GraggBulirschStoerIntegrator.java

/*
 * Licensed to the Hipparchus project under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The Hipparchus project licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *      https://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.hipparchus.ode.nonstiff;

import org.hipparchus.exception.MathIllegalArgumentException;
import org.hipparchus.exception.MathIllegalStateException;
import org.hipparchus.ode.ExpandableODE;
import org.hipparchus.ode.LocalizedODEFormats;
import org.hipparchus.ode.ODEState;
import org.hipparchus.ode.ODEStateAndDerivative;
import org.hipparchus.util.FastMath;

/**
 * This class implements a Gragg-Bulirsch-Stoer integrator for
 * Ordinary Differential Equations.
 *
 * <p>The Gragg-Bulirsch-Stoer algorithm is one of the most efficient
 * ones currently available for smooth problems. It uses Richardson
 * extrapolation to estimate what would be the solution if the step
 * size could be decreased down to zero.</p>
 *
 * <p>
 * This method changes both the step size and the order during
 * integration, in order to minimize computation cost. It is
 * particularly well suited when a very high precision is needed. The
 * limit where this method becomes more efficient than high-order
 * embedded Runge-Kutta methods like {@link DormandPrince853Integrator
 * Dormand-Prince 8(5,3)} depends on the problem. Results given in the
 * Hairer, Norsett and Wanner book show for example that this limit
 * occurs for accuracy around 1e-6 when integrating Saltzam-Lorenz
 * equations (the authors note this problem is <i>extremely sensitive
 * to the errors in the first integration steps</i>), and around 1e-11
 * for a two dimensional celestial mechanics problems with seven
 * bodies (pleiades problem, involving quasi-collisions for which
 * <i>automatic step size control is essential</i>).
 * </p>
 *
 * <p>
 * This implementation is basically a reimplementation in Java of the
 * <a
 * href="http://www.unige.ch/math/folks/hairer/prog/nonstiff/odex.f">odex</a>
 * fortran code by E. Hairer and G. Wanner. The redistribution policy
 * for this code is available <a
 * href="http://www.unige.ch/~hairer/prog/licence.txt">here</a>, for
 * convenience, it is reproduced below.</p>
 *
 * <blockquote>
 * <p>Copyright (c) 2004, Ernst Hairer</p>
 *
 * <p>Redistribution and use in source and binary forms, with or
 * without modification, are permitted provided that the following
 * conditions are met:</p>
 * <ul>
 *  <li>Redistributions of source code must retain the above copyright
 *      notice, this list of conditions and the following disclaimer.</li>
 *  <li>Redistributions in binary form must reproduce the above copyright
 *      notice, this list of conditions and the following disclaimer in the
 *      documentation and/or other materials provided with the distribution.</li>
 * </ul>
 *
 * <p><strong>THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND
 * CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING,
 * BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
 * FOR A  PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR
 * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
 * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
 * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
 * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
 * LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
 * NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.</strong></p>
 * </blockquote>
 *
 */

public class GraggBulirschStoerIntegrator extends AdaptiveStepsizeIntegrator {

    /** Integrator method name. */
    private static final String METHOD_NAME = "Gragg-Bulirsch-Stoer";

    /** maximal order. */
    private int maxOrder;

    /** step size sequence. */
    private int[] sequence;

    /** overall cost of applying step reduction up to iteration k + 1, in number of calls. */
    private int[] costPerStep;

    /** cost per unit step. */
    private double[] costPerTimeUnit;

    /** optimal steps for each order. */
    private double[] optimalStep;

    /** extrapolation coefficients. */
    private double[][] coeff;

    /** stability check enabling parameter. */
    private boolean performTest;

    /** maximal number of checks for each iteration. */
    private int maxChecks;

    /** maximal number of iterations for which checks are performed. */
    private int maxIter;

    /** stepsize reduction factor in case of stability check failure. */
    private double stabilityReduction;

    /** first stepsize control factor. */
    private double stepControl1;

