d02saf solves a two-point boundary value problem for a system of first-order ordinary differential equations with boundary conditions, combined with additional algebraic equations. It uses initial value techniques and a modified Newton iteration in a shooting and matching method.
The routine may be called by the names d02saf or nagf_ode_bvp_shoot_genpar_algeq.
3Description
d02saf solves a two-point boundary value problem for a system of $n$ first-order ordinary differential equations with separated boundary conditions by determining certain unknown arguments ${p}_{1},{p}_{2},\dots ,{p}_{m}$. (There may also be additional algebraic equations to be solved in the determination of the arguments and, if so, these equations are defined by eqn.) The arguments may be, but need not be, boundary values; they may include eigenvalues, arguments in the coefficients of the differential equations, coefficients in series expansions or asymptotic expansions for boundary values, the length of the range of definition of the system of differential equations, etc.
It is assumed that we have a system of $n$ differential equations of the form
$${y}^{\prime}=f(x,y,p)\text{,}$$
(1)
where $p={({p}_{1},{p}_{2},\dots ,{p}_{m})}^{\mathrm{T}}$ is the vector of arguments, and that the derivative $f$ is evaluated by fcn. Also, ${n}_{1}$ of the equations are assumed to depend on $p$. For ${n}_{1}<n$ the $n-{n}_{1}$ equations of the system are not involved in the matching process. These are the driving equations; they should be independent of $p$ and of the solution of the other ${n}_{1}$ equations. In numbering the equations in fcn and bc the driving equations must be put first (as they naturally occur in most applications). The range of definition [$a,b$] of the differential equations is defined by range and may depend on the arguments ${p}_{1},{p}_{2},\dots ,{p}_{m}$ (that is, on $p$). range must define the points ${x}_{1},{x}_{2},\dots ,{x}_{{\mathbf{npoint}}}$, ${\mathbf{npoint}}\ge 2$, which must satisfy
(or a similar relationship with all the inequalities reversed).
If ${\mathbf{npoint}}>2$ the points ${x}_{1},{x}_{2},\dots ,{x}_{{\mathbf{npoint}}}$ can be used to break up the range of definition. Integration is restarted at each of these points. This means that the differential equations (1) can be defined differently in each sub-interval
$[{x}_{\mathit{i}},{x}_{\mathit{i}+1}]$, for $\mathit{i}=1,2,\dots ,{\mathbf{npoint}}-1$. Also, since initial and maximum integration step sizes can be supplied on each sub-interval (via the array swp), you can indicate parts of the range $[a,b]$ where the solution $y\left(x\right)$ may be difficult to obtain accurately and can take appropriate action.
The boundary conditions may also depend on the arguments and are applied at $a={x}_{1}$ and $b={x}_{{\mathbf{npoint}}}$. They are defined (in bc) in the form
The boundary value problem is solved by determining the unknown arguments $p$ by a shooting and matching technique. The differential equations are always integrated from $a$ to $b$ with initial values $y\left(a\right)={g}_{1}\left(p\right)$. The solution vector thus obtained at $x=b$ is subtracted from the vector ${g}_{2}\left(p\right)$ to give the ${n}_{1}$ residuals ${r}_{1}\left(p\right)$, ignoring the first $n-{n}_{1}$, driving equations. Because the direction of integration is always from $a$ to $b$, it is unnecessary, in bc, to supply values for the first $n-{n}_{1}$ boundary values at $b$, that is the first $n-{n}_{1}$ components of ${g}_{2}$ in (3). For ${n}_{1}<m$ then ${r}_{1}\left(p\right)$. Together with the $m-{n}_{1}$ equations defined by eqn,
$${r}_{2}\left(p\right)=0\text{,}$$
(4)
these give a vector of residuals $r$, which at the solution, $p$, must satisfy
These equations are solved by a pseudo-Newton iteration which uses a modified singular value decomposition of $J=\frac{\partial r}{\partial p}$ when solving the linear equations which arise. The Jacobian $J$ used in Newton's method is obtained by numerical differentiation. The arguments at each Newton iteration are accepted only if the norm ${\Vert {D}^{\mathrm{-1}}{\stackrel{~}{J}}^{+}r\Vert}_{2}$ is much reduced from its previous value. Here ${\stackrel{~}{J}}^{+}$ is the pseudo-inverse, calculated from the singular value decomposition, of a modified version of the Jacobian $J$ (${J}^{+}$ is actually the inverse of the Jacobian in well-conditioned cases). $D$ is a diagonal matrix with
See Deuflhard (1974) for further details of the variants of Newton's method used, Gay (1976) for the modification of the singular value decomposition and Gladwell (1979) for an overview of the method used.
Two facilities are provided to prevent the pseudo-Newton iteration running into difficulty. First, you are permitted to specify constraints on the values of the arguments $p$ via a constr. These constraints are only used to prevent the Newton iteration using values for $p$ which would violate them; that is, they are not used to determine the values of $p$. Secondly, you are permitted to specify a maximum value ${y}_{\mathrm{max}}$ for ${\Vert y\left(x\right)\Vert}_{\infty}$ at all points in the range $[a,b]$. It is intended that this facility be used to prevent machine ‘overflow’ in the integrations of equation (1) due to poor choices of the arguments $p$ which might arise during the Newton iteration. When using this facility, it is presumed that you have an estimate of the likely size of ${\Vert y\left(x\right)\Vert}_{\infty}$ at all points $x\in [a,b]$. ${y}_{\mathrm{max}}$ should then be chosen rather larger (say by a factor of $10$) than this estimate.
