Package org.hipparchus.ode.nonstiff

Class 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: 

     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-Bashforth methods can be written:
    • k = 1: yn+1 = yn + s1(n)
    • k = 2: yn+1 = yn + 3/2 s1(n) + [ -1/2 ] qn
    • k = 3: yn+1 = yn + 23/12 s1(n) + [ -16/12 5/12 ] qn
    • k = 4: yn+1 = yn + 55/24 s1(n) + [ -59/24 37/24 -9/24 ] qn
    • ...

    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: 

     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 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.

    • Constructor Detail

      • 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