NAG Library Function Document
nag_ode_bvp_ps_lin_cgl_vals (d02ubc) evaluates a function, or one of its lower order derivatives, from its Chebyshev series representation at Chebyshev Gauss–Lobatto points on
. The coefficients of the Chebyshev series representation required are usually derived from those returned by nag_ode_bvp_ps_lin_coeffs (d02uac)
or nag_ode_bvp_ps_lin_solve (d02uec)
||nag_ode_bvp_ps_lin_cgl_vals (Integer n,
const double c,
nag_ode_bvp_ps_lin_cgl_vals (d02ubc) evaluates the Chebyshev series
or its derivative (up to fourth order) at the Chebyshev Gauss–Lobatto points on
denotes the Chebyshev polynomial of the first kind of degree
. In terms of your original variable,
say, the input values at which the function values are to be provided are
are respectively the upper and lower ends of the range of
over which the function is required.
The calculation is implemented by a forward one-dimensional discrete Fast Fourier Transform (DFT).
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
n – IntegerInput
On entry: , where the number of grid points is . This is also the largest order of Chebyshev polynomial in the Chebyshev series to be computed.
a – doubleInput
On entry: , the lower bound of domain .
b – doubleInput
On entry: , the upper bound of domain .
q – IntegerInput
On entry: the order, , of the derivative to evaluate.
c – const doubleInput
On entry: the Chebyshev coefficients,
, for .
f – doubleOutput
On exit: the derivatives
, for , of the Chebyshev series, .
fail – NagError *Input/Output
The NAG error argument (see Section 3.6
in the Essential Introduction).
6 Error Indicators and Warnings
Dynamic memory allocation failed.
On entry, argument had an illegal value.
On entry, .
On entry, .
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
On entry, and .
Evaluations of DFT to obtain function or derivative values should be a small multiple of machine precision assuming full accuracy to machine precision in the given Chebyshev series representation.
The number of operations is of the order and the memory requirements are ; thus the computation remains efficient and practical for very fine discretizations (very large values of ).
See Section 9
in nag_ode_bvp_ps_lin_solve (d02uec).