You are strongly advised to supply a monit (or to call the ‘default’ routine d02hbx, see monit) to monitor the progress of the pseudo-Newton iteration. You can output the solution of the problem $y\left(x\right)$ by supplying a suitable prsol.
d02saf is designed to try all possible options before admitting failure and returning to you. Provided the routine can start the Newton iteration from the initial point $p$ it will exhaust all the options available to it (though you can override this by specifying a maximum number of iterations to be taken). The fact that all its options have been exhausted is the only error exit from the iteration. Other error exits are possible, however, whilst setting up the Newton iteration and when computing the final solution.
If you require more background information about the solution of boundary value problems by shooting methods you are recommended to read the appropriate chapters of Hall and Watt (1976), and for a detailed description of d02safGladwell (1979) is recommended.
4References
Deuflhard P (1974) A modified Newton method for the solution of ill-conditioned systems of nonlinear equations with application to multiple shooting Numer. Math.22 289–315
Gay D (1976) On modifying singular values to solve possibly singular systems of nonlinear equations Working Paper 125 Computer Research Centre, National Bureau for Economics and Management Science, Cambridge, MA
Gladwell I (1979) The development of the boundary value codes in the ordinary differential equations chapter of the NAG Library Codes for Boundary Value Problems in Ordinary Differential Equations. Lecture Notes in Computer Science (eds B Childs, M Scott, J W Daniel, E Denman and P Nelson) 76 Springer–Verlag
Hall G and Watt J M (ed.) (1976) Modern Numerical Methods for Ordinary Differential Equations Clarendon Press, Oxford
5Arguments
1: $\mathbf{p}\left({\mathbf{m}}\right)$ – Real (Kind=nag_wp) arrayInput/Output
On entry: ${\mathbf{p}}\left(\mathit{i}\right)$ must be set to an estimate of the $\mathit{i}$th argument, ${p}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,m$.
On exit: the corrected value for the $i$th argument, unless an error has occurred, when it contains the last calculated value of the argument.
2: $\mathbf{m}$ – IntegerInput
On entry: $m$, the number of arguments.
Constraint:
${\mathbf{m}}>0$.
3: $\mathbf{n}$ – IntegerInput
On entry: $n$, the total number of differential equations.
Constraint:
${\mathbf{n}}>0$.
4: $\mathbf{n1}$ – IntegerInput
On entry: ${n}_{1}$, the number of differential equations active in the matching process. The active equations must be placed last in the numbering in fcn and bc. The first${\mathbf{n}}-{\mathbf{n1}}$ equations are used as the driving equations.
Constraint:
${\mathbf{n1}}\le {\mathbf{n}}$, ${\mathbf{n1}}\le {\mathbf{m}}$ and ${\mathbf{n1}}>0$.
5: $\mathbf{pe}\left({\mathbf{m}}\right)$ – Real (Kind=nag_wp) arrayInput
On entry: ${\mathbf{pe}}\left(\mathit{i}\right)$, for $\mathit{i}=1,2,\dots ,m$, must be set to a positive value for use in the convergence test in the $i$th argument ${p}_{i}$. See the description of pf for further details.
Constraint:
${\mathbf{pe}}\left(\mathit{i}\right)>0.0$, for $\mathit{i}=1,2,\dots ,m$.
6: $\mathbf{pf}\left({\mathbf{m}}\right)$ – Real (Kind=nag_wp) arrayInput/Output
On entry: ${\mathbf{pf}}\left(\mathit{i}\right)$, for $\mathit{i}=1,2,\dots ,m$, should be set to a ‘floor’ value in the convergence test on the $i$th argument ${p}_{i}$. If ${\mathbf{pf}}\left(i\right)\le 0.0$ on entry then it is set to the small positive value $\sqrt{\epsilon}$ (where $\epsilon $ may in most cases be considered to be machine precision); otherwise it is used unchanged.
The Newton iteration is presumed to have converged if a full Newton step is taken (${\mathbf{istate}}=1$ in the specification of monit), the singular values of the Jacobian are not being significantly perturbed (also see monit) and if the Newton correction ${C}_{i}$ satisfies
where ${p}_{i}$ is the current value of the $i$th argument. The values ${\mathbf{pf}}\left(i\right)$ are also used in determining the Newton iterates as discussed in Section 3, see equation (6).
On exit: the values actually used.
7: $\mathbf{e}\left({\mathbf{n}}\right)$ – Real (Kind=nag_wp) arrayInput
On entry: values for use in controlling the local error in the integration of the differential equations. If
${\mathit{err}}_{\mathit{i}}$ is an estimate of the local error in ${y}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,n$,
Constraint:
${\mathbf{e}}\left(\mathit{i}\right)>0.0$, for $\mathit{i}=1,2,\dots ,{\mathbf{n}}$.
