D02AGF (PDF version)
D02 Chapter Contents
D02 Chapter Introduction
NAG Library Manual

NAG Library Routine Document


Note:  before using this routine, please read the Users' Note for your implementation to check the interpretation of bold italicised terms and other implementation-dependent details.

+ Contents

    1  Purpose
    7  Accuracy

1  Purpose

D02AGF solves a two-point boundary value problem for a system of ordinary differential equations, using initial value techniques and Newton iteration; it generalizes D02HAF to include the case where parameters other than boundary values are to be determined.

2  Specification

REAL (KIND=nag_wp)  H, E(N), PARERR(N1), PARAM(N1), C(M1,N), MAT(N1,N1), COPY(1,1), WSPACE(N,9), WSPAC1(N), WSPAC2(N)

3  Description

D02AGF solves a two-point boundary value problem by determining the unknown parameters p1,p2,,pn1 of the problem. These parameters may be, but need not be, boundary values (as they are in D02HAF); they may include eigenvalue parameters in the coefficients of the differential equations, length of the range of integration, etc. The notation and methods used are similar to those of D02HAF and you are advised to study this first. (There the parameters p1,p2,,pn1 correspond to the unknown boundary conditions.) It is assumed that we have a system of n first-order ordinary differential equations of the form
dyi dx =fix,y1,y2,,yn,  i=1,2,,n,
and that derivatives fi are evaluated by AUX. The system, including the boundary conditions given by BCAUX, and the range of integration and matching point, r, given by RAAUX, involves the n1 unknown parameters p1,p2,,pn1 which are to be determined, and for which initial estimates must be supplied. The number of unknown parameters n1 must not exceed the number of equations n. If n1<n, we assume that n-n1 equations of the system are not involved in the matching process. These are usually referred to as ‘driving equations’; they are independent of the parameters and of the solutions of the other n1 equations. In numbering the equations for AUX, the driving equations must be put last.
The estimated values of the parameters are corrected by a form of Newton iteration. The Newton correction on each iteration is calculated using a matrix whose i,jth element depends on the derivative of the ith component of the solution, yi, with respect to the jth parameter, pj. This matrix is calculated by a simple numerical differentiation technique which requires n1 evaluations of the differential system.

