NAG Library Function Document
nag_ode_bvp_ps_lin_solve (d02uec)
1 Purpose
nag_ode_bvp_ps_lin_solve (d02uec) finds the solution of a linear constant coefficient boundary value problem by using the Chebyshev integration formulation on a Chebyshev Gauss–Lobatto grid.
2 Specification
#include <nag.h> 
#include <nagd02.h> 
void 
nag_ode_bvp_ps_lin_solve (Integer n,
double a,
double b,
Integer m,
const double c[],
double bmat[],
const double y[],
const double bvec[],
double f[],
double uc[],
double *resid,
NagError *fail) 

3 Description
nag_ode_bvp_ps_lin_solve (d02uec) solves the constant linear coefficient ordinary differential problem
subject to a set of
$m$ linear constraints at points
${y}_{\mathit{i}}\in \left[a,b\right]$, for
$\mathit{i}=1,2,\dots ,m$:
where
$1\le m\le 4$,
$B$ is an
$m\times \left(m+1\right)$ matrix of constant coefficients and
${\beta}_{i}$ are constants. The points
${y}_{i}$ are usually either
$a$ or
$b$.
The function
$f\left(x\right)$ is supplied as an array of Chebyshev coefficients
${c}_{j}$,
$j=0,1,\dots ,n$ for the function discretized on
$n+1$ Chebyshev Gauss–Lobatto points (as returned by
nag_ode_bvp_ps_lin_cgl_grid (d02ucc)); the coefficients are normally obtained by a previous call to
nag_ode_bvp_ps_lin_coeffs (d02uac). The solution and its derivatives (up to order
$m$) are returned, in the form of their Chebyshev series representation, as arrays of Chebyshev coefficients; subsequent calls to
nag_ode_bvp_ps_lin_cgl_vals (d02ubc) will return the corresponding function and derivative values at the Chebyshev Gauss–Lobatto discretization points on
$\left[a,b\right]$. Function and derivative values can be obtained on any uniform grid over the same range
$\left[a,b\right]$ by calling the interpolation function
nag_ode_bvp_ps_lin_grid_vals (d02uwc).
4 References
Clenshaw C W (1957) The numerical solution of linear differential equations in Chebyshev series Proc. Camb. Phil. Soc. 53 134–149
Coutsias E A, Hagstrom T and Torres D (1996) An efficient spectral method for ordinary differential equations with rational function coefficients Mathematics of Computation 65(214) 611–635
Greengard L (1991) Spectral integration and twopoint boundary value problems SIAM J. Numer. Anal. 28(4) 1071–80
Lundbladh A, Hennigson D S and Johannson A V (1992) An efficient spectral integration method for the solution of the Navier–Stokes equations Technical report FFA–TN 1992–28 Aeronautical Research Institute of Sweden
Muite B K (2010) A numerical comparison of Chebyshev methods for solving fourthorder semilinear initial boundary value problems Journal of Computational and Applied Mathematics 234(2) 317–342
5 Arguments
 1:
$\mathbf{n}$ – IntegerInput

On entry: $n$, where the number of grid points is $n+1$.
Constraint:
${\mathbf{n}}\ge 8$ and
n is even.
 2:
$\mathbf{a}$ – doubleInput

On entry: $a$, the lower bound of domain $\left[a,b\right]$.
Constraint:
${\mathbf{a}}<{\mathbf{b}}$.
 3:
$\mathbf{b}$ – doubleInput

On entry: $b$, the upper bound of domain $\left[a,b\right]$.
Constraint:
${\mathbf{b}}>{\mathbf{a}}$.
 4:
$\mathbf{m}$ – IntegerInput

On entry: the order, $m$, of the boundary value problem to be solved.
Constraint:
$1\le {\mathbf{m}}\le 4$.
 5:
$\mathbf{c}\left[{\mathbf{n}}+1\right]$ – const doubleInput

On entry: the Chebyshev coefficients
${c}_{j}$,
$j=0,1,\dots ,n$, for the right hand side of the boundary value problem. Usually these are obtained by a previous call of
nag_ode_bvp_ps_lin_coeffs (d02uac).
 6:
$\mathbf{bmat}\left[{\mathbf{m}}\times \left({\mathbf{m}}+1\right)\right]$ – doubleInput/Output

Note: the $\left(i,j\right)$th element of the matrix is stored in ${\mathbf{bmat}}\left[\left(j1\right)\times {\mathbf{m}}+i1\right]$.
On entry:
${\mathbf{bmat}}\left[\mathit{j}\times {\mathbf{m}}+\mathit{i}1\right]$ must contain the coefficients
${B}_{\mathit{i},\mathit{j}+1}$, for
$\mathit{i}=1,2,\dots ,m$ and
$\mathit{j}=0,1,\dots ,m$, in the problem formulation of
Section 3.
On exit: the coefficients have been scaled to form an equivalent problem defined on the domain $\left[1,1\right]$.
 7:
$\mathbf{y}\left[{\mathbf{m}}\right]$ – const doubleInput

On entry: the points,
${y}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,m$, where the boundary conditions are discretized.
 8:
$\mathbf{bvec}\left[{\mathbf{m}}\right]$ – const doubleInput

