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1   /*
2    * Licensed to the Hipparchus project under one or more
3    * contributor license agreements.  See the NOTICE file distributed with
4    * this work for additional information regarding copyright ownership.
5    * The Hipparchus project licenses this file to You under the Apache License, Version 2.0
6    * (the "License"); you may not use this file except in compliance with
7    * the License.  You may obtain a copy of the License at
8    *
9    *      https://www.apache.org/licenses/LICENSE-2.0
10   *
11   * Unless required by applicable law or agreed to in writing, software
12   * distributed under the License is distributed on an "AS IS" BASIS,
13   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14   * See the License for the specific language governing permissions and
15   * limitations under the License.
16   */
17  
18  package org.hipparchus.ode.nonstiff;
19  
20  import org.hipparchus.exception.MathIllegalArgumentException;
21  import org.hipparchus.linear.Array2DRowRealMatrix;
22  import org.hipparchus.linear.RealMatrix;
23  import org.hipparchus.ode.EquationsMapper;
24  import org.hipparchus.ode.ODEStateAndDerivative;
25  import org.hipparchus.util.FastMath;
26  
27  
28  /**
29   * This class implements explicit Adams-Bashforth integrators for Ordinary
30   * Differential Equations.
31   *
32   * <p>Adams-Bashforth methods (in fact due to Adams alone) are explicit
33   * multistep ODE solvers. This implementation is a variation of the classical
34   * one: it uses adaptive stepsize to implement error control, whereas
35   * classical implementations are fixed step size. The value of state vector
36   * at step n+1 is a simple combination of the value at step n and of the
37   * derivatives at steps n, n-1, n-2 ... Depending on the number k of previous
38   * steps one wants to use for computing the next value, different formulas
39   * are available:</p>
40   * <ul>
41   *   <li>k = 1: y<sub>n+1</sub> = y<sub>n</sub> + h y'<sub>n</sub></li>
42   *   <li>k = 2: y<sub>n+1</sub> = y<sub>n</sub> + h (3y'<sub>n</sub>-y'<sub>n-1</sub>)/2</li>
43   *   <li>k = 3: y<sub>n+1</sub> = y<sub>n</sub> + h (23y'<sub>n</sub>-16y'<sub>n-1</sub>+5y'<sub>n-2</sub>)/12</li>
44   *   <li>k = 4: y<sub>n+1</sub> = y<sub>n</sub> + h (55y'<sub>n</sub>-59y'<sub>n-1</sub>+37y'<sub>n-2</sub>-9y'<sub>n-3</sub>)/24</li>
45   *   <li>...</li>
46   * </ul>
47   *
48   * <p>A k-steps Adams-Bashforth method is of order k.</p>
49   *
50   * <p> There must be sufficient time for the {@link #setStarterIntegrator(org.hipparchus.ode.ODEIntegrator)
51   * starter integrator} to take several steps between the the last reset event, and the end
52   * of integration, otherwise an exception may be thrown during integration. The user can
53   * adjust the end date of integration, or the step size of the starter integrator to
54   * ensure a sufficient number of steps can be completed before the end of integration.
55   * </p>
56   *
57   * <p><strong>Implementation details</strong></p>
58   *
59   * <p>We define scaled derivatives s<sub>i</sub>(n) at step n as:
60   * \[
61   *   \left\{\begin{align}
62   *   s_1(n) &amp;= h y'_n \text{ for first derivative}\\
63   *   s_2(n) &amp;= \frac{h^2}{2} y_n'' \text{ for second derivative}\\
64   *   s_3(n) &amp;= \frac{h^3}{6} y_n''' \text{ for third derivative}\\
65   *   &amp;\cdots\\
66   *   s_k(n) &amp;= \frac{h^k}{k!} y_n^{(k)} \text{ for } k^\mathrm{th} \text{ derivative}
67   *   \end{align}\right.
68   * \]</p>
69   *
70   * <p>The definitions above use the classical representation with several previous first
71   * derivatives. Lets define
72   * \[
73   *   q_n = [ s_1(n-1) s_1(n-2) \ldots s_1(n-(k-1)) ]^T
74   * \]
75   * (we omit the k index in the notation for clarity). With these definitions,
76   * Adams-Bashforth methods can be written:</p>
77   * \[
78   *   \left\{\begin{align}
79   *   k = 1: &amp; y_{n+1} = y_n +               s_1(n) \\
80   *   k = 2: &amp; y_{n+1} = y_n + \frac{3}{2}   s_1(n) + [ \frac{-1}{2} ] q_n \\
81   *   k = 3: &amp; y_{n+1} = y_n + \frac{23}{12} s_1(n) + [ \frac{-16}{12} \frac{5}{12} ] q_n \\
82   *   k = 4: &amp; y_{n+1} = y_n + \frac{55}{24} s_1(n) + [ \frac{-59}{24} \frac{37}{24} \frac{-9}{24} ] q_n \\
83   *          &amp; \cdots
84   *   \end{align}\right.
