Package org.hipparchus.ode.nonstiff

Class AdamsBashforthIntegrator

java.lang.Object
org.hipparchus.ode.AbstractIntegrator
org.hipparchus.ode.nonstiff.AdaptiveStepsizeIntegrator
org.hipparchus.ode.MultistepIntegrator
org.hipparchus.ode.nonstiff.AdamsIntegrator
org.hipparchus.ode.nonstiff.AdamsBashforthIntegrator
All Implemented Interfaces:
ODEIntegrator
public class AdamsBashforthIntegrator extends AdamsIntegrator
This class implements explicit Adams-Bashforth integrators for Ordinary Differential Equations.

Adams-Bashforth methods (in fact due to Adams alone) are explicit multistep ODE solvers. This implementation is a variation of the classical one: it uses adaptive stepsize to implement error control, whereas classical implementations are fixed step size. The value of state vector at step n+1 is a simple combination of the value at step n and of the derivatives at steps n, n-1, n-2 ... Depending on the number k of previous steps one wants to use for computing the next value, different formulas are available:

  • k = 1: yn+1 = yn + h y'n
  • k = 2: yn+1 = yn + h (3y'n-y'n-1)/2
  • k = 3: yn+1 = yn + h (23y'n-16y'n-1+5y'n-2)/12
  • k = 4: yn+1 = yn + h (55y'n-59y'n-1+37y'n-2-9y'n-3)/24
  • ...

A k-steps Adams-Bashforth method is of order k.

There must be sufficient time for the starter integrator to take several steps between the the last reset event, and the end of integration, otherwise an exception may be thrown during integration. The user can adjust the end date of integration, or the step size of the starter integrator to ensure a sufficient number of steps can be completed before the end of integration.

Implementation details

We define scaled derivatives si(n) at step n as: \[ \left\{\begin{align} s_1(n) &= h y'_n \text{ for first derivative}\\ s_2(n) &= \frac{h^2}{2} y_n'' \text{ for second derivative}\\ s_3(n) &= \frac{h^3}{6} y_n''' \text{ for third derivative}\\ &\cdots\\ s_k(n) &= \frac{h^k}{k!} y_n^{(k)} \text{ for } k^\mathrm{th} \text{ derivative} \end{align}\right. \]

The definitions above use the classical representation with several previous first derivatives. Lets define \[ q_n = [ s_1(n-1) s_1(n-2) \ldots s_1(n-(k-1)) ]^T \] (we omit the k index in the notation for clarity). With these definitions, Adams-Bashforth methods can be written:

\[ \left\{\begin{align} k = 1: & y_{n+1} = y_n + s_1(n) \\ k = 2: & y_{n+1} = y_n + \frac{3}{2} s_1(n) + [ \frac{-1}{2} ] q_n \\ k = 3: & y_{n+1} = y_n + \frac{23}{12} s_1(n) + [ \frac{-16}{12} \frac{5}{12} ] q_n \\ k = 4: & y_{n+1} = y_n + \frac{55}{24} s_1(n) + [ \frac{-59}{24} \frac{37}{24} \frac{-9}{24} ] q_n \\ & \cdots \end{align}\right. \]

Instead of using the classical representation with first derivatives only (yn, s1(n) and qn), our implementation uses the Nordsieck vector with higher degrees scaled derivatives all taken at the same step (yn, s1(n) and rn) where rn is defined as: \[ r_n = [ s_2(n), s_3(n) \ldots s_k(n) ]^T \] (here again we omit the k index in the notation for clarity)

Taylor series formulas show that for any index offset i, s1(n-i) can be computed from s1(n), s2(n) ... sk(n), the formula being exact for degree k polynomials. \[ s_1(n-i) = s_1(n) + \sum_{j\gt 0} (j+1) (-i)^j s_{j+1}(n) \] The previous formula can be used with several values for i to compute the transform between classical representation and Nordsieck vector. The transform between rn and qn resulting from the Taylor series formulas above is: \[ q_n = s_1(n) u + P r_n \] where u is the [ 1 1 ... 1 ]T vector and P is the (k-1)×(k-1) matrix built with the \((j+1) (-i)^j\) terms with i being the row number starting from 1 and j being the column number starting from 1: \[ P=\begin{bmatrix} -2 & 3 & -4 & 5 & \ldots \\ -4 & 12 & -32 & 80 & \ldots \\ -6 & 27 & -108 & 405 & \ldots \\ -8 & 48 & -256 & 1280 & \ldots \\ & & \ldots\\ \end{bmatrix} \]

Using the Nordsieck vector has several advantages:

  • it greatly simplifies step interpolation as the interpolator mainly applies Taylor series formulas,
  • it simplifies step changes that occur when discrete events that truncate the step are triggered,
  • it allows to extend the methods in order to support adaptive stepsize.