8: $\mathbf{dp}\left({\mathbf{m}}\right)$ – Real (Kind=nag_wp) arrayInput/Output
On entry: a value to be used in perturbing the argument ${p}_{i}$ in the numerical differentiation to estimate the Jacobian used in Newton's method. If ${\mathbf{dp}}\left(i\right)=0.0$ on entry, an estimate is made internally by setting
where ${p}_{i}$ is the initial value of the argument supplied by you and $\epsilon $ may in most cases be considered to be machine precision. The estimate of the Jacobian, $J$, is made using forward differences, that is for each
$\mathit{i}$, for $\mathit{i}=1,2,\dots ,m$, ${p}_{\mathit{i}}$ is perturbed to ${p}_{\mathit{i}}+{\mathbf{dp}}\left(\mathit{i}\right)$ and the $\mathit{i}$th column of $J$ is estimated as
where the other components of $p$ are unchanged (see (3) for the notation used). If this fails to produce a Jacobian with significant columns, backward differences are tried by perturbing ${p}_{\mathit{i}}$ to ${p}_{\mathit{i}}-{\mathbf{dp}}\left(\mathit{i}\right)$ and if this also fails then central differences are used with ${p}_{\mathit{i}}$ perturbed to ${p}_{\mathit{i}}+10.0\times {\mathbf{dp}}\left(\mathit{i}\right)$. If this also fails then the calculation of the Jacobian is abandoned. If the Jacobian has not previously been calculated then an error exit is taken. If an earlier estimate of the Jacobian is available then the current argument set,
${p}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,m$, is abandoned in favour of the last argument set from which useful progress was made and the singular values of the Jacobian used at the point are modified before proceeding with the Newton iteration. You are recommended to use the default value ${\mathbf{dp}}\left(i\right)=0.0$ unless you have prior knowledge of a better choice. If any of the perturbations described are likely to lead to an unfortunate set of argument values then you should use the LOGICAL FUNCTION constr to prevent such perturbations (all changes of arguments are checked by a call to constr).
On exit: the values actually used.
9: $\mathbf{npoint}$ – IntegerInput
On entry: 2 plus the number of break-points in the range of definition of the system of differential equations (1).
Constraint:
${\mathbf{npoint}}\ge 2$.
10: $\mathbf{swp}({\mathbf{ldswp}},6)$ – Real (Kind=nag_wp) arrayInput/Output
On entry: ${\mathbf{swp}}(i,1)$ must contain an estimate for an initial step size for integration across the $i$th sub-interval
$[{\mathbf{x}}\left(\mathit{i}\right),{\mathbf{x}}\left(\mathit{i}+1\right)]$, for $\mathit{i}=1,2,\dots ,{\mathbf{npoint}}-1$, (see range). ${\mathbf{swp}}(i,1)$ should have the same sign as ${\mathbf{x}}\left(i+1\right)-{\mathbf{x}}\left(i\right)$ if it is nonzero. If ${\mathbf{swp}}(i,1)=0.0$, on entry, a default value for the initial step size is calculated internally. This is the recommended mode of entry.
${\mathbf{swp}}(i,3)$ must contain a lower bound for the modulus of the step size on the
$\mathit{i}$th sub-interval $[{\mathbf{x}}\left(\mathit{i}\right),{\mathbf{x}}\left(\mathit{i}+1\right)]$, for $\mathit{i}=1,2,\dots ,{\mathbf{npoint}}-1$. If ${\mathbf{swp}}(i,3)=0.0$ on entry, a very small default value is used. By setting ${\mathbf{swp}}(i,3)>0.0$ but smaller than the expected step sizes (assuming you have some insight into the likely step sizes) expensive integrations with arguments $p$ far from the solution can be avoided.
${\mathbf{swp}}(\mathit{i},2)$ must contain an upper bound on the modulus of the step size to be used in the integration on $[{\mathbf{x}}\left(\mathit{i}\right),{\mathbf{x}}\left(\mathit{i}+1\right)]$, for $\mathit{i}=1,2,\dots ,{\mathbf{npoint}}-1$. If ${\mathbf{swp}}(i,2)=0.0$ on entry no bound is assumed. This is the recommended mode of entry unless the solution is expected to have important features which might be ‘missed’ in the integration if the step size were permitted to be chosen freely.
On exit: ${\mathbf{swp}}(\mathit{i},1)$ contains the initial step size used on the last integration on $[{\mathbf{x}}\left(\mathit{i}\right),{\mathbf{x}}\left(\mathit{i}+1\right)]$, for $\mathit{i}=1,2,\dots ,{\mathbf{npoint}}-1$, (excluding integrations during the calculation of the Jacobian).
${\mathbf{swp}}(\mathit{i},2)$, for $\mathit{i}=1,2,\dots ,{\mathbf{npoint}}-1$, is usually unchanged. If the maximum step size ${\mathbf{swp}}(i,2)$ is so small or the length of the range $[{\mathbf{x}}\left(i\right),{\mathbf{x}}\left(i+1\right)]$ is so short that on the last integration the step size was not controlled in the main by the size of the error tolerances ${\mathbf{e}}\left(i\right)$ but by these other factors, ${\mathbf{swp}}({\mathbf{npoint}},2)$ is set to the floating-point value of $i$ if the problem last occurred in $[{\mathbf{x}}\left(i\right),{\mathbf{x}}\left(i+1\right)]$. Any results obtained when this value is returned as nonzero should be viewed with caution.
${\mathbf{swp}}(\mathit{i},3)$, for $\mathit{i}=1,2,\dots ,{\mathbf{npoint}}-1$, are unchanged.
If an error exit with ${\mathbf{ifail}}={\mathbf{4}}$, ${\mathbf{5}}$ or ${\mathbf{6}}$ (see Section 6) occurs on the integration made from ${\mathbf{x}}\left(i\right)$ to ${\mathbf{x}}\left(i+1\right)$ the floating-point value of $i$ is returned in ${\mathbf{swp}}({\mathbf{npoint}},1)$. The actual point $x\in [{\mathbf{x}}\left(i\right),{\mathbf{x}}\left(i+1\right)]$ where the error occurred is returned in ${\mathbf{swp}}(1,5)$ (see also the specification of w). The floating-point value of npoint is returned in ${\mathbf{swp}}({\mathbf{npoint}},1)$ if the error exit is caused by a call to bc.