85   * \]
86   *
87   * <p>Instead of using the classical representation with first derivatives only (y<sub>n</sub>,
88   * s<sub>1</sub>(n) and q<sub>n</sub>), our implementation uses the Nordsieck vector with
89   * higher degrees scaled derivatives all taken at the same step (y<sub>n</sub>, s<sub>1</sub>(n)
90   * and r<sub>n</sub>) where r<sub>n</sub> is defined as:
91   * \[
92   * r_n = [ s_2(n), s_3(n) \ldots s_k(n) ]^T
93   * \]
94   * (here again we omit the k index in the notation for clarity)
95   * </p>
96   *
97   * <p>Taylor series formulas show that for any index offset i, s<sub>1</sub>(n-i) can be
98   * computed from s<sub>1</sub>(n), s<sub>2</sub>(n) ... s<sub>k</sub>(n), the formula being exact
99   * for degree k polynomials.
100  * \[
101  * s_1(n-i) = s_1(n) + \sum_{j\gt 0} (j+1) (-i)^j s_{j+1}(n)
102  * \]
103  * The previous formula can be used with several values for i to compute the transform between
104  * classical representation and Nordsieck vector. The transform between r<sub>n</sub>
105  * and q<sub>n</sub> resulting from the Taylor series formulas above is:
106  * \[
107  * q_n = s_1(n) u + P r_n
108  * \]
109  * where u is the [ 1 1 ... 1 ]<sup>T</sup> vector and P is the (k-1)&times;(k-1) matrix built
110  * with the \((j+1) (-i)^j\) terms with i being the row number starting from 1 and j being
111  * the column number starting from 1:
112  * \[
113  *   P=\begin{bmatrix}
114  *   -2  &amp;  3 &amp;   -4 &amp;    5 &amp; \ldots \\
115  *   -4  &amp; 12 &amp;  -32 &amp;   80 &amp; \ldots \\
116  *   -6  &amp; 27 &amp; -108 &amp;  405 &amp; \ldots \\
117  *   -8  &amp; 48 &amp; -256 &amp; 1280 &amp; \ldots \\
118  *       &amp;    &amp;  \ldots\\
119  *    \end{bmatrix}
120  * \]
121  * </p>
122  *
123  * <p>Using the Nordsieck vector has several advantages:</p>
124  * <ul>
125  *   <li>it greatly simplifies step interpolation as the interpolator mainly applies
126  *   Taylor series formulas,</li>
127  *   <li>it simplifies step changes that occur when discrete events that truncate
128  *   the step are triggered,</li>
129  *   <li>it allows to extend the methods in order to support adaptive stepsize.</li>
130  * </ul>
131  *
132  * <p>The Nordsieck vector at step n+1 is computed from the Nordsieck vector at step n as follows:
133  * <ul>
134  *   <li>y<sub>n+1</sub> = y<sub>n</sub> + s<sub>1</sub>(n) + u<sup>T</sup> r<sub>n</sub></li>
135  *   <li>s<sub>1</sub>(n+1) = h f(t<sub>n+1</sub>, y<sub>n+1</sub>)</li>
136  *   <li>r<sub>n+1</sub> = (s<sub>1</sub>(n) - s<sub>1</sub>(n+1)) P<sup>-1</sup> u + P<sup>-1</sup> A P r<sub>n</sub></li>
137  * </ul>
138  * <p>where A is a rows shifting matrix (the lower left part is an identity matrix):</p>
139  * <pre>
140  *        [ 0 0   ...  0 0 | 0 ]
141  *        [ ---------------+---]
142  *        [ 1 0   ...  0 0 | 0 ]
143  *    A = [ 0 1   ...  0 0 | 0 ]
144  *        [       ...      | 0 ]
145  *        [ 0 0   ...  1 0 | 0 ]
146  *        [ 0 0   ...  0 1 | 0 ]
147  * </pre>
148  *
149  * <p>The P<sup>-1</sup>u vector and the P<sup>-1</sup> A P matrix do not depend on the state,
150  * they only depend on k and therefore are precomputed once for all.</p>
151  *
152  */
153 public class AdamsBashforthIntegrator extends AdamsIntegrator {
154 
155     /** Integrator method name. */
156     private static final String METHOD_NAME = "Adams-Bashforth";
157 
158     /**
159      * Build an Adams-Bashforth integrator with the given order and step control parameters.
