Examples of org.apache.commons.math.ode.AdaptiveStepsizeIntegrator

org.apache.commons.math.ode.AdaptiveStepsizeIntegrator

This abstract class holds the common part of all adaptive stepsize integrators for Ordinary Differential Equations.

These algorithms perform integration with stepsize control, which means the user does not specify the integration step but rather a tolerance on error. The error threshold is computed as

 threshold_i = absTol_i + relTol_i * max (abs (ym), abs (ym+1))

where absTol_i is the absolute tolerance for component i of the state vector and relTol_i is the relative tolerance for the same component. The user can also use only two scalar values absTol and relTol which will be used for all components.

If the estimated error for ym+1 is such that

 sqrt((sum (errEst_i / threshold_i)^2 ) / n) < 1

(where n is the state vector dimension) then the step is accepted, otherwise the step is rejected and a new attempt is made with a new stepsize.

@version $Revision: 620312 $ $Date: 2008-02-10 12:28:59 -0700 (Sun, 10 Feb 2008) $ @since 1.2

    double minStep = 1.25;
    double maxStep = Math.abs(pb.getFinalTime() - pb.getInitialTime());
    double scalAbsoluteTolerance = 6.0e-4;
    double scalRelativeTolerance = 6.0e-4;


    AdaptiveStepsizeIntegrator integ =
      new DormandPrince54Integrator(minStep, maxStep,
                                    scalAbsoluteTolerance,
                                    scalRelativeTolerance);


    DP54SmallLastHandler handler = new DP54SmallLastHandler(minStep);
    integ.setStepHandler(handler);
    integ.setInitialStepSize(1.7);
    integ.integrate(pb,
                    pb.getInitialTime(), pb.getInitialState(),
                    pb.getFinalTime(), new double[pb.getDimension()]);
    assertTrue(handler.wasLastSeen());
    assertEquals("Dormand-Prince 5(4)", integ.getName());


  }

Examples of org.apache.commons.math.ode.AdaptiveStepsizeIntegrator

Related Classes of org.apache.commons.math.ode.AdaptiveStepsizeIntegrator