    /** second stepsize control factor. */
    private double stepControl2;

    /** third stepsize control factor. */
    private double stepControl3;

    /** fourth stepsize control factor. */
    private double stepControl4;

    /** first order control factor. */
    private double orderControl1;

    /** second order control factor. */
    private double orderControl2;

    /** use interpolation error in stepsize control. */
    private boolean useInterpolationError;

    /** interpolation order control parameter. */
    private int mudif;

    /** Simple constructor.
     * Build a Gragg-Bulirsch-Stoer integrator with the given step
     * bounds. All tuning parameters are set to their default
     * values. The default step handler does nothing.
     * @param minStep minimal step (sign is irrelevant, regardless of
     * integration direction, forward or backward), the last step can
     * be smaller than this
     * @param maxStep maximal step (sign is irrelevant, regardless of
     * integration direction, forward or backward), the last step can
     * be smaller than this
     * @param scalAbsoluteTolerance allowed absolute error
     * @param scalRelativeTolerance allowed relative error
     */
    public GraggBulirschStoerIntegrator(final double minStep, final double maxStep,
                                        final double scalAbsoluteTolerance,
                                        final double scalRelativeTolerance) {
        super(METHOD_NAME, minStep, maxStep,
              scalAbsoluteTolerance, scalRelativeTolerance);
        setStabilityCheck(true, -1, -1, -1);
        setControlFactors(-1, -1, -1, -1);
        setOrderControl(-1, -1, -1);
        setInterpolationControl(true, -1);
    }

    /** Simple constructor.
     * Build a Gragg-Bulirsch-Stoer integrator with the given step
     * bounds. All tuning parameters are set to their default
     * values. The default step handler does nothing.
     * @param minStep minimal step (must be positive even for backward
     * integration), the last step can be smaller than this
     * @param maxStep maximal step (must be positive even for backward
     * integration)
     * @param vecAbsoluteTolerance allowed absolute error
     * @param vecRelativeTolerance allowed relative error
     */
    public GraggBulirschStoerIntegrator(final double minStep, final double maxStep,
                                        final double[] vecAbsoluteTolerance,
                                        final double[] vecRelativeTolerance) {
        super(METHOD_NAME, minStep, maxStep,
              vecAbsoluteTolerance, vecRelativeTolerance);
        setStabilityCheck(true, -1, -1, -1);
        setControlFactors(-1, -1, -1, -1);
        setOrderControl(-1, -1, -1);
        setInterpolationControl(true, -1);
    }

    /** Set the stability check controls.
     * <p>The stability check is performed on the first few iterations of
     * the extrapolation scheme. If this test fails, the step is rejected
     * and the stepsize is reduced.</p>
     * <p>By default, the test is performed, at most during two
     * iterations at each step, and at most once for each of these
     * iterations. The default stepsize reduction factor is 0.5.</p>
     * @param performStabilityCheck if true, stability check will be performed,
     if false, the check will be skipped
     * @param maxNumIter maximal number of iterations for which checks are
     * performed (the number of iterations is reset to default if negative
     * or null)
     * @param maxNumChecks maximal number of checks for each iteration
     * (the number of checks is reset to default if negative or null)
     * @param stepsizeReductionFactor stepsize reduction factor in case of
     * failure (the factor is reset to default if lower than 0.0001 or
     * greater than 0.9999)
     */
    public void setStabilityCheck(final boolean performStabilityCheck,
                                  final int maxNumIter, final int maxNumChecks,
                                  final double stepsizeReductionFactor) {

        this.performTest = performStabilityCheck;
        this.maxIter     = (maxNumIter   <= 0) ? 2 : maxNumIter;
        this.maxChecks   = (maxNumChecks <= 0) ? 1 : maxNumChecks;

        if ((stepsizeReductionFactor < 0.0001) || (stepsizeReductionFactor > 0.9999)) {
            this.stabilityReduction = 0.5;
        } else {
            this.stabilityReduction = stepsizeReductionFactor;
        }

    }

    /** Set the step size control factors.

     * <p>The new step size hNew is computed from the old one h by:
     * <pre>
     * hNew = h * stepControl2 / (err/stepControl1)^(1/(2k + 1))
     * </pre>
     * <p>where err is the scaled error and k the iteration number of the
     * extrapolation scheme (counting from 0). The default values are
     * 0.65 for stepControl1 and 0.94 for stepControl2.</p>
     * <p>The step size is subject to the restriction:</p>
     * <pre>
     * stepControl3^(1/(2k + 1))/stepControl4 &lt;= hNew/h &lt;= 1/stepControl3^(1/(2k + 1))
     * </pre>
     * <p>The default values are 0.02 for stepControl3 and 4.0 for
     * stepControl4.</p>
     * @param control1 first stepsize control factor (the factor is
     * reset to default if lower than 0.0001 or greater than 0.9999)
     * @param control2 second stepsize control factor (the factor
     * is reset to default if lower than 0.0001 or greater than 0.9999)
     * @param control3 third stepsize control factor (the factor is
     * reset to default if lower than 0.0001 or greater than 0.9999)
     * @param control4 fourth stepsize control factor (the factor
     * is reset to default if lower than 1.0001 or greater than 999.9)
     */
    public void setControlFactors(final double control1, final double control2,
                                  final double control3, final double control4) {

        if ((control1 < 0.0001) || (control1 > 0.9999)) {
            this.stepControl1 = 0.65;
        } else {
            this.stepControl1 = control1;
        }

        if ((control2 < 0.0001) || (control2 > 0.9999)) {
            this.stepControl2 = 0.94;
        } else {
            this.stepControl2 = control2;
        }

        if ((control3 < 0.0001) || (control3 > 0.9999)) {
            this.stepControl3 = 0.02;
        } else {
            this.stepControl3 = control3;
        }

        if ((control4 < 1.0001) || (control4 > 999.9)) {
            this.stepControl4 = 4.0;
        } else {
            this.stepControl4 = control4;
        }

    }

    /** Set the order control parameters.
     * <p>The Gragg-Bulirsch-Stoer method changes both the step size and
     * the order during integration, in order to minimize computation
     * cost. Each extrapolation step increases the order by 2, so the
     * maximal order that will be used is always even, it is twice the
     * maximal number of columns in the extrapolation table.</p>
     * <pre>
     * order is decreased if w(k - 1) &lt;= w(k)     * orderControl1
     * order is increased if w(k)     &lt;= w(k - 1) * orderControl2
     * </pre>
     * <p>where w is the table of work per unit step for each order
     * (number of function calls divided by the step length), and k is
     * the current order.</p>
     * <p>The default maximal order after construction is 18 (i.e. the
     * maximal number of columns is 9). The default values are 0.8 for
     * orderControl1 and 0.9 for orderControl2.</p>
     * @param maximalOrder maximal order in the extrapolation table (the
     * maximal order is reset to default if order &lt;= 6 or odd)
     * @param control1 first order control factor (the factor is
     * reset to default if lower than 0.0001 or greater than 0.9999)
     * @param control2 second order control factor (the factor
     * is reset to default if lower than 0.0001 or greater than 0.9999)
     */
    public void setOrderControl(final int maximalOrder,
                                final double control1, final double control2) {

        if (maximalOrder > 6 && maximalOrder % 2 == 0) {
            this.maxOrder = maximalOrder;
        } else {
            this.maxOrder = 18;
        }

        if ((control1 < 0.0001) || (control1 > 0.9999)) {
            this.orderControl1 = 0.8;
        } else {
            this.orderControl1 = control1;
        }

        if ((control2 < 0.0001) || (control2 > 0.9999)) {
            this.orderControl2 = 0.9;
        } else {
            this.orderControl2 = control2;
        }

        // reinitialize the arrays
        initializeArrays();

    }

    /** Initialize the integrator internal arrays. */
    private void initializeArrays() {

        final int size = maxOrder / 2;

        if ((sequence == null) || (sequence.length != size)) {
            // all arrays should be reallocated with the right size
            sequence        = new int[size];
            costPerStep     = new int[size];
            coeff           = new double[size][];
            costPerTimeUnit = new double[size];
            optimalStep     = new double[size];
        }

        // step size sequence: 2, 6, 10, 14, ...
        for (int k = 0; k < size; ++k) {
            sequence[k] = 4 * k + 2;
        }

        // initialize the order selection cost array
        // (number of function calls for each column of the extrapolation table)
        costPerStep[0] = sequence[0] + 1;
        for (int k = 1; k < size; ++k) {
            costPerStep[k] = costPerStep[k - 1] + sequence[k];
        }

        // initialize the extrapolation tables
        for (int k = 0; k < size; ++k) {
            coeff[k] = (k > 0) ? new double[k] : null;
            for (int l = 0; l < k; ++l) {
                final double ratio = ((double) sequence[k]) / sequence[k - l - 1];
                coeff[k][l] = 1.0 / (ratio * ratio - 1.0);
            }
        }

    }

    /** Set the interpolation order control parameter.
     * The interpolation order for dense output is 2k - mudif + 1. The
     * default value for mudif is 4 and the interpolation error is used
     * in stepsize control by default.

     * @param useInterpolationErrorForControl if true, interpolation error is used
     * for stepsize control
     * @param mudifControlParameter interpolation order control parameter (the parameter
     * is reset to default if &lt;= 0 or &gt;= 7)
     */
    public void setInterpolationControl(final boolean useInterpolationErrorForControl,
                                        final int mudifControlParameter) {

        this.useInterpolationError = useInterpolationErrorForControl;

        if ((mudifControlParameter <= 0) || (mudifControlParameter >= 7)) {
            this.mudif = 4;
        } else {
            this.mudif = mudifControlParameter;
        }

    }

    /** Update scaling array.
     * @param y1 first state vector to use for scaling
     * @param y2 second state vector to use for scaling
     * @param scale scaling array to update (can be shorter than state)
     */
    private void rescale(final double[] y1, final double[] y2, final double[] scale) {
        final StepsizeHelper helper = getStepSizeHelper();
        for (int i = 0; i < scale.length; ++i) {
            scale[i] = helper.getTolerance(i, FastMath.max(FastMath.abs(y1[i]), FastMath.abs(y2[i])));
        }
    }

    /** Perform integration over one step using substeps of a modified
     * midpoint method.
     * @param t0 initial time
     * @param y0 initial value of the state vector at t0
     * @param step global step
     * @param k iteration number (from 0 to sequence.length - 1)
     * @param scale scaling array (can be shorter than state)
     * @param f placeholder where to put the state vector derivatives at each substep
     *          (element 0 already contains initial derivative)
     * @param yMiddle placeholder where to put the state vector at the middle of the step
     * @param yEnd placeholder where to put the state vector at the end
     * @return true if computation was done properly,
     *         false if stability check failed before end of computation
     * @exception MathIllegalStateException if the number of functions evaluations is exceeded
     * @exception MathIllegalArgumentException if arrays dimensions do not match equations settings
     */
    private boolean tryStep(final double t0, final double[] y0, final double step, final int k,
                            final double[] scale, final double[][] f,
                            final double[] yMiddle, final double[] yEnd)
        throws MathIllegalArgumentException, MathIllegalStateException {

        final int    n        = sequence[k];
        final double subStep  = step / n;
        final double subStep2 = 2 * subStep;

        // first substep
        double t = t0 + subStep;
        for (int i = 0; i < y0.length; ++i) {
            yEnd[i] = y0[i] + subStep * f[0][i];
        }
        f[1] = computeDerivatives(t, yEnd);

        // other substeps
        final double[] yTmp = y0.clone();
        for (int j = 1; j < n; ++j) {

            if (2 * j == n) {
                // save the point at the middle of the step
                System.arraycopy(yEnd, 0, yMiddle, 0, y0.length);
            }

            t += subStep;
            for (int i = 0; i < y0.length; ++i) {
                final double middle = yEnd[i];
                yEnd[i]       = yTmp[i] + subStep2 * f[j][i];
                yTmp[i]       = middle;
            }

            f[j + 1] = computeDerivatives(t, yEnd);

            // stability check
            if (performTest && (j <= maxChecks) && (k < maxIter)) {
                double initialNorm = 0.0;
                for (int l = 0; l < scale.length; ++l) {
                    final double ratio = f[0][l] / scale[l];
                    initialNorm += ratio * ratio;
                }
                double deltaNorm = 0.0;
                for (int l = 0; l < scale.length; ++l) {
                    final double ratio = (f[j + 1][l] - f[0][l]) / scale[l];
                    deltaNorm += ratio * ratio;
                }
                if (deltaNorm > 4 * FastMath.max(1.0e-15, initialNorm)) {
                    return false;
                }
            }

        }

        // correction of the last substep (at t0 + step)
        for (int i = 0; i < y0.length; ++i) {
            yEnd[i] = 0.5 * (yTmp[i] + yEnd[i] + subStep * f[n][i]);
        }

        return true;

    }

    /** Extrapolate a vector.
     * @param offset offset to use in the coefficients table
     * @param k index of the last updated point
     * @param diag working diagonal of the Aitken-Neville's
     * triangle, without the last element
     * @param last last element
     */
    private void extrapolate(final int offset, final int k,
                             final double[][] diag, final double[] last) {

        // update the diagonal
        for (int j = 1; j < k; ++j) {
            for (int i = 0; i < last.length; ++i) {
                // Aitken-Neville's recursive formula
                diag[k - j - 1][i] = diag[k - j][i] +
                                coeff[k + offset][j - 1] * (diag[k - j][i] - diag[k - j - 1][i]);
            }
        }

        // update the last element
        for (int i = 0; i < last.length; ++i) {
            // Aitken-Neville's recursive formula
            last[i] = diag[0][i] + coeff[k + offset][k - 1] * (diag[0][i] - last[i]);
        }
    }

    /** {@inheritDoc} */
    @Override
    public ODEStateAndDerivative integrate(final ExpandableODE equations,
                                           final ODEState initialState, final double finalTime)
        throws MathIllegalArgumentException, MathIllegalStateException {

        sanityChecks(initialState, finalTime);
        setStepStart(initIntegration(equations, initialState, finalTime));
        final boolean forward = finalTime > initialState.getTime();

        // create some internal working arrays
        double[]         y        = getStepStart().getCompleteState();
        final double[]   y1       = new double[y.length];
        final double[][] diagonal = new double[sequence.length - 1][];
        final double[][] y1Diag   = new double[sequence.length - 1][];
        for (int k = 0; k < sequence.length - 1; ++k) {
            diagonal[k] = new double[y.length];
            y1Diag[k]   = new double[y.length];
        }

        final double[][][] fk = new double[sequence.length][][];
        for (int k = 0; k < sequence.length; ++k) {
            fk[k] = new double[sequence[k] + 1][];
        }

        // scaled derivatives at the middle of the step $\tau$
        // (element k is $h^{k} d^{k}y(\tau)/dt^{k}$ where h is step size...)
        final double[][] yMidDots = new double[1 + 2 * sequence.length][y.length];

        // initial scaling
        final int mainSetDimension = getStepSizeHelper().getMainSetDimension();
        final double[] scale = new double[mainSetDimension];
        rescale(y, y, scale);

        // initial order selection
        final double tol    = getStepSizeHelper().getRelativeTolerance(0);
        final double log10R = FastMath.log10(FastMath.max(1.0e-10, tol));
        int targetIter = FastMath.max(1,
                                      FastMath.min(sequence.length - 2,
                                                   (int) FastMath.floor(0.5 - 0.6 * log10R)));

        double  hNew                     = 0;
        double  maxError                 = Double.MAX_VALUE;
        boolean previousRejected         = false;
        boolean firstTime                = true;
        boolean newStep                  = true;
        costPerTimeUnit[0] = 0;
        setIsLastStep(false);
        do {

            double error;
            boolean reject = false;

            if (newStep) {

                // first evaluation, at the beginning of the step
                final double[] yDot0 = getStepStart().getCompleteDerivative();
                for (int k = 0; k < sequence.length; ++k) {
                    // all sequences start from the same point, so we share the derivatives
                    fk[k][0] = yDot0;
                }

                if (firstTime) {
                    hNew = initializeStep(forward, 2 * targetIter + 1, scale,
                                          getStepStart());
                }

                newStep = false;

            }

            setStepSize(hNew);

            // step adjustment near bounds
            if (forward) {
                if (getStepStart().getTime() + getStepSize() >= finalTime) {
                    setStepSize(finalTime - getStepStart().getTime());
                }
            } else {
                if (getStepStart().getTime() + getStepSize() <= finalTime) {
                    setStepSize(finalTime - getStepStart().getTime());
                }
            }
            final double nextT = getStepStart().getTime() + getStepSize();
            setIsLastStep(forward ? (nextT >= finalTime) : (nextT <= finalTime));

            // iterate over several substep sizes
            int k = -1;
            for (boolean loop = true; loop; ) {

                ++k;

                // modified midpoint integration with the current substep
                if ( ! tryStep(getStepStart().getTime(), y, getStepSize(), k, scale, fk[k],
                               (k == 0) ? yMidDots[0] : diagonal[k - 1],
                               (k == 0) ? y1 : y1Diag[k - 1])) {

                    // the stability check failed, we reduce the global step
                    hNew   = FastMath.abs(getStepSizeHelper().filterStep(getStepSize() * stabilityReduction, forward, false));
                    reject = true;
                    loop   = false;

                } else {

                    // the substep was computed successfully
                    if (k > 0) {

                        // extrapolate the state at the end of the step
                        // using last iteration data
                        extrapolate(0, k, y1Diag, y1);
                        rescale(y, y1, scale);

                        // estimate the error at the end of the step.
                        error = 0;
                        for (int j = 0; j < mainSetDimension; ++j) {
                            final double e = FastMath.abs(y1[j] - y1Diag[0][j]) / scale[j];
                            error += e * e;
                        }
                        error = FastMath.sqrt(error / mainSetDimension);
                        if (Double.isNaN(error)) {
                            throw new MathIllegalStateException(LocalizedODEFormats.NAN_APPEARING_DURING_INTEGRATION,
                                                                nextT);
                        }

                        if ((error > 1.0e15) || ((k > 1) && (error > maxError))) {
                            // error is too big, we reduce the global step
                            hNew   = FastMath.abs(getStepSizeHelper().filterStep(getStepSize() * stabilityReduction, forward, false));
                            reject = true;
                            loop   = false;
                        } else {

                            maxError = FastMath.max(4 * error, 1.0);

                            // compute optimal stepsize for this order
                            final double exp = 1.0 / (2 * k + 1);
                            double fac = stepControl2 / FastMath.pow(error / stepControl1, exp);
                            final double pow = FastMath.pow(stepControl3, exp);
                            fac = FastMath.max(pow / stepControl4, FastMath.min(1 / pow, fac));
                            final boolean acceptSmall = k < targetIter;
                            optimalStep[k]     = FastMath.abs(getStepSizeHelper().filterStep(getStepSize() * fac, forward, acceptSmall));
                            costPerTimeUnit[k] = costPerStep[k] / optimalStep[k];

                            // check convergence
                            switch (k - targetIter) {

                                case -1 :
                                    if ((targetIter > 1) && ! previousRejected) {

                                        // check if we can stop iterations now
                                        if (error <= 1.0) {
                                            // convergence have been reached just before targetIter
                                            loop = false;
                                        } else {
                                            // estimate if there is a chance convergence will
                                            // be reached on next iteration, using the
                                            // asymptotic evolution of error
                                            final double ratio = ((double) sequence [targetIter] * sequence[targetIter + 1]) /
                                                            (sequence[0] * sequence[0]);
                                            if (error > ratio * ratio) {
                                                // we don't expect to converge on next iteration
                                                // we reject the step immediately and reduce order
                                                reject = true;
                                                loop   = false;
                                                targetIter = k;
                                                if ((targetIter > 1) &&
                                                    (costPerTimeUnit[targetIter - 1] <
                                                                    orderControl1 * costPerTimeUnit[targetIter])) {
                                                    --targetIter;
                                                }
                                                hNew = getStepSizeHelper().filterStep(optimalStep[targetIter], forward, false);
                                            }
                                        }
                                    }
                                    break;

                                case 0:
                                    if (error <= 1.0) {
                                        // convergence has been reached exactly at targetIter
                                        loop = false;
                                    } else {
                                        // estimate if there is a chance convergence will
                                        // be reached on next iteration, using the
                                        // asymptotic evolution of error
                                        final double ratio = ((double) sequence[k + 1]) / sequence[0];
                                        if (error > ratio * ratio) {
                                            // we don't expect to converge on next iteration
                                            // we reject the step immediately
                                            reject = true;
                                            loop = false;
                                            if ((targetIter > 1) &&
                                                 (costPerTimeUnit[targetIter - 1] <
                                                                 orderControl1 * costPerTimeUnit[targetIter])) {
                                                --targetIter;
                                            }
                                            hNew = getStepSizeHelper().filterStep(optimalStep[targetIter], forward, false);
                                        }
                                    }
                                    break;

                                case 1 :
                                    if (error > 1.0) {
                                        reject = true;
                                        if ((targetIter > 1) &&
                                            (costPerTimeUnit[targetIter - 1] <
                                                            orderControl1 * costPerTimeUnit[targetIter])) {
                                            --targetIter;
                                        }
                                        hNew = getStepSizeHelper().filterStep(optimalStep[targetIter], forward, false);
                                    }
                                    loop = false;
                                    break;

                                default :
                                    if ((firstTime || isLastStep()) && (error <= 1.0)) {
                                        loop = false;
                                    }
                                    break;

                            }

                        }
                    }
                }
            }

            // dense output handling
            double hInt = getMaxStep();
            final GraggBulirschStoerStateInterpolator interpolator;
            if (! reject) {

                // extrapolate state at middle point of the step
                for (int j = 1; j <= k; ++j) {
                    extrapolate(0, j, diagonal, yMidDots[0]);
                }

                final int mu = 2 * k - mudif + 3;

                for (int l = 0; l < mu; ++l) {

                    // derivative at middle point of the step
                    final int l2 = l / 2;
                    double factor = FastMath.pow(0.5 * sequence[l2], l);
                    int middleIndex = fk[l2].length / 2;
                    for (int i = 0; i < y.length; ++i) {
                        yMidDots[l + 1][i] = factor * fk[l2][middleIndex + l][i];
                    }
                    for (int j = 1; j <= k - l2; ++j) {
                        factor = FastMath.pow(0.5 * sequence[j + l2], l);
                        middleIndex = fk[l2 + j].length / 2;
                        for (int i = 0; i < y.length; ++i) {
                            diagonal[j - 1][i] = factor * fk[l2 + j][middleIndex + l][i];
                        }
                        extrapolate(l2, j, diagonal, yMidDots[l + 1]);
                    }
                    for (int i = 0; i < y.length; ++i) {
                        yMidDots[l + 1][i] *= getStepSize();
                    }