160      * @param nSteps number of steps of the method excluding the one being computed
161      * @param minStep minimal step (sign is irrelevant, regardless of
162      * integration direction, forward or backward), the last step can
163      * be smaller than this
164      * @param maxStep maximal step (sign is irrelevant, regardless of
165      * integration direction, forward or backward), the last step can
166      * be smaller than this
167      * @param scalAbsoluteTolerance allowed absolute error
168      * @param scalRelativeTolerance allowed relative error
169      * @exception MathIllegalArgumentException if order is 1 or less
170      */
171     public AdamsBashforthIntegrator(final int nSteps,
172                                     final double minStep, final double maxStep,
173                                     final double scalAbsoluteTolerance,
174                                     final double scalRelativeTolerance)
175         throws MathIllegalArgumentException {
176         super(METHOD_NAME, nSteps, nSteps, minStep, maxStep,
177               scalAbsoluteTolerance, scalRelativeTolerance);
178     }
179 
180     /**
181      * Build an Adams-Bashforth integrator with the given order and step control parameters.
182      * @param nSteps number of steps of the method excluding the one being computed
183      * @param minStep minimal step (sign is irrelevant, regardless of
184      * integration direction, forward or backward), the last step can
185      * be smaller than this
186      * @param maxStep maximal step (sign is irrelevant, regardless of
187      * integration direction, forward or backward), the last step can
188      * be smaller than this
189      * @param vecAbsoluteTolerance allowed absolute error
190      * @param vecRelativeTolerance allowed relative error
191      * @exception IllegalArgumentException if order is 1 or less
192      */
193     public AdamsBashforthIntegrator(final int nSteps,
194                                     final double minStep, final double maxStep,
195                                     final double[] vecAbsoluteTolerance,
196                                     final double[] vecRelativeTolerance)
197         throws IllegalArgumentException {
198         super(METHOD_NAME, nSteps, nSteps, minStep, maxStep,
199               vecAbsoluteTolerance, vecRelativeTolerance);
200     }
201 
202     /** {@inheritDoc} */
203     @Override
204     protected double errorEstimation(final double[] previousState, final double predictedTime,
205                                      final double[] predictedState,
206                                      final double[] predictedScaled,
207                                      final RealMatrix predictedNordsieck) {
208 
209         final StepsizeHelper helper = getStepSizeHelper();
210         double error = 0;
211         for (int i = 0; i < helper.getMainSetDimension(); ++i) {
212             final double tol = helper.getTolerance(i, FastMath.abs(predictedState[i]));
213 
214             // apply Taylor formula from high order to low order,
215             // for the sake of numerical accuracy
216             double variation = 0;
217             int sign = predictedNordsieck.getRowDimension() % 2 == 0 ? -1 : 1;
218             for (int k = predictedNordsieck.getRowDimension() - 1; k >= 0; --k) {
219                 variation += sign * predictedNordsieck.getEntry(k, i);
220                 sign       = -sign;
221             }
222             variation -= predictedScaled[i];
223 
224             final double ratio  = (predictedState[i] - previousState[i] + variation) / tol;
225             error              += ratio * ratio;
226 
227         }
228 
229         return FastMath.sqrt(error / helper.getMainSetDimension());
230 
231     }
232 
233     /** {@inheritDoc} */
234     @Override
235     protected AdamsStateInterpolator finalizeStep(final double stepSize, final double[] predictedState,
236                                                   final double[] predictedScaled, final Array2DRowRealMatrix predictedNordsieck,
237                                                   final boolean isForward,
238                                                   final ODEStateAndDerivative globalPreviousState,
239                                                   final ODEStateAndDerivative globalCurrentState,
240                                                   final EquationsMapper equationsMapper) {
241         return new AdamsStateInterpolator(getStepSize(), globalCurrentState,
242                                           predictedScaled, predictedNordsieck, isForward,
243                                           getStepStart(), globalCurrentState, equationsMapper);
244     }
245 
246 }