/*
 * 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
 *
 *      http://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 package provides classes to solve Ordinary Differential Equations problems.

This package solves Initial Value Problems of the form y'=f(t,y) with t0 and y(t0)=y0 known. The provided integrators compute an estimate of y(t) from t=t0 to t=t1. It is also possible to get thederivatives with respect to the initial state dy(t)/dy(t0) or the derivatives with respect to some ODE parameters dy(t)/dp.

All integrators provide dense output. This means that besides computing the state vector at discrete times, they also provide a cheap mean to get the state between the time steps. They do so through classes extending the StepInterpolator abstract class, which are made available to the user at the end of each step.

All integrators handle multiple discrete events detection based on switching functions. This means that the integrator can be driven by user specified discrete events. The steps are shortened as needed to ensure the events occur at step boundaries (even if the integrator is a fixed-step integrator). When the events are triggered, integration can be stopped (this is called a G-stop facility), the state vector can be changed, or integration can simply go on. The latter case is useful to handle discontinuities in the differential equations gracefully and get accurate dense output even close to the discontinuity.

The user should describe his problem in his own classes (UserProblem in the diagram below) which should implement the FirstOrderDifferentialEquations interface. Then he should pass it to the integrator he prefers among all the classes that implement the FirstOrderIntegrator interface.

The solution of the integration problem is provided by two means. The first one is aimed towards simple use: the state vector at the end of the integration process is copied in the y array of the FirstOrderIntegrator.integrate method. The second one should be used when more in-depth information is needed throughout the integration process. The user can register an object implementing the StepHandler interface or a StepNormalizer object wrapping a user-specified object implementing the FixedStepHandler interface into the integrator before calling the FirstOrderIntegrator.integrate method. The user object will be called appropriately during the integration process, allowing the user to process intermediate results. The default step handler does nothing.

ContinuousOutputModel is a special-purpose step handler that is able to store all steps and to provide transparent access to any intermediate result once the integration is over. An important feature of this class is that it implements the Serializable interface. This means that a complete continuous model of the integrated function throughout the integration range can be serialized and reused later (if stored into a persistent medium like a filesystem or a database) or elsewhere (if sent to another application). Only the result of the integration is stored, there is no reference to the integrated problem by itself.

Other default implementations of the StepHandler interface are available for general needs (DummyStepHandler, StepNormalizer) and custom implementations can be developed for specific needs. As an example, if an application is to be completely driven by the integration process, then most of the application code will be run inside a step handler specific to this application.

Some integrators (the simple ones) use fixed steps that are set at creation time. The more efficient integrators use variable steps that are handled internally in order to control the integration error with respect to a specified accuracy (these integrators extend the AdaptiveStepsizeIntegrator abstract class). In this case, the step handler which is called after each successful step shows up the variable stepsize. The StepNormalizer class can be used to convert the variable stepsize into a fixed stepsize that can be handled by classes implementing the FixedStepHandler interface. Adaptive stepsize integrators can automatically compute the initial stepsize by themselves, however the user can specify it if he prefers to retain full control over the integration or if the automatic guess is wrong.

Fixed Step Integrators
NameOrder
Euler1
Midpoint2
Classical Runge-Kutta4
Gill4
3/84
Luther6

Adaptive Stepsize Integrators
NameIntegration OrderError Estimation Order
Higham and Hall54
Dormand-Prince 5(4)54
Dormand-Prince 8(5,3)85 and 3
Gragg-Bulirsch-Stoervariable (up to 18 by default)variable
Adams-Bashforthvariablevariable
Adams-Moultonvariablevariable

In the table above, the Adams-Bashforth and Adams-Moulton integrators appear as variable-step ones. This is an experimental extension to the classical algorithms using the Nordsieck vector representation.