On entry: the values,
${\beta}_{\mathit{i}}$, for
$\mathit{i}=1,2,\dots ,m$, in the formulation of the boundary conditions given in
Section 3.
 9:
$\mathbf{f}\left[{\mathbf{m}}+1\right]$ – doubleInput/Output

On entry: the coefficients,
${f}_{\mathit{j}}$, for
$\mathit{j}=1,2,\dots ,m+1$, in the formulation of the linear boundary value problem given in
Section 3. The highest order term,
${\mathbf{f}}\left[{\mathbf{m}}\right]$, needs to be nonzero to have a well posed problem.
On exit: the coefficients have been scaled to form an equivalent problem defined on the domain $\left[1,1\right]$.
 10:
$\mathbf{uc}\left[\left({\mathbf{n}}+1\right)\times \left({\mathbf{m}}+1\right)\right]$ – doubleOutput

Note: the $\left(i,j\right)$th element of the matrix is stored in ${\mathbf{uc}}\left[\left(j1\right)\times \left({\mathbf{n}}+1\right)+i1\right]$.
On exit: the Chebyshev coefficients in the Chebyshev series representations of the solution and derivatives of the solution to the boundary value problem. The coefficients of
$U$ are stored as the first
$n+1$ elements of
uc, the first derivative coefficients are stored as the next
$n+1$ elements of
uc, and so on.
 11:
$\mathbf{resid}$ – double *Output

On exit: the maximum residual resulting from substituting the solution vectors returned in
uc into both linear equations of
Section 3 representing the linear boundary value problem and associated boundary conditions. That is
 12:
$\mathbf{fail}$ – NagError *Input/Output

The NAG error argument (see
Section 2.7 in How to Use the NAG Library and its Documentation).
6 Error Indicators and Warnings
 NE_ALLOC_FAIL

Dynamic memory allocation failed.
See
Section 2.3.1.2 in How to Use the NAG Library and its Documentation for further information.
 NE_BAD_PARAM

On entry, argument $\u2329\mathit{\text{value}}\u232a$ had an illegal value.
 NE_CONVERGENCE

During iterative refinement, convergence was achieved, but the residual is more than $100\times \mathit{machineprecision}$. $\text{Residual achieved on convergence}=\u2329\mathit{\text{value}}\u232a$.
 NE_INT

On entry, ${\mathbf{m}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: $1\le {\mathbf{m}}\le 4$.
On entry,
${\mathbf{n}}=\u2329\mathit{\text{value}}\u232a$.
Constraint:
n is even.
On entry, ${\mathbf{n}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{n}}\ge 8$.
 NE_INTERNAL_ERROR

An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact
NAG for assistance.
An unexpected error has been triggered by this function. Please contact
NAG.
See
Section 2.7.6 in How to Use the NAG Library and its Documentation for further information.
Internal error while unpacking matrix during iterative refinement.
Please contact
NAG.
 NE_NO_LICENCE

Your licence key may have expired or may not have been installed correctly.
See
Section 2.7.5 in How to Use the NAG Library and its Documentation for further information.
 NE_REAL_2

On entry, ${\mathbf{a}}=\u2329\mathit{\text{value}}\u232a$ and ${\mathbf{b}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{a}}<{\mathbf{b}}$.
 NE_REAL_ARRAY

On entry, ${\mathbf{f}}\left[{\mathbf{m}}\right]=0.0$.
 NE_SINGULAR_MATRIX

Singular matrix encountered during iterative refinement.
Please check that your system is well posed.
 NE_TOO_MANY_ITER

During iterative refinement, the maximum number of iterations was reached.
$\text{Number of iterations}=\u2329\mathit{\text{value}}\u232a$ and $\text{residual achieved}=\u2329\mathit{\text{value}}\u232a$.
7 Accuracy
The accuracy should be close to machine precision for well conditioned boundary value problems.
8 Parallelism and Performance
nag_ode_bvp_ps_lin_solve (d02uec) is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
nag_ode_bvp_ps_lin_solve (d02uec) 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 function. Please also consult the
Users' Note for your implementation for any additional implementationspecific information.
The number of operations is of the order $n\mathrm{log}\left(n\right)$ and the memory requirements are $\mathit{O}\left(n\right)$; thus the computation remains efficient and practical for very fine discretizations (very large values of $n$). Collocation methods will be faster for small problems, but the method of nag_ode_bvp_ps_lin_solve (d02uec) should be faster for larger discretizations.
10 Example
This example solves the thirdorder problem $4{U}_{xxx}+3{U}_{xx}+2{U}_{x}+U=2\mathrm{sin}x2\mathrm{cos}x$ on $\left[\pi /2,\pi /2\right]$ subject to the boundary conditions $U\left[\pi /2\right]=0$, $3{U}_{xx}\left[\pi /2\right]+2{U}_{x}\left[\pi /2\right]+U\left[\pi /2\right]=2$, and $3{U}_{xx}\left[\pi /2\right]+2{U}_{x}\left[\pi /2\right]+U\left[\pi /2\right]=2$ using the Chebyshev integration formulation on a Chebyshev Gauss–Lobatto grid of order $16$.
10.1 Program Text
Program Text (d02uece.c)
10.2 Program Data
Program Data (d02uece.d)
10.3 Program Results
Program Results (d02uece.r)