The Nordsieck vector at step n+1 is computed from the Nordsieck vector at step n as follows:

  • yn+1 = yn + s1(n) + uT rn
  • s1(n+1) = h f(tn+1, yn+1)
  • rn+1 = (s1(n) - s1(n+1)) P-1 u + P-1 A P rn

where A is a rows shifting matrix (the lower left part is an identity matrix):

        [ 0 0   ...  0 0 | 0 ]
        [ ---------------+---]
        [ 1 0   ...  0 0 | 0 ]
    A = [ 0 1   ...  0 0 | 0 ]
        [       ...      | 0 ]
        [ 0 0   ...  1 0 | 0 ]
        [ 0 0   ...  0 1 | 0 ]

The P-1u vector and the P-1 A P matrix do not depend on the state, they only depend on k and therefore are precomputed once for all.

  • Field Details

  • Constructor Details

    • AdamsBashforthIntegrator

      public AdamsBashforthIntegrator(int nSteps, double minStep, double maxStep, double scalAbsoluteTolerance, double scalRelativeTolerance) throws MathIllegalArgumentException
      Build an Adams-Bashforth integrator with the given order and step control parameters.
      Parameters:
      nSteps - number of steps of the method excluding the one being computed
      minStep - minimal step (sign is irrelevant, regardless of integration direction, forward or backward), the last step can be smaller than this
      maxStep - maximal step (sign is irrelevant, regardless of integration direction, forward or backward), the last step can be smaller than this
      scalAbsoluteTolerance - allowed absolute error
      scalRelativeTolerance - allowed relative error
      Throws:
      MathIllegalArgumentException - if order is 1 or less

    • AdamsBashforthIntegrator

      public AdamsBashforthIntegrator(int nSteps, double minStep, double maxStep, double[] vecAbsoluteTolerance, double[] vecRelativeTolerance) throws IllegalArgumentException
      Build an Adams-Bashforth integrator with the given order and step control parameters.
      Parameters:
      nSteps - number of steps of the method excluding the one being computed
      minStep - minimal step (sign is irrelevant, regardless of integration direction, forward or backward), the last step can be smaller than this
      maxStep - maximal step (sign is irrelevant, regardless of integration direction, forward or backward), the last step can be smaller than this
      vecAbsoluteTolerance - allowed absolute error
      vecRelativeTolerance - allowed relative error
      Throws:
      IllegalArgumentException - if order is 1 or less

  • Method Details

    • errorEstimation

      protected double errorEstimation(double[] previousState, double predictedTime, double[] predictedState, double[] predictedScaled, RealMatrix predictedNordsieck)
      Estimate error.
      Specified by:
      errorEstimation in class AdamsIntegrator
      Parameters:
      previousState - state vector at step start
      predictedTime - time at step end
      predictedState - predicted state vector at step end
      predictedScaled - predicted value of the scaled derivatives at step end
      predictedNordsieck - predicted value of the Nordsieck vector at step end
      Returns:
      estimated normalized local discretization error

    • finalizeStep

      protected org.hipparchus.ode.nonstiff.AdamsStateInterpolator finalizeStep(double stepSize, double[] predictedState, double[] predictedScaled, Array2DRowRealMatrix predictedNordsieck, boolean isForward, ODEStateAndDerivative globalPreviousState, ODEStateAndDerivative globalCurrentState, EquationsMapper equationsMapper)
      Finalize the step.
      Specified by:
      finalizeStep in class AdamsIntegrator
      Parameters:
      stepSize - step size used in the scaled and Nordsieck arrays
      predictedState - predicted state at end of step
      predictedScaled - predicted first scaled derivative
      predictedNordsieck - predicted Nordsieck vector
      isForward - integration direction indicator
      globalPreviousState - start of the global step
      globalCurrentState - end of the global step
      equationsMapper - mapper for ODE equations primary and secondary components
      Returns:
      step interpolator