4  References


5  Parameters

You are strongly recommended to read Sections 3 and 8 in conjunction with this section.
1:     H – REAL (KIND=nag_wp)Input/Output
On entry: H must be set to an estimate of the step size, h, needed for integration.
On exit: the last step length used.
2:     E(N) – REAL (KIND=nag_wp) arrayInput
On entry: Ei must be set to a small quantity to control the ith solution component. The element Ei is used:
(i) in the bound on the local error in the ith component of the solution yi during integration,
(ii) in the convergence test on the ith component of the solution yi at the matching point in the Newton iteration.
The elements Ei should not be chosen too small. They should usually be several orders of magnitude larger than machine precision.
3:     PARERR(N1) – REAL (KIND=nag_wp) arrayInput
On entry: PARERRi must be set to a small quantity to control the ith parameter component. The element PARERRi is used:
(i) in the convergence test on the ith parameter in the Newton iteration,
(ii) in perturbing the ith parameter when approximating the derivatives of the components of the solution with respect to the ith parameter, for use in the Newton iteration.
The elements PARERRi should not be chosen too small. They should usually be several orders of magnitude larger than machine precision.
4:     PARAM(N1) – REAL (KIND=nag_wp) arrayInput/Output
On entry: PARAMi must be set to an estimate for the ith parameter, pi, for i=1,2,,N1.
On exit: the corrected value for the ith parameter, unless an error has occurred, when it contains the last calculated value of the parameter (possibly perturbed by PARERRi×1+PARAMi if the error occurred when calculating the approximate derivatives).
5:     C(M1,N) – REAL (KIND=nag_wp) arrayOutput
On exit: the solution when M1>1 (see M1).
If M1=1, the elements of C are not used.
6:     N – INTEGERInput
7:     N1 – INTEGERInput
Constraint: N1N.
8:     M1 – INTEGERInput
9:     AUX – SUBROUTINE, supplied by the user.External Procedure
AUX must evaluate the functions fi (i.e., the derivatives yi) for given values of its arguments, x,y1,,yn, p1,,pn1.
AUX must either be a module subprogram USEd by, or declared as EXTERNAL in, the (sub)program from which D02AGF is called. Parameters denoted as Input must not be changed by this procedure.
10:   BCAUX – SUBROUTINE, supplied by the user.External Procedure
BCAUX must evaluate the values of yi at the end points of the range given the values of p1,,pn1.
BCAUX must either be a module subprogram USEd by, or declared as EXTERNAL in, the (sub)program from which D02AGF is called. Parameters denoted as Input must not be changed by this procedure.
11:   RAAUX – SUBROUTINE, supplied by the user.External Procedure
RAAUX must evaluate the end points, x0 and x1, of the range and the matching point, r, given the values p1,p2,,pn1.
RAAUX must either be a module subprogram USEd by, or declared as EXTERNAL in, the (sub)program from which D02AGF is called. Parameters denoted as Input must not be changed by this procedure.
12:   PRSOL – SUBROUTINE, supplied by the user.External Procedure
PRSOL is called at each iteration of the Newton method and can be used to print the current values of the parameters pi, for i=1,2,,n1, their errors, ei, and the sum of squares of the errors at the matching point, r.
The specification of PRSOL is:
REAL (KIND=nag_wp)  PARAM(N1), RES, ERR(N1)
1:     PARAM(N1) – REAL (KIND=nag_wp) arrayInput
On entry: pi, for i=1,2,,n1, the current value of the parameters.
2:     RES – REAL (KIND=nag_wp)Input
3:     N1 – INTEGERInput
4:     ERR(N1) – REAL (KIND=nag_wp) arrayInput
On entry: the errors in the parameters, ei, for i=1,2,,n1.
PRSOL must either be a module subprogram USEd by, or declared as EXTERNAL in, the (sub)program from which D02AGF is called. Parameters denoted as Input must not be changed by this procedure.
13:   MAT(N1,N1) – REAL (KIND=nag_wp) arrayWorkspace
14:   COPY(1,1) – REAL (KIND=nag_wp) arrayInput
15:   WSPACE(N,9) – REAL (KIND=nag_wp) arrayWorkspace
16:   WSPAC1(N) – REAL (KIND=nag_wp) arrayWorkspace
17:   WSPAC2(N) – REAL (KIND=nag_wp) arrayWorkspace
18:   IFAIL – INTEGERInput/Output
On entry: IFAIL must be set to 0, -1​ or ​1. If you are unfamiliar with this parameter you should refer to Section 3.3 in the Essential Introduction for details.
For environments where it might be inappropriate to halt program execution when an error is detected, the value -1​ or ​1 is recommended. If the output of error messages is undesirable, then the value 1 is recommended. Otherwise, if you are not familiar with this parameter, the recommended value is 0. When the value -1​ or ​1 is used it is essential to test the value of IFAIL on exit.
On exit: IFAIL=0 unless the routine detects an error or a warning has been flagged (see Section 6).

6  Error Indicators and Warnings

If on entry IFAIL=0 or -1, explanatory error messages are output on the current error message unit (as defined by X04AAF).
Errors or warnings detected by the routine:
This indicates that N1>N on entry, that is the number of parameters is greater than the number of differential equations.
As for IFAIL=4 except that the integration failed while calculating the matrix for use in the Newton iteration.
The step length for integration H has halved more than 13 times (or too many steps were needed to reach the end of the range of integration) in attempting to control the local truncation error whilst integrating to obtain the solution corresponding to the current values pi. If, on failure, H has the sign of r-x0 then failure has occurred whilst integrating from x0 to r, otherwise it has occurred whilst integrating from x1 to r.
Convergence has not been obtained after 12 satisfactory iterations of the Newton method.
A further discussion of these errors and the steps which might be taken to correct them is given in Section 8.

7  Accuracy

If the process converges, the accuracy to which the unknown parameters are determined is usually close to that specified by you; and the solution, if requested, is usually determined to the accuracy specified.

