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## org.apache.commons.math.ode
Class MultistepIntegrator

    java.lang.Object
      org.apache.commons.math.ode.AbstractIntegrator
          org.apache.commons.math.ode.nonstiff.AdaptiveStepsizeIntegrator
              org.apache.commons.math.ode.MultistepIntegrator

All Implemented Interfaces:
  FirstOrderIntegrator, ODEIntegrator

Direct Known Subclasses:
  AdamsIntegrator

    public abstract class MultistepIntegrator

extends AdaptiveStepsizeIntegrator

This class is the base class for multistep integrators for Ordinary Differential Equations.

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

Rather than storing several previous steps separately, this 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

(we omit the k index in the notation for clarity)

Multistep integrators with Nordsieck representation are highly sensitive to large step changes because when the step is multiplied by a factor a, the kth component of the Nordsieck vector is multiplied by ak and the last components are the least accurate ones. The default max growth factor is therefore set to a quite low value: 21/order.

Since:
  2.0
Version:
  $Revision: 1073158 $ $Date: 2011-02-21 22:46:52 +0100 (lun. 21 fÃ©vr. 2011) $
See Also:
  `AdamsBashforthIntegrator`, `AdamsMoultonIntegrator`

Nested Class Summary
`static interface` | `MultistepIntegrator.NordsieckTransformer` Transformer used to convert the first step to Nordsieck representation.

Field Summary
`protected Array2DRowRealMatrix` | `nordsieck` Nordsieck matrix of the higher scaled derivatives.
`protected double[]` | `scaled` First scaled derivative (h y').

Fields inherited from class org.apache.commons.math.ode.nonstiff.AdaptiveStepsizeIntegrator
`mainSetDimension, scalAbsoluteTolerance, scalRelativeTolerance, vecAbsoluteTolerance, vecRelativeTolerance`

Fields inherited from class org.apache.commons.math.ode.AbstractIntegrator
`isLastStep, resetOccurred, stepHandlers, stepSize, stepStart`

Constructor Summary
`protected` | `MultistepIntegrator(String name, int nSteps, int order, double minStep, double maxStep, double[] vecAbsoluteTolerance, double[] vecRelativeTolerance)` Build a multistep integrator with the given stepsize bounds.
`protected` | `MultistepIntegrator(String name, int nSteps, int order, double minStep, double maxStep, double scalAbsoluteTolerance, double scalRelativeTolerance)` Build a multistep integrator with the given stepsize bounds.

Method Summary
`protected double` | `computeStepGrowShrinkFactor(double error)` Compute step grow/shrink factor according to normalized error.
`double` | `getMaxGrowth()` Get the maximal growth factor for stepsize control.
`double` | `getMinReduction()` Get the minimal reduction factor for stepsize control.
`double` | `getSafety()` Get the safety factor for stepsize control.
`ODEIntegrator` | `getStarterIntegrator()` Get the starter integrator.
`protected abstract Array2DRowRealMatrix` | `initializeHighOrderDerivatives(double[] first, double[][] multistep)` Initialize the high order scaled derivatives at step start.
`void` | `setMaxGrowth(double maxGrowth)` Set the maximal growth factor for stepsize control.
`void` | `setMinReduction(double minReduction)` Set the minimal reduction factor for stepsize control.
`void` | `setSafety(double safety)` Set the safety factor for stepsize control.
`void` | `setStarterIntegrator(FirstOrderIntegrator starterIntegrator)` Set the starter integrator.
`protected void` | `start(double t0, double[] y0, double t)` Start the integration.

Methods inherited from class org.apache.commons.math.ode.nonstiff.AdaptiveStepsizeIntegrator
`filterStep, getCurrentStepStart, getMaxStep, getMinStep, initializeStep, integrate, resetInternalState, sanityChecks, setInitialStepSize`

Methods inherited from class org.apache.commons.math.ode.AbstractIntegrator
`acceptStep, addEndTimeChecker, addEventHandler, addStepHandler, clearEventHandlers, clearStepHandlers, computeDerivatives, getCurrentSignedStepsize, getEvaluations, getEventHandlers, getMaxEvaluations, getName, getStepHandlers, requiresDenseOutput, resetEvaluations, setEquations, setMaxEvaluations, setStateInitialized`

Methods inherited from class java.lang.Object
`clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

Field Detail

### scaled

    protected double[] scaled

  First scaled derivative (h y').

### nordsieck

    protected Array2DRowRealMatrix nordsieck

  Nordsieck matrix of the higher scaled derivatives.

