Examples of org.apache.commons.math3.ode.nonstiff.DormandPrince853Integrator

Package org.apache.commons.math3.ode.nonstiff

Examples of org.apache.commons.math3.ode.nonstiff.DormandPrince853Integrator

org.apache.commons.math3.ode.nonstiff.DormandPrince853Integrator
This class implements the 8(5,3) Dormand-Prince integrator for Ordinary Differential Equations.
This integrator is an embedded Runge-Kutta integrator of order 8(5,3) used in local extrapolation mode (i.e. the solution is computed using the high order formula) with stepsize control (and automatic step initialization) and continuous output. This method uses 12 functions evaluations per step for integration and 4 evaluations for interpolation. However, since the first interpolation evaluation is the same as the first integration evaluation of the next step, we have included it in the integrator rather than in the interpolator and specified the method was an fsal. Hence, despite we have 13 stages here, the cost is really 12 evaluations per step even if no interpolation is done, and the overcost of interpolation is only 3 evaluations.

This method is based on an 8(6) method by Dormand and Prince (i.e. order 8 for the integration and order 6 for error estimation) modified by Hairer and Wanner to use a 5th order error estimator with 3rd order correction. This modification was introduced because the original method failed in some cases (wrong steps can be accepted when step size is too large, for example in the Brusselator problem) and also had severe difficulties when applied to problems with discontinuities. This modification is explained in the second edition of the first volume (Nonstiff Problems) of the reference book by Hairer, Norsett and Wanner: Solving Ordinary Differential Equations (Springer-Verlag, ISBN 3-540-56670-8).
@since 1.2

     * @param matrix matrix with columns representing variables to correlate
     * @return correlation matrix
     */
    public RealMatrix computeCorrelationMatrix(final RealMatrix matrix) {
        int nVars = matrix.getColumnDimension();
        RealMatrix outMatrix = new BlockRealMatrix(nVars, nVars);
        for (int i = 0; i < nVars; i++) {
            for (int j = 0; j < i; j++) {
                double corr = correlation(matrix.getColumn(i), matrix.getColumn(j));
                outMatrix.setEntry(i, j, corr);
                outMatrix.setEntry(j, i, corr);
            }
            outMatrix.setEntry(i, i, 1d);
        }
        return outMatrix;
    }

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        // solve the rectangular system in the least square sense
        // to get the best estimate of the Nordsieck vector [s2 ... sk]
        QRDecomposition decomposition;
        decomposition = new QRDecomposition(new Array2DRowRealMatrix(a, false));
        RealMatrix x = decomposition.getSolver().solve(new Array2DRowRealMatrix(b, false));
        return new Array2DRowRealMatrix(x.getData(), false);
    }

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    for (SiteWithPolynomial site : sites) {
      
      List<SiteWithPolynomial> nearestSites =
          nearestSiteMap.get(site);
      
      RealVector vector = new ArrayRealVector(SITES_FOR_APPROX);
      RealMatrix matrix = new Array2DRowRealMatrix(
          SITES_FOR_APPROX, DefaultPolynomial.NUM_COEFFS);
      
      for (int row = 0; row < SITES_FOR_APPROX; row++) {
        SiteWithPolynomial nearSite = nearestSites.get(row);
        DefaultPolynomial.populateMatrix(matrix, row, nearSite.pos.x, nearSite.pos.z);
        vector.setEntry(row, nearSite.pos.y);
      }
      
      QRDecomposition qr = new QRDecomposition(matrix);
      RealVector solution = qr.getSolver().solve(vector);
        
      double[] coeffs = solution.toArray();
      
      for (double coeff : coeffs) {
        if (coeff > 10e3) {
          continue calculatePolynomials;
        }

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                return Double.compare(weightedResidual(o1),
                                      weightedResidual(o2));
            }


            private double weightedResidual(final PointVectorValuePair pv) {
                final RealVector v = new ArrayRealVector(pv.getValueRef(), false);
                final RealVector r = target.subtract(v);
                return r.dotProduct(weight.operate(r));
            }
        };
    }

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            throw new NumberIsTooSmallException(
                  LocalizedFormats.INTEGRATION_METHOD_NEEDS_AT_LEAST_TWO_PREVIOUS_POINTS,
                  nSteps, 2, true);
        }


        starter = new DormandPrince853Integrator(minStep, maxStep,
                                                 scalAbsoluteTolerance,
                                                 scalRelativeTolerance);
        this.nSteps = nSteps;


        exp = -1.0 / order;

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                                  final int order,
                                  final double minStep, final double maxStep,
                                  final double[] vecAbsoluteTolerance,
                                  final double[] vecRelativeTolerance) {
        super(name, minStep, maxStep, vecAbsoluteTolerance, vecRelativeTolerance);
        starter = new DormandPrince853Integrator(minStep, maxStep,
                                                 vecAbsoluteTolerance,
                                                 vecRelativeTolerance);
        this.nSteps = nSteps;


        exp = -1.0 / order;

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          };


      // integrate backward from &pi; to 0;
      ContinuousOutputModel cm1 = new ContinuousOutputModel();
      FirstOrderIntegrator integ1 =
          new DormandPrince853Integrator(0, 1.0, 1.0e-8, 1.0e-8);
      integ1.addStepHandler(cm1);
      integ1.integrate(problem, FastMath.PI, new double[] { -1.0, 0.0 },
                       0, new double[2]);


      // integrate backward from 2&pi; to &pi;
      ContinuousOutputModel cm2 = new ContinuousOutputModel();
      FirstOrderIntegrator integ2 =
          new DormandPrince853Integrator(0, 0.1, 1.0e-12, 1.0e-12);
      integ2.addStepHandler(cm2);
      integ2.integrate(problem, 2.0 * FastMath.PI, new double[] { 1.0, 0.0 },
                       FastMath.PI, new double[2]);


      // merge the two half circles
      ContinuousOutputModel cm = new ContinuousOutputModel();
      cm.append(cm2);

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            throw new MathIllegalArgumentException(
                  LocalizedFormats.INTEGRATION_METHOD_NEEDS_AT_LEAST_TWO_PREVIOUS_POINTS,
                  name);
        }


        starter = new DormandPrince853Integrator(minStep, maxStep,
                                                 scalAbsoluteTolerance,
                                                 scalRelativeTolerance);
        this.nSteps = nSteps;


        exp = -1.0 / order;

View Full Code Here

                                  final int order,
                                  final double minStep, final double maxStep,
                                  final double[] vecAbsoluteTolerance,
                                  final double[] vecRelativeTolerance) {
        super(name, minStep, maxStep, vecAbsoluteTolerance, vecRelativeTolerance);
        starter = new DormandPrince853Integrator(minStep, maxStep,
                                                 vecAbsoluteTolerance,
                                                 vecRelativeTolerance);
        this.nSteps = nSteps;


        exp = -1.0 / order;

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        throws DimensionMismatchException, NumberIsTooSmallException,
               MaxCountExceededException, NoBracketingException {
        double e = 1e-15;
        FirstOrderIntegrator integrator;
        integrator = (integratorType == 1)
                     ? new DormandPrince853Integrator(e, 100.0, 1e-7, 1e-7)
                     : new GraggBulirschStoerIntegrator(e, 100.0, 1e-7, 1e-7);
        PegasusSolver rootSolver = new PegasusSolver(e, e);
        integrator.addEventHandler(new Event(), 0.1, e, 1000, rootSolver);
        double t0 = 6.0;
        double tEnd = 10.0;

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