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drawar
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Construct extrapolation table with optimal rates of convergence
Let [itex]S[/itex] be a cubic spline interpolant that approximates a function [itex]f[/itex] on the given nodes [itex]x_{0},x_{1},...,x_{n}[/itex] with the boundary conditions: [itex]S''(x_{0})=0[/itex] and [itex]S'(x_{n})=f'(x_{n})[/itex]. Use [itex]S[/itex] to estimate [itex]f(0.1234567)[/itex] where [itex]f(x)=xe^{x}[/itex] and the nodes are [itex]n+1[/itex] uniformly distributed points on [itex][-1;1][/itex] for [itex]n=20, 40, 80, 160, 320[/itex]. Construct an extrapolation table with optimal rates of convergence using these estimates.
I've already computed the approximation to [itex]f(0.1234567)[/itex] using various choices of nodes, the results are listed below:
0.139678933961527 0.139678959983576 0.139679035306608 0.139679035488050 0.139679035532356
I just don't know how to do the last part, that is, find the extrapolation table with optimal convergence. I'm supposed to use Richardson's extrapolation to generate such a table but what hinders me is the truncation error involved in the cubic spline approximation. From what I've learned, extrapolation is applied only when the truncation error has a predictable form, like [itex]M = {N_1}(h) + {K_1}h + {K_2}{h^2} + {K_3}{h^3} + ...[/itex].
edit:
Well after some searches (http://www.alglib.net/interpolation/spline3.php#header4) I've found out that the errors for clamped and natural cubic splines are [itex]O({h^4})[/itex] and [itex]O({h^2})[/itex] respectively, but have no idea how they are derived. My cubic spline is something kind of in-between, like half-clamped and half-natural.
Homework Statement
Let [itex]S[/itex] be a cubic spline interpolant that approximates a function [itex]f[/itex] on the given nodes [itex]x_{0},x_{1},...,x_{n}[/itex] with the boundary conditions: [itex]S''(x_{0})=0[/itex] and [itex]S'(x_{n})=f'(x_{n})[/itex]. Use [itex]S[/itex] to estimate [itex]f(0.1234567)[/itex] where [itex]f(x)=xe^{x}[/itex] and the nodes are [itex]n+1[/itex] uniformly distributed points on [itex][-1;1][/itex] for [itex]n=20, 40, 80, 160, 320[/itex]. Construct an extrapolation table with optimal rates of convergence using these estimates.
Homework Equations
The Attempt at a Solution
I've already computed the approximation to [itex]f(0.1234567)[/itex] using various choices of nodes, the results are listed below:
0.139678933961527 0.139678959983576 0.139679035306608 0.139679035488050 0.139679035532356
I just don't know how to do the last part, that is, find the extrapolation table with optimal convergence. I'm supposed to use Richardson's extrapolation to generate such a table but what hinders me is the truncation error involved in the cubic spline approximation. From what I've learned, extrapolation is applied only when the truncation error has a predictable form, like [itex]M = {N_1}(h) + {K_1}h + {K_2}{h^2} + {K_3}{h^3} + ...[/itex].
edit:
Well after some searches (http://www.alglib.net/interpolation/spline3.php#header4) I've found out that the errors for clamped and natural cubic splines are [itex]O({h^4})[/itex] and [itex]O({h^2})[/itex] respectively, but have no idea how they are derived. My cubic spline is something kind of in-between, like half-clamped and half-natural.
Last edited: