/* * Licensed to the Apache Software Foundation (ASF) under one or more * contributor license agreements. See the NOTICE file distributed with * this work for additional information regarding copyright ownership. * The ASF 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. */ /* * This is not the original file distributed by the Apache Software Foundation * It has been modified by the Hipparchus project */ package org.hipparchus.ode; /** This class converts second order differential equations to first * order ones. * * <p>This class is a wrapper around a {@link SecondOrderODE} which * allow to use a {@link ODEIntegrator} to integrate it.</p> * * <p>The transformation is done by changing the n dimension state * vector to a 2n dimension vector, where the first n components are * the initial state variables and the n last components are their * first time derivative. The first time derivative of this state * vector then really contains both the first and second time * derivative of the initial state vector, which can be handled by the * underlying second order equations set.</p> * * <p>One should be aware that the data is duplicated during the * transformation process and that for each call to {@link * #computeDerivatives computeDerivatives}, this wrapper does copy 4n * scalars : 2n before the call to {@link * SecondOrderODE#computeSecondDerivatives * computeSecondDerivatives} in order to dispatch the y state vector * into z and zDot, and 2n after the call to gather zDot and zDDot * into yDot. Since the underlying problem by itself perhaps also * needs to copy data and dispatch the arrays into domain objects, * this has an impact on both memory and CPU usage. The only way to * avoid this duplication is to perform the transformation at the * problem level, i.e. to implement the problem as a first order one * and then avoid using this class.</p> * * @see ODEIntegrator * @see OrdinaryDifferentialEquation * @see SecondOrderODE */ public class FirstOrderConverter implements OrdinaryDifferentialEquation { /** Underlying second order equations set. */ private final SecondOrderODE equations; /** second order problem dimension. */ private final int dimension; /** Simple constructor. * Build a converter around a second order equations set. * @param equations second order equations set to convert */ public FirstOrderConverter (final SecondOrderODE equations) { this.equations = equations; dimension = equations.getDimension(); } /** {@inheritDoc} * <p>The dimension of the first order problem is twice the * dimension of the underlying second order problem.</p> * @return dimension of the problem */ @Override public int getDimension() { return 2 * dimension; } /** {@inheritDoc} */ @Override public double[] computeDerivatives(final double t, final double[] y) { final double[] yDot = new double[y.length]; // split the state vector in two final double[] z = new double[dimension]; final double[] zDot = new double[dimension]; System.arraycopy(y, 0, z, 0, dimension); System.arraycopy(y, dimension, zDot, 0, dimension); // apply the underlying equations set final double[] zDDot = equations.computeSecondDerivatives(t, z, zDot); // build the result state derivative System.arraycopy(zDot, 0, yDot, 0, dimension); System.arraycopy(zDDot, 0, yDot, dimension, dimension); return yDot; } }