# NAG CL Interfaced02ubc (bvp_​ps_​lin_​cgl_​vals)

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## 1Purpose

d02ubc evaluates a function, or one of its lower order derivatives, from its Chebyshev series representation at Chebyshev Gauss–Lobatto points on $\left[a,b\right]$. The coefficients of the Chebyshev series representation required are usually derived from those returned by d02uac or d02uec.

## 2Specification

 #include
 void d02ubc (Integer n, double a, double b, Integer q, const double c[], double f[], NagError *fail)
The function may be called by the names: d02ubc or nag_ode_bvp_ps_lin_cgl_vals.

## 3Description

d02ubc evaluates the Chebyshev series
 $S (x¯) = 12 c1 T0 (x¯) + c2 T1 (x¯) + c3T2 (x¯) +⋯+ cn+1 Tn (x¯) ,$
or its derivative (up to fourth order) at the Chebyshev Gauss–Lobatto points on $\left[a,b\right]$. Here ${T}_{j}\left(\overline{x}\right)$ denotes the Chebyshev polynomial of the first kind of degree $j$ with argument $\overline{x}$ defined on $\left[-1,1\right]$. In terms of your original variable, $x$ say, the input values at which the function values are to be provided are
 $xr = - 12 (b-a) cos(π(r-1)/n) + 1 2 (b+a) , r=1,2,…,n+1 , ​$
where $b$ and $a$ are respectively the upper and lower ends of the range of $x$ over which the function is required.
The calculation is implemented by a forward one-dimensional discrete Fast Fourier Transform (DFT).

## 4References

Canuto C (1988) Spectral Methods in Fluid Dynamics 502 Springer
Canuto C, Hussaini M Y, Quarteroni A and Zang T A (2006) Spectral Methods: Fundamentals in Single Domains Springer
Trefethen L N (2000) Spectral Methods in MATLAB SIAM

## 5Arguments

1: $\mathbf{n}$Integer Input
On entry: $n$, where the number of grid points is $n+1$. This is also the largest order of Chebyshev polynomial in the Chebyshev series to be computed.
Constraint: ${\mathbf{n}}>0$ and n is even.
2: $\mathbf{a}$double Input
On entry: $a$, the lower bound of domain $\left[a,b\right]$.
Constraint: ${\mathbf{a}}<{\mathbf{b}}$.
3: $\mathbf{b}$double Input
On entry: $b$, the upper bound of domain $\left[a,b\right]$.
Constraint: ${\mathbf{b}}>{\mathbf{a}}$.
4: $\mathbf{q}$Integer Input
On entry: the order, $q$, of the derivative to evaluate.
Constraint: $0\le {\mathbf{q}}\le 4$.
5: $\mathbf{c}\left[{\mathbf{n}}+1\right]$const double Input
On entry: the Chebyshev coefficients, ${c}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,n+1$.
6: $\mathbf{f}\left[{\mathbf{n}}+1\right]$double Output
On exit: the derivatives ${S}^{\left(q\right)}{x}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,n+1$, of the Chebyshev series, $S$.
7: $\mathbf{fail}$NagError * Input/Output
The NAG error argument (see Section 7 in the Introduction to the NAG Library CL Interface).

## 6Error Indicators and Warnings

NE_ALLOC_FAIL
Dynamic memory allocation failed.
See Section 3.1.2 in the Introduction to the NAG Library CL Interface for further information.
On entry, argument $⟨\mathit{\text{value}}⟩$ had an illegal value.
NE_INT
On entry, ${\mathbf{n}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{n}}>0$.
On entry, ${\mathbf{n}}=⟨\mathit{\text{value}}⟩$.
Constraint: n is even.
On entry, ${\mathbf{q}}=⟨\mathit{\text{value}}⟩$.
Constraint: $0\le {\mathbf{q}}\le 4$.
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.
See Section 7.5 in the Introduction to the NAG Library CL Interface for further information.
NE_NO_LICENCE
Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library CL Interface for further information.
NE_REAL_2
On entry, ${\mathbf{a}}=⟨\mathit{\text{value}}⟩$ and ${\mathbf{b}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{a}}<{\mathbf{b}}$.

## 7Accuracy

Evaluations of DFT to obtain function or derivative values should be an order $n$ multiple of machine precision assuming full accuracy to machine precision in the given Chebyshev series representation.

## 8Parallelism and Performance

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 $n$).