/** * * <p> * This package provides classes to solve Ordinary Differential Equations problems. * </p> * * <p> * This package solves Initial Value Problems of the form * <code>y'=f(t,y)</code> with <code>t<sub>0</sub></code> and * <code>y(t<sub>0</sub>)=y<sub>0</sub></code> known. The provided * integrators compute an estimate of <code>y(t)</code> from * <code>t=t<sub>0</sub></code> to <code>t=t<sub>1</sub></code>. * It is also possible to get thederivatives with respect to the initial state * <code>dy(t)/dy(t<sub>0</sub>)</code> or the derivatives with * respect to some ODE parameters <code>dy(t)/dp</code>. * </p> * * <p> * All integrators provide dense output. This means that besides * computing the state vector at discrete times, they also provide a * cheap mean to get the state between the time steps. They do so through * classes extending the {@link * org.apache.commons.math3.ode.sampling.StepInterpolator StepInterpolator} * abstract class, which are made available to the user at the end of * each step. * </p> * * <p> * All integrators handle multiple discrete events detection based on switching * functions. This means that the integrator can be driven by user specified * discrete events. The steps are shortened as needed to ensure the events occur * at step boundaries (even if the integrator is a fixed-step * integrator). When the events are triggered, integration can be stopped * (this is called a G-stop facility), the state vector can be changed, * or integration can simply go on. The latter case is useful to handle * discontinuities in the differential equations gracefully and get * accurate dense output even close to the discontinuity. * </p> * * <p> * The user should describe his problem in his own classes * (<code>UserProblem</code> in the diagram below) which should implement * the {@link org.apache.commons.math3.ode.FirstOrderDifferentialEquations * FirstOrderDifferentialEquations} interface. Then he should pass it to * the integrator he prefers among all the classes that implement the * {@link org.apache.commons.math3.ode.FirstOrderIntegrator * FirstOrderIntegrator} interface. * </p> * * <p> * The solution of the integration problem is provided by two means. The * first one is aimed towards simple use: the state vector at the end of * the integration process is copied in the <code>y</code> array of the * {@link org.apache.commons.math3.ode.FirstOrderIntegrator#integrate * FirstOrderIntegrator.integrate} method. The second one should be used * when more in-depth information is needed throughout the integration * process. The user can register an object implementing the {@link * org.apache.commons.math3.ode.sampling.StepHandler StepHandler} interface or a * {@link org.apache.commons.math3.ode.sampling.StepNormalizer StepNormalizer} * object wrapping a user-specified object implementing the {@link * org.apache.commons.math3.ode.sampling.FixedStepHandler FixedStepHandler} * interface into the integrator before calling the {@link * org.apache.commons.math3.ode.FirstOrderIntegrator#integrate * FirstOrderIntegrator.integrate} method. The user object will be called * appropriately during the integration process, allowing the user to * process intermediate results. The default step handler does nothing. * </p> * * <p> * {@link org.apache.commons.math3.ode.ContinuousOutputModel * ContinuousOutputModel} is a special-purpose step handler that is able * to store all steps and to provide transparent access to any * intermediate result once the integration is over. An important feature * of this class is that it implements the <code>Serializable</code> * interface. This means that a complete continuous model of the * integrated function throughout the integration range can be serialized * and reused later (if stored into a persistent medium like a filesystem * or a database) or elsewhere (if sent to another application). Only the * result of the integration is stored, there is no reference to the * integrated problem by itself. * </p> * * <p> * Other