  (h2/2 y'', h3/6 y''' ..., hk/k! y(k))

Constructor Detail

### MultistepIntegrator

    protected MultistepIntegrator(String name,
                                  int nSteps,
                                  int order,
                                  double minStep,
                                  double maxStep,
                                  double scalAbsoluteTolerance,
                                  double scalRelativeTolerance)

  Build a multistep integrator with the given stepsize bounds.

  The default starter integrator is set to the `Dormand-Prince 8(5,3)` integrator with some defaults settings.

  The default max growth factor is set to a quite low value: 21/order.

  Parameters:
    `name` - name of the method
    `nSteps` - number of steps of the multistep method (excluding the one being computed)
    `order` - order of the method
    `minStep` - minimal step (must be positive even for backward integration), the last step can be smaller than this
    `maxStep` - maximal step (must be positive even for backward integration)
    `scalAbsoluteTolerance` - allowed absolute error
    `scalRelativeTolerance` - allowed relative error

### MultistepIntegrator

    protected MultistepIntegrator(String name,
                                  int nSteps,
                                  int order,
                                  double minStep,
                                  double maxStep,
                                  double[] vecAbsoluteTolerance,
                                  double[] vecRelativeTolerance)

  Build a multistep integrator with the given stepsize bounds.

  The default starter integrator is set to the `Dormand-Prince 8(5,3)` integrator with some defaults settings.

  The default max growth factor is set to a quite low value: 21/order.

  Parameters:
    `name` - name of the method
    `nSteps` - number of steps of the multistep method (excluding the one being computed)
    `order` - order of the method
    `minStep` - minimal step (must be positive even for backward integration), the last step can be smaller than this
    `maxStep` - maximal step (must be positive even for backward integration)
    `vecAbsoluteTolerance` - allowed absolute error
    `vecRelativeTolerance` - allowed relative error

Method Detail

### getStarterIntegrator

    public ODEIntegrator getStarterIntegrator()

  Get the starter integrator.

  Returns:
    starter integrator

### setStarterIntegrator

    public void setStarterIntegrator(FirstOrderIntegrator starterIntegrator)

  Set the starter integrator.

  The various step and event handlers for this starter integrator will be managed automatically by the multi-step integrator. Any user configuration for these elements will be cleared before use.

  Parameters:
    `starterIntegrator` - starter integrator

### start

    protected void start(double t0,
                         double[] y0,
                         double t)
                  throws DerivativeException,
                         IntegratorException

  Start the integration.

  This method computes one step using the underlying starter integrator, and initializes the Nordsieck vector at step start. The starter integrator purpose is only to establish initial conditions, it does not really change time by itself. The top level multistep integrator remains in charge of handling time propagation and events handling as it will starts its own computation right from the beginning. In a sense, the starter integrator can be seen as a dummy one and so it will never trigger any user event nor call any user step handler.

  Parameters:
    `t0` - initial time
    `y0` - initial value of the state vector at t0
    `t` - target time for the integration (can be set to a value smaller than `t0` for backward integration)
  Throws:
    `IntegratorException` - if the integrator cannot perform integration
    `DerivativeException` - this exception is propagated to the caller if the underlying user function triggers one

### initializeHighOrderDerivatives

    protected abstract Array2DRowRealMatrix initializeHighOrderDerivatives(double[] first,
                                                                           double[][] multistep)

  Initialize the high order scaled derivatives at step start.

  Parameters:
    `first` - first scaled derivative at step start
    `multistep` - scaled derivatives after step start (hy'1, ..., hy'k-1) will be modified
  Returns:
    high order scaled derivatives at step start

### getMinReduction

    public double getMinReduction()

  Get the minimal reduction factor for stepsize control.

  Returns:
    minimal reduction factor

### setMinReduction

    public void setMinReduction(double minReduction)

  Set the minimal reduction factor for stepsize control.

  Parameters:
    `minReduction` - minimal reduction factor

### getMaxGrowth

    public double getMaxGrowth()

  Get the maximal growth factor for stepsize control.

  Returns:
    maximal growth factor

### setMaxGrowth

    public void setMaxGrowth(double maxGrowth)

  Set the maximal growth factor for stepsize control.

  Parameters:
    `maxGrowth` - maximal growth factor

### getSafety

    public double getSafety()

  Get the safety factor for stepsize control.

  Returns:
    safety factor

### setSafety

    public void setSafety(double safety)

  Set the safety factor for stepsize control.

  Parameters:
    `safety` - safety factor

### computeStepGrowShrinkFactor

    protected double computeStepGrowShrinkFactor(double error)

  Compute step grow/shrink factor according to normalized error.

  Parameters:
    `error` - normalized error of the current step
  Returns:
    grow/shrink factor for next step

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