8  Further Comments

The time taken by D02AGF depends on the complexity of the system, and on the number of iterations required. In practice, integration of the differential equations is by far the most costly process involved.
There may be particular difficulty in integrating the differential equations in one direction (indicated by IFAIL=2 or 4). The value of r should be adjusted to avoid such difficulties.
If the matching point r is at one of the end points x0 or x1 and some of the parameters are used only to determine the boundary values at this point, then good initial estimates for these parameters are not required, since they are completely determined by the routine (for example, see p2 in EX1 of Section 9).
Wherever they occur in the procedure, the error parameters contained in the arrays E and PARERR are used in ‘mixed’ form; that is Ei always occurs in expressions of the form Ei×1+yi, and PARERRi always occurs in expressions of the form PARERRi×1+pi. Though not ideal for every application, it is expected that this mixture of absolute and relative error testing will be adequate for most purposes.
Note that convergence is not guaranteed. You are strongly advised to provide an output PRSOL, as shown in EX1 of Section 9, in order to monitor the progress of the iteration. Failure of the Newton iteration to converge (see IFAIL=6 or 7) usually results from poor starting approximations to the parameters, though occasionally such failures occur because the elements of one or both of the arrays PARERR or E are too small. (It should be possible to distinguish these cases by studying the output from PRSOL.) Poor starting approximations can also result in the failure described under IFAIL=4 and 5 in Section 6 (especially if these errors occur after some Newton iterations have been completed, that is, after two or more calls of PRSOL). More frequently, a singular matrix in the Newton method (monitored as IFAIL=5) occurs because the mathematical problem has been posed incorrectly. The case IFAIL=4 usually occurs because h or r has been poorly estimated, so these values should be checked first. If IFAIL=2 is monitored, the solution y1,y2,,yn is sensitive to perturbations in the parameters pi. Reduce the size of one or more values PARERRi to reduce the perturbations. Since only one value pi is perturbed at any time when forming the matrix, the perturbation which is too large can be located by studying the final output from PRSOL and the values of the parameters returned by D02AGF. If this change leads to other types of failure improve the initial values of pi by other means.
The computing time for integrating the differential equations can sometimes depend critically on the quality of the initial estimates for the parameters pi. If it seems that too much computing time is required and, in particular, if the values ERRi (available on each call of PRSOL) are much larger than the expected values of the solution at the matching point r, then the coding of AUX, BCAUX and RAAUX should be checked for errors. If no errors can be found, an independent attempt should be made to improve the initial estimates for PARAMi.
The subroutine can be used to solve a very wide range of problems, for example:
(a) eigenvalue problems, including problems where the eigenvalue occurs in the boundary conditions;
(b) problems where the differential equations depend on some parameters which are to be determined so as to satisfy certain boundary conditions (see EX1 in Section 9);
(c) problems where one of the end points of the range of integration is to be determined as the point where a variable yi takes a particular value (see EX2 in Section 9);
(d) singular problems and problems on infinite ranges of integration where the values of the solution at x0 or x1 or both are determined by a power series or an asymptotic expansion (or a more complicated expression) and where some of the coefficients in the expression are to be determined (see EX1 in Section 9); and
(e) differential equations with certain terms defined by other independent (driving) differential equations.

9  Example

For this routine two examples are presented. There is a single example program for D02AGF, with a main program and the code to solve the two example problems given in Example 1 (EX1) and Example 2 (EX2).
Example 1 (EX1)
This example finds the solution of the differential equation
y=y3-y 2x
on the range 0x16, with boundary conditions y0=0.1 and y16=1/6.
We cannot use the differential equation at x=0 because it is singular, so we take the truncated series expansion
near the origin (which is correct to the number of terms given in this case). Here p1 is one of the parameters to be determined. We choose the range as 0.1,16 and setting p2=y16, we can determine all the boundary conditions. We take the matching point to be 16, the end of the range, and so a good initial guess for p2 is not necessary. We write y=Y1, y=Y2, and estimate p1=PARAM1=0.2, p2=PARAM2=0.0.
Example 2 (EX2)
This example finds the gravitational constant p1 and the range p2 over which a projectile must be fired to hit the target with a given velocity. The differential equations are
y=tanϕ v= -p1sinϕ+0.00002v2 vcosϕ ϕ=-p1v2k
on the range 0<x<p2 with boundary conditions
y=0, v=500, ϕ=0.5 at x=0 y=0, v=450, ϕ=p3 at x=p2.
We write y=Y1, v=Y2, ϕ=Y3, and we take the matching point r=p2. We estimate p1=PARAM1=32, p2=PARAM2=6000 and p3=PARAM3=0.54 (though this estimate is not important).

9.1  Program Text

Program Text (d02agfe.f90)

9.2  Program Data

Program Data (d02agfe.d)

9.3  Program Results

Program Results (d02agfe.r)

Example Program Plot for d02agf1-plot
Example Program Plot for d02agf2-plot

D02AGF (PDF version)
D02 Chapter Contents
D02 Chapter Introduction
NAG Library Manual

© The Numerical Algorithms Group Ltd, Oxford, UK. 2012