If an error exit occurs when estimating the Jacobian matrix (${\mathbf{ifail}}={\mathbf{7}}$, ${\mathbf{8}}$, ${\mathbf{9}}$, ${\mathbf{10}}$, ${\mathbf{11}}$ or ${\mathbf{12}}$, see Section 6) and if argument ${p}_{i}$ was the cause of the failure then on exit ${\mathbf{swp}}({\mathbf{npoint}},1)$ contains the floating-point value of $i$.
${\mathbf{swp}}(\mathit{i},4)$ contains the point ${\mathbf{x}}\left(\mathit{i}\right)$, for $\mathit{i}=1,2,\dots ,{\mathbf{npoint}}$, used at the solution $p$ or at the final values of $p$ if an error occurred.
On entry: an upper bound on the number of Newton iterations. If ${\mathbf{icount}}=0$ on entry, no check on the number of iterations is made (this is the recommended mode of entry).
Constraint:
${\mathbf{icount}}\ge 0$.
13: $\mathbf{range}$ – Subroutine, supplied by the user.External Procedure
range must specify the break-points
${x}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,{\mathbf{npoint}}$, which may depend on the arguments
${p}_{\mathit{j}}$, for $\mathit{j}=1,2,\dots ,m$.
1: $\mathbf{x}\left({\mathbf{npoint}}\right)$ – Real (Kind=nag_wp) arrayOutput
On exit: the
$\mathit{i}$th break-point, for $\mathit{i}=1,2,\dots ,{\mathbf{npoint}}$. The sequence $\left({\mathbf{x}}\left(i\right)\right)$ must be strictly monotonic, that is either
On entry: $2$ plus the number of break-points in $(a,b)$.
3: $\mathbf{p}\left({\mathbf{m}}\right)$ – Real (Kind=nag_wp) arrayInput
On entry: the current estimate of the
$\mathit{i}$th argument, for $\mathit{i}=1,2,\dots ,m$.
4: $\mathbf{m}$ – IntegerInput
On entry: $m$, the number of arguments.
range must either be a module subprogram USEd by, or declared as EXTERNAL in, the (sub)program from which d02saf is called. Arguments denoted as Input must not be changed by this procedure.
Note:range should not return floating-point NaN (Not a Number) or infinity values, since these are not handled by d02saf. If your code inadvertently does return any NaNs or infinities, d02saf is likely to produce unexpected results.
14: $\mathbf{bc}$ – Subroutine, supplied by the user.External Procedure
bc must place in g1 and g2 the boundary conditions at $a$ and $b$ respectively.
1: $\mathbf{g1}\left({\mathbf{n}}\right)$ – Real (Kind=nag_wp) arrayOutput
On exit: the value of
${y}_{\mathit{i}}\left(a\right)$, for $\mathit{i}=1,2,\dots ,n$, (where this may be a known value or a function of the parameters
${p}_{\mathit{j}}$, for $\mathit{j}=1,2,\dots ,m$).
2: $\mathbf{g2}\left({\mathbf{n}}\right)$ – Real (Kind=nag_wp) arrayOutput
On exit: the value of
${y}_{\mathit{i}}\left(b\right)$, for $\mathit{i}=1,2,\dots ,n$, (where these may be known values or functions of the parameters
${p}_{\mathit{j}}$, for $\mathit{j}=1,2,\dots ,m$). If $n>{n}_{1}$, so that there are some driving equations, the first $n-{n}_{1}$ values of g2 need not be set since they are never used.
3: $\mathbf{p}\left({\mathbf{m}}\right)$ – Real (Kind=nag_wp) arrayInput
On entry: an estimate of the
$\mathit{i}$th argument, ${p}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,m$.
4: $\mathbf{m}$ – IntegerInput
On entry: $m$, the number of arguments.
5: $\mathbf{n}$ – IntegerInput
On entry: $n$, the number of differential equations.
bc must either be a module subprogram USEd by, or declared as EXTERNAL in, the (sub)program from which d02saf is called. Arguments denoted as Input must not be changed by this procedure.
Note:bc should not return floating-point NaN (Not a Number) or infinity values, since these are not handled by d02saf. If your code inadvertently does return any NaNs or infinities, d02saf is likely to produce unexpected results.
15: $\mathbf{fcn}$ – Subroutine, supplied by the user.External Procedure
fcn must evaluate the functions
${f}_{\mathit{i}}$ (i.e., the derivatives ${y}_{\mathit{i}}^{\prime}$), for $\mathit{i}=1,2,\dots ,n$.
2: $\mathbf{y}\left({\mathbf{n}}\right)$ – Real (Kind=nag_wp) arrayInput
On entry: ${y}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,\mathit{n}$, the value of the argument.
3: $\mathbf{f}\left({\mathbf{n}}\right)$ – Real (Kind=nag_wp) arrayOutput
On exit: the derivative of
${y}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,n$, evaluated at $x$. ${\mathbf{f}}\left(i\right)$ may depend upon the parameters
${p}_{\mathit{j}}$, for $\mathit{j}=1,2,\dots ,m$. If there are any driving equations (see Section 3) then these must be numbered first in the ordering of the components of f.
4: $\mathbf{n}$ – IntegerInput
On entry: $\mathit{n}$, the number of equations.
5: $\mathbf{p}\left({\mathbf{m}}\right)$ – Real (Kind=nag_wp) arrayInput
On entry: the current estimate of the
$\mathit{i}$th argument ${p}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,m$.
6: $\mathbf{m}$ – IntegerInput
On entry: $m$, the number of arguments.