                    // compute centered differences to evaluate next derivatives
                    for (int j = (l + 1) / 2; j <= k; ++j) {
                        for (int m = fk[j].length - 1; m >= 2 * (l + 1); --m) {
                            for (int i = 0; i < y.length; ++i) {
                                fk[j][m][i] -= fk[j][m - 2][i];
                            }
                        }
                    }

                }

                // state at end of step
                final ODEStateAndDerivative stepEnd =
                    equations.getMapper().mapStateAndDerivative(nextT, y1, computeDerivatives(nextT, y1));

                // set up interpolator covering the full step
                interpolator = new GraggBulirschStoerStateInterpolator(forward,
                                                                       getStepStart(), stepEnd,
                                                                       getStepStart(), stepEnd,
                                                                       equations.getMapper(),
                                                                       yMidDots, mu);

                if (mu >= 0 && useInterpolationError) {
                    // use the interpolation error to limit stepsize
                    final double interpError = interpolator.estimateError(scale);
                    hInt = FastMath.abs(getStepSize() /
                                        FastMath.max(FastMath.pow(interpError, 1.0 / (mu + 4)), 0.01));
                    if (interpError > 10.0) {
                        hNew   = getStepSizeHelper().filterStep(hInt, forward, false);
                        reject = true;
                    }
                }

            } else {
                interpolator = null;
            }

            if (! reject) {

                // Discrete events handling
                setStepStart(acceptStep(interpolator, finalTime));

                // prepare next step
                // beware that y1 is not always valid anymore here,
                // as some event may have triggered a reset
                // so we need to copy the new step start set previously
                y = getStepStart().getCompleteState();

                int optimalIter;
                if (k == 1) {
                    optimalIter = 2;
                    if (previousRejected) {
                        optimalIter = 1;
                    }
                } else if (k <= targetIter) {
                    optimalIter = k;
                    if (costPerTimeUnit[k - 1] < orderControl1 * costPerTimeUnit[k]) {
                        optimalIter = k - 1;
                    } else if (costPerTimeUnit[k] < orderControl2 * costPerTimeUnit[k - 1]) {
                        optimalIter = FastMath.min(k + 1, sequence.length - 2);
                    }
                } else {
                    optimalIter = k - 1;
                    if ((k > 2) && (costPerTimeUnit[k - 2] < orderControl1 * costPerTimeUnit[k - 1])) {
                        optimalIter = k - 2;
                    }
                    if (costPerTimeUnit[k] < orderControl2 * costPerTimeUnit[optimalIter]) {
                        optimalIter = FastMath.min(k, sequence.length - 2);
                    }
                }

                if (previousRejected) {
                    // after a rejected step neither order nor stepsize
                    // should increase
                    targetIter = FastMath.min(optimalIter, k);
                    hNew = FastMath.min(FastMath.abs(getStepSize()), optimalStep[targetIter]);
                } else {
                    // stepsize control
                    if (optimalIter <= k) {
                        hNew = getStepSizeHelper().filterStep(optimalStep[optimalIter], forward, false);
                    } else {
                        if ((k < targetIter) &&
                                        (costPerTimeUnit[k] < orderControl2 * costPerTimeUnit[k - 1])) {
                            hNew = getStepSizeHelper().
                                   filterStep(optimalStep[k] * costPerStep[optimalIter + 1] / costPerStep[k], forward, false);
                        } else {
                            hNew = getStepSizeHelper().
                                   filterStep(optimalStep[k] * costPerStep[optimalIter] / costPerStep[k], forward, false);
                        }
                    }

                    targetIter = optimalIter;

                }

                newStep = true;

            }

            hNew = FastMath.min(hNew, hInt);
            if (! forward) {
                hNew = -hNew;
            }

            firstTime = false;

            if (reject) {
                setIsLastStep(false);
                previousRejected = true;
            } else {
                previousRejected = false;
            }

        } while (!isLastStep());

        final ODEStateAndDerivative finalState = getStepStart();
        resetInternalState();
        return finalState;

    }

}