(self, K, h, scale):
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err5 = np.dot(K.T, self.E5) / scale
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err3 = np.dot(K.T, self.E3) / scale
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err5_norm_2 = np.linalg.norm(err5)**2
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err3_norm_2 = np.linalg.norm(err3)**2
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if err5_norm_2 == 0 and err3_norm_2 == 0:
|
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return 0.0
|
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denom = err5_norm_2 + 0.01 * err3_norm_2
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return np.abs(h) * err5_norm_2 / np.sqrt(denom * len(scale))
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def _dense_output_impl(self):
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K = self.K_extended
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h = self.h_previous
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for s, (a, c) in enumerate(zip(self.A_EXTRA, self.C_EXTRA),
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start=self.n_stages + 1):
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dy = np.dot(K[:s].T, a[:s]) * h
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K[s] = self.fun(self.t_old + c * h, self.y_old + dy)
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F = np.empty((dop853_coefficients.INTERPOLATOR_POWER, self.n),
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dtype=self.y_old.dtype)
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f_old = K[0]
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delta_y = self.y - self.y_old
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|
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F[0] = delta_y
|
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F[1] = h * f_old - delta_y
|
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F[2] = 2 * delta_y - h * (self.f + f_old)
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F[3:] = h * np.dot(self.D, K)
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return Dop853DenseOutput(self.t_old, self.t, self.y_old, F)
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class RkDenseOutput(DenseOutput):
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def __init__(self, t_old, t, y_old, Q):
|
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super().__init__(t_old, t)
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self.h = t - t_old
|
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self.Q = Q
|
|
self.order = Q.shape[1] - 1
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self.y_old = y_old
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def _call_impl(self, t):
|
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x = (t - self.t_old) / self.h
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if t.ndim == 0:
|
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p = np.tile(x, self.order + 1)
|
|
p = np.cumprod(p)
|
|
else:
|
|
p = np.tile(x, (self.order + 1, 1))
|
|
p = np.cumprod(p, axis=0)
|
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y = self.h * np.dot(self.Q, p)
|
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if y.ndim == 2:
|
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y += self.y_old[:, None]
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else:
|
|
y += self.y_old
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|
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return y
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|
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|
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class Dop853DenseOutput(DenseOutput):
|
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def __init__(self, t_old, t, y_old, F):
|
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super().__init__(t_old, t)
|
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self.h = t - t_old
|
|
self.F = F
|
|
self.y_old = y_old
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|
|
|
def _call_impl(self, t):
|
|
x = (t - self.t_old) / self.h
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|
|
|
if t.ndim == 0:
|
|
y = np.zeros_like(self.y_old)
|
|
else:
|
|
x = x[:, None]
|
|
y = np.zeros((len(x), len(self.y_old)), dtype=self.y_old.dtype)
|
|
|
|
for i, f in enumerate(reversed(self.F)):
|
|
y += f
|
|
if i % 2 == 0:
|
|
y *= x
|
|
else:
|
|
y *= 1 - x
|
|
y += self.y_old
|
|
|
|
return y.T
|