7: $\mathbf{i}$ – IntegerInput
On entry: specifies the sub-interval $[{x}_{i},{x}_{i+1}]$ on which the derivatives are to be evaluated.
fcn must either be a module subprogram USEd by, or declared as EXTERNAL in, the (sub)program from which d02saf is called. Arguments denoted as Input must not be changed by this procedure.
Note:fcn should not return floating-point NaN (Not a Number) or infinity values, since these are not handled by d02saf. If your code inadvertently does return any NaNs or infinities, d02saf is likely to produce unexpected results.
16: $\mathbf{eqn}$ – Subroutine, supplied by the NAG Library or the user.External Procedure
eqn is used to describe the additional algebraic equations to be solved in the determination of the parameters,
${p}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,m$. If there are no additional algebraic equations (i.e., $m={n}_{1}$) then eqn is never called and the dummy routine d02hbz should be used as the actual argument.
1: $\mathbf{e}\left({\mathbf{q}}\right)$ – Real (Kind=nag_wp) arrayOutput
On exit: the vector of residuals, ${r}_{2}\left(p\right)$, that is the amount by which the current estimates of the arguments fail to satisfy the algebraic equations.
2: $\mathbf{q}$ – IntegerInput
On entry: the number of algebraic equations, $m-{n}_{1}$.
3: $\mathbf{p}\left({\mathbf{m}}\right)$ – Real (Kind=nag_wp) arrayInput
On entry: the current estimate of the
$\mathit{i}$th argument ${p}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,m$.
4: $\mathbf{m}$ – IntegerInput
On entry: $m$, the number of arguments.
eqn must either be a module subprogram USEd by, or declared as EXTERNAL in, the (sub)program from which d02saf is called. Arguments denoted as Input must not be changed by this procedure.
Note:eqn should not return floating-point NaN (Not a Number) or infinity values, since these are not handled by d02saf. If your code inadvertently does return any NaNs or infinities, d02saf is likely to produce unexpected results.
17: $\mathbf{constr}$ – Logical Function, supplied by the user.External Procedure
constr is used to prevent the pseudo-Newton iteration running into difficulty. constr should return the value .TRUE. if the constraints are satisfied by the parameters ${p}_{1},{p}_{2},\dots ,{p}_{m}$. Otherwise constr should return the value .FALSE.. Usually the dummy function d02hby, which returns the value .TRUE. at all times, will suffice and in the first instance this is recommended as the actual argument.
1: $\mathbf{p}\left({\mathbf{m}}\right)$ – Real (Kind=nag_wp) arrayInput
On entry: an estimate of the
$\mathit{i}$th argument, ${p}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,m$.
2: $\mathbf{m}$ – IntegerInput
On entry: $m$, the number of arguments.
constr must either be a module subprogram USEd by, or declared as EXTERNAL in, the (sub)program from which d02saf is called. Arguments denoted as Input must not be changed by this procedure.
18: $\mathbf{ymax}$ – Real (Kind=nag_wp)Input
On entry: a non-negative value which is used as a bound on all values ${\Vert y\left(x\right)\Vert}_{\infty}$ where $y\left(x\right)$ is the solution at any point $x$ between ${\mathbf{x}}\left(1\right)$ and ${\mathbf{x}}\left({\mathbf{npoint}}\right)$ for the current arguments ${p}_{1},{p}_{2},\dots ,{p}_{m}$. If this bound is exceeded the integration is terminated and the current arguments are rejected. Such a rejection will result in an error exit if it prevents the initial residual or Jacobian, or the final solution, being calculated. If ${\mathbf{ymax}}=0$ on entry, no bound on the solution $y$ is used; that is the integrations proceed without any checking on the size of ${\Vert y\Vert}_{\infty}$.
19: $\mathbf{monit}$ – Subroutine, supplied by the NAG Library or the user.External Procedure
monit enables you to monitor the values of various quantities during the calculation. It is called by d02saf after every calculation of the norm ${\Vert {{\mathbf{d}}}^{\mathrm{-1}}{\stackrel{~}{J}}^{+}r\Vert}_{2}$ which determines the strategy of the Newton method, every time there is an internal error exit leading to a change of strategy, and before an error exit when calculating the initial Jacobian. Usually the routine d02hbx will be adequate and you are advised to use this as the actual argument for monit in the first instance. (In this case a call to x04abf must be made before the call of d02saf.) If no monitoring is required, the dummy routine d02sas may be used.
The calculation of the residual, Jacobian and ${\Vert {{\mathbf{d}}}^{\mathrm{-1}}{\stackrel{~}{J}}^{+}r\Vert}_{2}$ are taking place.
${\mathbf{istate}}=1$ to $5$
During the Newton iteration a factor of ${2}^{(-{\mathbf{istate}}+1)}$ of the Newton step is being used to try to reduce the norm.
${\mathbf{istate}}=6$
The current Newton step has been rejected and the Jacobian is being re-calculated.
${\mathbf{istate}}=\mathrm{-6}$ to $\mathrm{-1}$
An internal error exit has caused the rejection of the current set of argument values, $p$. $-{\mathbf{istate}}$ is the value which istate would have taken if the error had not occurred.
${\mathbf{istate}}=\mathrm{-7}$
An internal error exit has occurred when calculating the initial Jacobian.
2: $\mathbf{iflag}$ – IntegerInput
On entry: whether or not the Jacobian being used has been calculated at the beginning of the current iteration. If the Jacobian has been updated then ${\mathbf{iflag}}=1$; otherwise ${\mathbf{iflag}}=2$. The Jacobian is only calculated when convergence to the current argument values has been slow.
3: $\mathbf{ifail1}$ – IntegerInput
On entry: if $\mathrm{-6}\le {\mathbf{istate}}\le \mathrm{-1}$, ifail1 specifies the ifail error number that would be produced were control returned to you. ifail1 is unspecified for values of istate outside this range.
4: $\mathbf{p}\left({\mathbf{m}}\right)$ – Real (Kind=nag_wp) arrayInput
On entry: the current estimate of the
$\mathit{i}$th argument ${p}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,m$.
5: $\mathbf{m}$ – IntegerInput
On entry: $m$, the number of arguments.
6: $\mathbf{f}\left({\mathbf{m}}\right)$ – Real (Kind=nag_wp) arrayInput
On entry: $r$, the residual corresponding to the current argument values, provided $1\le {\mathbf{istate}}\le 5$ or ${\mathbf{istate}}=\mathrm{-7}$. f is unspecified for other values of istate.
7: $\mathbf{pnorm}$ – Real (Kind=nag_wp)Input
On entry: a quantity against which all reductions in norm are currently measured.
8: $\mathbf{pnorm1}$ – Real (Kind=nag_wp)Input
On entry: $p$, the norm of the current arguments. It is set for $1\le {\mathbf{istate}}\le 5$ and is undefined for other values of istate.
9: $\mathbf{eps}$ – Real (Kind=nag_wp)Input
On entry: gives some indication of the convergence rate. It is the current singular value modification factor (see Gay (1976)). It is zero initially and whenever convergence is proceeding steadily. eps is ${\epsilon}^{3/8}$ or greater (where $\epsilon $ may in most cases be considered machine precision) when the singular values of $J$ are approximately zero or when convergence is not being achieved. The larger the value of eps the worse the convergence rate. When eps becomes too large the Newton iteration is terminated.
10: $\mathbf{d}\left({\mathbf{m}}\right)$ – Real (Kind=nag_wp) arrayInput
On entry: $J$, the singular values of the current modified Jacobian matrix. If ${\mathbf{d}}\left(m\right)$ is small relative to ${\mathbf{d}}\left(1\right)$ for a number of Jacobians corresponding to different argument values then the computed results should be viewed with suspicion. It could be that the matching equations do not depend significantly on some argument (which could be due to a programming error in fcn, bc, range or eqn). Alternatively, the system of differential equations may be very ill-conditioned when viewed as an initial value problem, in which case d02saf is unsuitable. This may also be indicated by some singular values being very large. These values of
${\mathbf{d}}\left(\mathit{i}\right)$, for $\mathit{i}=1,2,\dots ,m$, should not be changed.
monit must either be a module subprogram USEd by, or declared as EXTERNAL in, the (sub)program from which d02saf is called. Arguments denoted as Input must not be changed by this procedure.
Note:monit should not return floating-point NaN (Not a Number) or infinity values, since these are not handled by d02saf. If your code inadvertently does return any NaNs or infinities, d02saf is likely to produce unexpected results.
20: $\mathbf{prsol}$ – Subroutine, supplied by the NAG Library or the user.External Procedure
prsol can be used to obtain values of the solution $y$ at a selected point $z$ by integration across the final range $[{\mathbf{x}}\left(1\right),{\mathbf{x}}\left({\mathbf{npoint}}\right)]$. If no output is required d02hbw can be used as the actual argument.
On entry: contains ${x}_{1}$ on the first call. On subsequent calls z contains its previous output value.
On exit: the next point at which output is required. The new point must be nearer ${\mathbf{x}}\left({\mathbf{npoint}}\right)$ than the old.
If z is set to a point outside $[{\mathbf{x}}\left(1\right),{\mathbf{x}}\left({\mathbf{npoint}}\right)]$ the process stops and control returns from d02saf to the (sub)program from which d02saf is called. Otherwise the next call to prsol is made by d02saf at the point z, with solution values ${y}_{1},{y}_{2},\dots ,{y}_{n}$ at z contained in y. If z is set to ${\mathbf{x}}\left({\mathbf{npoint}}\right)$ exactly, the final call to prsol is made with ${y}_{1},{y}_{2},\dots ,{y}_{n}$ as values of the solution at ${\mathbf{x}}\left({\mathbf{npoint}}\right)$ produced by the integration. In general the solution values obtained at ${\mathbf{x}}\left({\mathbf{npoint}}\right)$ from prsol will differ from the values obtained at this point by a call to bc. The difference between the two solutions is the residual $r$. You are reminded that the points ${\mathbf{x}}\left(1\right),{\mathbf{x}}\left(2\right),\dots ,{\mathbf{x}}\left({\mathbf{npoint}}\right)$ are available in the locations ${\mathbf{swp}}(1,4),{\mathbf{swp}}(2,4),\dots ,{\mathbf{swp}}({\mathbf{npoint}},4)$ at all times.
2: $\mathbf{y}\left({\mathbf{n}}\right)$ – Real (Kind=nag_wp) arrayInput
On entry: the solution value
${y}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,n$, at $z$.
3: $\mathbf{n}$ – IntegerInput
On entry: $\mathit{n}$, the total number of differential equations.
prsol must either be a module subprogram USEd by, or declared as EXTERNAL in, the (sub)program from which d02saf is called. Arguments denoted as Input must not be changed by this procedure.
Note:prsol should not return floating-point NaN (Not a Number) or infinity values, since these are not handled by d02saf. If your code inadvertently does return any NaNs or infinities, d02saf is likely to produce unexpected results.
21: $\mathbf{w}({\mathbf{ldw}},{\mathbf{sdw}})$ – Real (Kind=nag_wp) arrayOutput
On exit: in the case of an error exit of the type where the point of failure is returned in ${\mathbf{swp}}(1,5)$, the solution at this point of failure is returned in
${\mathbf{w}}(\mathit{i},1)$, for $\mathit{i}=1,2,\dots ,n$.
On entry: ifail must be set to $0$, $\mathrm{-1}$ or $1$ to set behaviour on detection of an error; these values have no effect when no error is detected.
A value of $0$ causes the printing of an error message and program execution will be halted; otherwise program execution continues. A value of $\mathrm{-1}$ means that an error message is printed while a value of $1$ means that it is not.
If halting is not appropriate, the value $\mathrm{-1}$ or $1$ is recommended. If message printing is undesirable, then the value $1$ is recommended. Otherwise, the value $0$ is recommended. When the value $-\mathbf{1}$ or $\mathbf{1}$ is used it is essential to test the value of ifail on exit.
On exit: ${\mathbf{ifail}}={\mathbf{0}}$ unless the routine detects an error or a warning has been flagged (see Section 6).
6Error Indicators and Warnings
If on entry ${\mathbf{ifail}}=0$ or $\mathrm{-1}$, explanatory error messages are output on the current error message unit (as defined by x04aaf).
Errors or warnings detected by the routine:
${\mathbf{ifail}}=1$
On entry, an element of the parameter convergence control array is zero or negative.
On entry, ${\mathbf{icount}}=\u27e8\mathit{\text{value}}\u27e9$.
Constraint: ${\mathbf{icount}}\ge 0$.
On entry, ${\mathbf{ldswp}}=\u27e8\mathit{\text{value}}\u27e9$ and ${\mathbf{npoint}}=\u27e8\mathit{\text{value}}\u27e9$.
Constraint: ${\mathbf{ldswp}}\ge {\mathbf{npoint}}$.
On entry, ${\mathbf{ldw}}=\u27e8\mathit{\text{value}}\u27e9$ and ${\mathbf{m}}=\u27e8\mathit{\text{value}}\u27e9$.
Constraint: ${\mathbf{ldw}}\ge {\mathbf{m}}$.
On entry, ${\mathbf{ldw}}=\u27e8\mathit{\text{value}}\u27e9$ and ${\mathbf{n}}=\u27e8\mathit{\text{value}}\u27e9$.
Constraint: ${\mathbf{ldw}}\ge {\mathbf{n}}$.
On entry, ${\mathbf{m}}=\u27e8\mathit{\text{value}}\u27e9$ and ${\mathbf{n1}}=\u27e8\mathit{\text{value}}\u27e9$.
Constraint: ${\mathbf{m}}\ge {\mathbf{n1}}$.
On entry, ${\mathbf{n1}}=\u27e8\mathit{\text{value}}\u27e9$.
Constraint: ${\mathbf{n1}}\ge 1$.
On entry, ${\mathbf{n}}=\u27e8\mathit{\text{value}}\u27e9$ and ${\mathbf{n1}}=\u27e8\mathit{\text{value}}\u27e9$.
Constraint: ${\mathbf{n}}\ge {\mathbf{n1}}$.
On entry, ${\mathbf{npoint}}=\u27e8\mathit{\text{value}}\u27e9$.
Constraint: ${\mathbf{npoint}}\ge 2$.
On entry, ${\mathbf{sdw}}=\u27e8\mathit{\text{value}}\u27e9$ and ${\mathbf{m}}=\u27e8\mathit{\text{value}}\u27e9$.
Constraint: ${\mathbf{sdw}}\ge 3\times {\mathbf{m}}+23$.
On entry, ${\mathbf{sdw}}=\u27e8\mathit{\text{value}}\u27e9$ and ${\mathbf{m}}=\u27e8\mathit{\text{value}}\u27e9$.
Constraint: ${\mathbf{sdw}}\ge 4\times {\mathbf{m}}+12$.
On entry, ${\mathbf{ymax}}=\u27e8\mathit{\text{value}}\u27e9$.
Constraint: ${\mathbf{ymax}}\ge 0.0$.
On entry an element of the local error control array is zero or negative.
${\mathbf{ifail}}=2$
The constraints have been violated by the initial parameters.
${\mathbf{ifail}}=3$
The sequence of break-points is not strictly monotonic in their initial specification.
${\mathbf{ifail}}=4$
In the integration from first to last break-point with initial or final parameters, the step size was reduced too far for the integration to proceed. Consider reversing the order of break-points or relaxing the local error control. If this error exit still results, either this routine is not a suitable method for solving the problem, or the initial choice of parameters is very poor.
${\mathbf{ifail}}=5$
In the integration from first to last break-point with initial or final parameters, a suitable initial step could not be found on one of the intervals. Consider reversing the order of break-points or relaxing the local error control. If this error exit still results, either this routine is not suitable for solving the problem, or the initial choice of parameters is very poor.
${\mathbf{ifail}}=6$
During integration the solution exceeded ymax in magnitude, where, on entry, ${\mathbf{ymax}}=\u27e8\mathit{\text{value}}\u27e9$.
${\mathbf{ifail}}=7$
On calculating the initial approximation to the Jacobian, the constraints were violated.
${\mathbf{ifail}}=8$
On perturbing the parameters when calculating the initial approximation to the Jacobian, the monotonicity condition on break-points is violated.
${\mathbf{ifail}}=9$
An initial step-length could be found for integration to proceed with the current parameters.
${\mathbf{ifail}}=10$
The step-length required to calculate the Jacobian to sufficient accuracy is too small.