default implementations of the {@link * org.apache.commons.math3.ode.sampling.StepHandler StepHandler} interface are * available for general needs ({@link * org.apache.commons.math3.ode.sampling.DummyStepHandler DummyStepHandler}, {@link * org.apache.commons.math3.ode.sampling.StepNormalizer StepNormalizer}) and custom * implementations can be developed for specific needs. As an example, * if an application is to be completely driven by the integration * process, then most of the application code will be run inside a step * handler specific to this application. * </p> * * <p> * Some integrators (the simple ones) use fixed steps that are set at * creation time. The more efficient integrators use variable steps that * are handled internally in order to control the integration error with * respect to a specified accuracy (these integrators extend the {@link * org.apache.commons.math3.ode.nonstiff.AdaptiveStepsizeIntegrator * AdaptiveStepsizeIntegrator} abstract class). In this case, the step * handler which is called after each successful step shows up the * variable stepsize. The {@link * org.apache.commons.math3.ode.sampling.StepNormalizer StepNormalizer} class can * be used to convert the variable stepsize into a fixed stepsize that * can be handled by classes implementing the {@link * org.apache.commons.math3.ode.sampling.FixedStepHandler FixedStepHandler} * interface. Adaptive stepsize integrators can automatically compute the * initial stepsize by themselves, however the user can specify it if he * prefers to retain full control over the integration or if the * automatic guess is wrong. * </p> * * <p> * <table border="1" align="center"> * <tr BGCOLOR="#CCCCFF"><td colspan=2><font size="+2">Fixed Step Integrators</font></td></tr> * <tr BGCOLOR="#EEEEFF"><font size="+1"><td>Name</td><td>Order</td></font></tr> * <tr><td>{@link org.apache.commons.math3.ode.nonstiff.EulerIntegrator Euler}</td><td>1</td></tr> * <tr><td>{@link org.apache.commons.math3.ode.nonstiff.MidpointIntegrator Midpoint}</td><td>2</td></tr> * <tr><td>{@link org.apache.commons.math3.ode.nonstiff.ClassicalRungeKuttaIntegrator Classical Runge-Kutta}</td><td>4</td></tr> * <tr><td>{@link org.apache.commons.math3.ode.nonstiff.GillIntegrator Gill}</td><td>4</td></tr> * <tr><td>{@link org.apache.commons.math3.ode.nonstiff.ThreeEighthesIntegrator 3/8}</td><td>4</td></tr> * <tr><td>{@link org.apache.commons.math3.ode.nonstiff.LutherIntegrator Luther}</td><td>6</td></tr> * </table> * </p> * * <table border="1" align="center"> * <tr BGCOLOR="#CCCCFF"><td colspan=3><font size="+2">Adaptive Stepsize Integrators</font></td></tr> * <tr BGCOLOR="#EEEEFF"><font size="+1"><td>Name</td><td>Integration Order</td><td>Error Estimation Order</td></font></tr> * <tr><td>{@link org.apache.commons.math3.ode.nonstiff.HighamHall54Integrator Higham and Hall}</td><td>5</td><td>4</td></tr> * <tr><td>{@link org.apache.commons.math3.ode.nonstiff.DormandPrince54Integrator Dormand-Prince 5(4)}</td><td>5</td><td>4</td></tr> * <tr><td>{@link org.apache.commons.math3.ode.nonstiff.DormandPrince853Integrator Dormand-Prince 8(5,3)}</td><td>8</td><td>5 and 3</td></tr> * <tr><td>{@link org.apache.commons.math3.ode.nonstiff.GraggBulirschStoerIntegrator Gragg-Bulirsch-Stoer}</td><td>variable (up to 18 by default)</td><td>variable</td></tr> * <tr><td>{@link org.apache.commons.math3.ode.nonstiff.AdamsBashforthIntegrator Adams-Bashforth}</td><td>variable</td><td>variable</td></tr> * <tr><td>{@link org.apache.commons.math3.ode.nonstiff.AdamsMoultonIntegrator Adams-Moulton}</td><td>variable</td><td>variable</td></tr> * </table> * </p> * * <p> * In the table above, the {@link org.apache.commons.math3.ode.nonstiff.AdamsBashforthIntegrator * Adams-Bashforth} and {@link org.apache.commons.math3.ode.nonstiff.AdamsMoultonIntegrator * Adams-Moulton} integrators appear as variable-step ones. This is an experimental extension * to the classical algorithms using the Nordsieck vector representation. * </p> * * */
package org.apache.commons.math3.ode;