Package org.apache.commons.math4.legacy.ode.nonstiff

Class AdamsMoultonFieldIntegrator<T extends RealFieldElement<T>>

  • Type Parameters:
    T - the type of the field elements
    All Implemented Interfaces:
    FirstOrderFieldIntegrator<T>
    public class AdamsMoultonFieldIntegrator<T extends RealFieldElement<T>>
extends AdamsFieldIntegrator<T>
    This class implements implicit Adams-Moulton integrators for Ordinary Differential Equations.

    Adams-Moulton methods (in fact due to Adams alone) are implicit 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+1, n, n-1 ... Since y'n+1 is needed to compute yn+1, another method must be used to compute a first estimate of yn+1, then compute y'n+1, then compute a final estimate of yn+1 using the following formulas. Depending on the number k of previous steps one wants to use for computing the next value, different formulas are available for the final estimate:

    • k = 1: yn+1 = yn + h y'n+1
    • k = 2: yn+1 = yn + h (y'n+1+y'n)/2
    • k = 3: yn+1 = yn + h (5y'n+1+8y'n-y'n-1)/12
    • k = 4: yn+1 = yn + h (9y'n+1+19y'n-5y'n-1+y'n-2)/24
    • ...

    A k-steps Adams-Moulton method is of order k+1.

    Implementation details

    We define scaled derivatives si(n) at step n as:

    s1(n) = h y'n for first derivative s2(n) = h2/2 y''n for second derivative s3(n) = h3/6 y'''n for third derivative ... sk(n) = hk/k! y(k)n for kth derivative

    The definitions above use the classical representation with several previous first derivatives. Lets define

    qn = [ s1(n-1) s1(n-2) ... s1(n-(k-1)) ]T
    (we omit the k index in the notation for clarity). With these definitions, Adams-Moulton methods can be written:
    • k = 1: yn+1 = yn + s1(n+1)
    • k = 2: yn+1 = yn + 1/2 s1(n+1) + [ 1/2 ] qn+1
    • k = 3: yn+1 = yn + 5/12 s1(n+1) + [ 8/12 -1/12 ] qn+1
    • k = 4: yn+1 = yn + 9/24 s1(n+1) + [ 19/24 -5/24 1/24 ] qn+1
    • ...

    Instead of using the classical representation with first derivatives only (yn, s1(n+1) and qn+1), 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:

    rn = [ s2(n), s3(n) ... sk(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.

    s1(n-i) = s1(n) + ∑j>0 (j+1) (-i)j sj+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:
    qn = s1(n) u + P rn
    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: 
            [  -2   3   -4    5  ... ]
        [  -4  12  -32   80  ... ]
   P =  [  -6  27 -108  405  ... ]
        [  -8  48 -256 1280  ... ]
        [          ...           ]

    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 predicted 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 ]
    From this predicted vector, the corrected vector is computed as follows:
    • yn+1 = yn + S1(n+1) + [ -1 +1 -1 +1 ... ±1 ] rn+1
    • s1(n+1) = h f(tn+1, yn+1)
    • rn+1 = Rn+1 + (s1(n+1) - S1(n+1)) P-1 u
    where the upper case Yn+1, S1(n+1) and Rn+1 represent the predicted states whereas the lower case yn+1, sn+1 and rn+1 represent the corrected states.

    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.

    Since:
    3.6

    • Constructor Detail

      • AdamsMoultonFieldIntegrator

        public AdamsMoultonFieldIntegrator​(Field<T> field,
                                   int nSteps,
                                   double minStep,
                                   double maxStep,
                                   double scalAbsoluteTolerance,
                                   double scalRelativeTolerance)
                            throws NumberIsTooSmallException
        Build an Adams-Moulton integrator with the given order and error control parameters.
        Parameters:
        field - field to which the time and state vector elements belong
        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:
        NumberIsTooSmallException - if order is 1 or less

      • AdamsMoultonFieldIntegrator

        public AdamsMoultonFieldIntegrator​(Field<T> field,
                                   int nSteps,
                                   double minStep,
                                   double maxStep,
                                   double[] vecAbsoluteTolerance,
                                   double[] vecRelativeTolerance)
                            throws IllegalArgumentException
        Build an Adams-Moulton integrator with the given order and error control parameters.
        Parameters:
        field - field to which the time and state vector elements belong
        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