${\mathbf{ifail}}=11$
On calculating the initial approximation to the Jacobian, the solution to the system of differential equations exceeded ymax in magnitude, where, on entry, ${\mathbf{ymax}}=\u27e8\mathit{\text{value}}\u27e9$.
${\mathbf{ifail}}=12$
The Jacobian has an insignificant column. Make sure that the solution vector depends on all the parameters.
${\mathbf{ifail}}=13$
An internal singular value decomposition has failed. This error can be avoided by changing the initial parameter estimates.
${\mathbf{ifail}}=14$
The Newton iteration has failed to converge. This can indicate a poor initial choice of parameters or a very difficult problem. Consider varying elements of the parameter convergence control if the residuals are small; otherwise vary initial parameter estimates.
${\mathbf{ifail}}=15$
The number of iterations permitted by icount has been exceeded where this was set to a positive value on entry. ${\mathbf{icount}}=\u27e8\mathit{\text{value}}\u27e9$.
${\mathbf{ifail}}=16$
Internal error in calculating Jacobian. Please contact NAG.
${\mathbf{ifail}}=17$
Internal error in calculating residual. Please contact NAG.
${\mathbf{ifail}}=18$
Internal error in integration. Please contact NAG.
${\mathbf{ifail}}=19$
Internal error in Newton method. Please contact NAG.
${\mathbf{ifail}}=-99$
An unexpected error has been triggered by this routine. Please
contact NAG.
See Section 7 in the Introduction to the NAG Library FL Interface for further information.
${\mathbf{ifail}}=-399$
Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library FL Interface for further information.
${\mathbf{ifail}}=-999$
Dynamic memory allocation failed.
See Section 9 in the Introduction to the NAG Library FL Interface for further information.
7Accuracy
If the iteration converges, the accuracy to which the unknown arguments are determined is usually close to that specified by you. The accuracy of the solution (output via prsol) depends on the error tolerances
${\mathbf{e}}\left(\mathit{i}\right)$, for $\mathit{i}=1,2,\dots ,n$. You are strongly recommended to vary all tolerances to check the accuracy of the arguments $p$ and the solution $y$.
8Parallelism and Performance
d02saf is not thread safe and should not be called from a multithreaded user program. Please see Section 1 in FL Interface Multithreading for more information on thread safety.
d02saf makes calls to BLAS and/or LAPACK routines, which may be threaded within the vendor library used by this implementation. Consult the documentation for the vendor library for further information.
Please consult the X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this routine. Please also consult the Users' Note for your implementation for any additional implementation-specific information.
9Further Comments
The time taken by d02saf depends on the complexity of the system of differential equations and on the number of iterations required. In practice, the integration of the differential system (1) is usually by far the most costly process involved. The computing time for integrating the differential equations can sometimes depend critically on the quality of the initial estimates for the arguments $p$. If it seems that too much computing time is required and, in particular, if the values of the residuals (output in monit) are much larger than expected given your knowledge of the expected solution, then the coding of fcn, eqn, range and bc should be checked for errors. If no errors can be found then an independent attempt should be made to improve the initial estimates $p$.
In the case of an error exit in the integration of the differential system indicated by
${\mathbf{ifail}}={\mathbf{4}}$, ${\mathbf{5}}$, ${\mathbf{9}}$ or ${\mathbf{10}}$ you are strongly recommended to perform trial integrations with d02pff to determine the effects of changes of the local error tolerances and of changes to the initial choice of the arguments
${p}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,m$, (that is the initial choice of $p$).
It is possible that by following the advice given in Section 6 an error exit with ${\mathbf{ifail}}={\mathbf{7}}$, ${\mathbf{8}}$, ${\mathbf{9}}$, ${\mathbf{10}}$ or ${\mathbf{11}}$ might be followed by one with ${\mathbf{ifail}}={\mathbf{12}}$ (or vice-versa) where the advice given is the opposite. If you are unable to refine the choice of
${\mathbf{dp}}\left(\mathit{i}\right)$, for $\mathit{i}=1,2,\dots ,n$, such that both these types of exits are avoided then the problem should be rescaled if possible or the method must be abandoned.
The choice of the ‘floor’ values
${\mathbf{pf}}\left(\mathit{i}\right)$, for $\mathit{i}=1,2,\dots ,m$, may be critical in the convergence of the Newton iteration. For each value $i$, the initial choice of ${p}_{i}$ and the choice of ${\mathbf{pf}}\left(i\right)$ should not both be very small unless it is expected that the final argument ${p}_{i}$ will be very small and that it should be determined accurately in a relative sense.
For many problems it is critical that a good initial estimate be found for the arguments $p$ or the iteration will not converge or may even break down with an error exit. There are many mathematical techniques which obtain good initial estimates for $p$ in simple cases but which may fail to produce useful estimates in harder cases. If no such technique is available it is recommended that you try a continuation (homotopy) technique preferably based on a physical argument (e.g., the Reynolds or Prandtl number is often a suitable continuation argument). In a continuation method a sequence of problems is solved, one for each choice of the continuation argument, starting with the problem of interest. At each stage the arguments $p$ calculated at earlier stages are used to compute a good initial estimate for the arguments at the current stage (see Hall and Watt (1976) for more details).
10Example
This example intends to illustrate the use of the break-point and equation solving facilities of d02saf. Most of the facilities which are common to d02saf and d02hbf are illustrated in the example in the specification of d02hbf (which should also be consulted).
The program solves a projectile problem in two media determining the position of change of media, ${p}_{3}$, and the gravity and viscosity in the second medium (${p}_{2}$ represents gravity and ${p}_{4}$ represents viscosity).