The Runge Phenomenon and Chebyshev interpolation¶

A polynomial approximation to $f(x) = \sin(x)$¶

Here we'll use four easily computable data points to construct a polynomial approximation to $f(x) = \sin(x)$. We know

\begin{array}{c|c} x_i & y_i = \sin(x_i) \\ 0 & 0 \\ \pi/6 & 1/2 \\ \pi/3 & \sqrt{3}/2 \\ \pi/2 & 1 \end{array}
In [1]:
include("polyinterp.jl")
using PyPlot

In [ ]:


In [2]:
xi = [0 π/6 π/3 π/2]
yi = [0 1/2 sqrt(3)/2 1]
(c,b) = newtondivdiff(xi,yi)
x = linspace(-π/6, 4π/6)
p = polyeval(c,b,x)
clf()
plot(xi,yi,"bo")
plot(x, p, "r-")
plot(x,sin(x),"b-")
xlabel("x")
legend(("(xi,yi)", "P(x)", "sin x"), loc="upper left")

Out[2]:
PyObject <matplotlib.legend.Legend object at 0x7f50be736f50>
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In [3]:
plot(xi, [0 0 0 0], "bo")
plot(x, sin(x)-p, "r-")
xlabel("x")
legend(("datapts", "sin x - P(x)"))
grid("on")

In [5]:
sin(1) - polyeval(c,b,1) # evaluate sin(x) - P(x) at x=1

Out[5]:
0.0003849684673111753

Polynomial interpolant of triangular bump, uniform gridpoints¶

In [7]:
xi = linspace(-1.0, 1.0, 13)
yi = [0, 0, 0, 0, 0, 0, 1.0, 0, 0, 0, 0, 0, 0]
plot(xi,yi,"bo")
ylim(-1.1, 1.1)

Out[7]:
(-1.1,1.1)
In [10]:
xi = linspace(-1.0, 1.0, 13)
yi = [0, 0, 0, 0, 0, 0, 1.0, 0, 0, 0, 0, 0, 0]
plot(xi,yi,"bo")

(c,b) = newtondivdiff(xi,yi)
x = linspace(-1.2,1.2,200)
p = polyeval(c,b,x)
plot(x,p, "r-")

xlabel("x")
legend(("datapts", "P(x)"))
ylim((-5,5))

Out[10]:
(-5,5)

Polynomial interpolation of Runge Example Function, uniform gridpoints¶

The Runge Example Function $f(x) = 1/(1+12x^2)$ is like a smoother form of the triangular bump, but it's still problematic for polynomial interpolation.

In [11]:
f(x) = 1./(1+12x.^2)
x = linspace(-1,1,200)
plot(x,f(x),"b-")

Out[11]:
1-element Array{Any,1}:
PyObject <matplotlib.lines.Line2D object at 0x7f50bdf46fd0>

Interpolate the Runge Example Function with 15 data points on [-1, 1]

In [12]:
xi = linspace(-1,1,15)
yi = f(xi)
(c,b) = newtondivdiff(xi,yi)
x = linspace(-1,1,200)
p = polyeval(c,b,x)
plot(xi,yi, "bo")
plot(x,p, "r-")
xlabel("x")
xlim((-1.1, 1.1))

Out[12]:
(-1.1,1.1)
In [13]:
plot(xi,0*xi, "bo")
plot(x,f(x)-p, "r-")
xlabel("x")
xlim((-1.1, 1.1))
title("interpolation error f(x) - p(x)")

Out[13]:
PyObject <matplotlib.text.Text object at 0x7f50bde7f290>

Same with twice as many datapoints

In [15]:
xi = linspace(-1,1,31)
yi = f(xi)
(c,b) = newtondivdiff(xi,yi)
x = linspace(-1,1,200)
p = polyeval(c,b,x)
plot(xi,yi, "bo")
plot(x,p, "r-")
xlabel("x")
xlim((-1.1, 1.1))
ylim((-2,2))

Out[15]:
(-2,2)
In [ ]:
plot(xi,yi, "bo")
plot(x,p, "r-")
xlabel("x")
xlim((-1.1, 1.1))
ylim((-2,2))


The Runge Phenomenon: Increasing the density of uniform data points does not improve the interpolant near the endpoints. In fact it makes it worse!

The error polynomial¶

In [16]:
# define error polynomial for base points xi
function E(x,xi)
p = ones(length(x))
n = length(xi)
for i = 1:n
p = p.*(x-xi[i])
end
p /= factorial(float(n))
end

Out[16]:
E (generic function with 1 method)
In [18]:
xi = linspace(-1,1,17)
x = linspace(-1,1,200)
plot(xi,xi*0,"bo")
plot(x,E(x,xi), "r-")

Out[18]:
1-element Array{Any,1}:
PyObject <matplotlib.lines.Line2D object at 0x7f50be013790>
In [ ]:
xi


The Runge phenomenon is caused by the error polynomial being larger near the endpoints.

Chebyshev points¶

To improve the behavior of the interpolant near the endpoints, use denser datapoints near ends than in middle. Optimum distribution is given by the Chebyshev points

\begin{equation*} x_i = \cos \frac{(2i-1)\pi}{2n} \quad \text{for } i = 1, \ldots, n \end{equation*}
In [20]:
n=17
xi = cos((2*(1:n)-1)π/(2n))
plot(xi,0*xi, "bo")
plot(x, E(x,xi), "r-")

Out[20]:
1-element Array{Any,1}:
PyObject <matplotlib.lines.Line2D object at 0x7f50be4f9250>
In [22]:
n=17
xi = cos((2*(1:n)-1)π/(2n))
yi = f(xi)
plot(xi,yi,"bo")
(c,b) = newtondivdiff(xi,yi)
x = linspace(-1.1, 1.1, 200)
p = polyeval(c,b,x)
plot(xi,yi,"bo")
plot(x,p,"r-")
plot(x,f(x),"b-")
ylim((-2,2))
grid("on")

In [23]:
n=31
xi = cos((2*(1:n)-1)π/(2n))
yi = f(xi)
plot(xi,yi,"bo")
(c,b) = newtondivdiff(xi,yi)
x = linspace(-1.1, 1.1, 200)
p = polyeval(c,b,x)
plot(xi,yi,"bo")
plot(x,p,"r-")
plot(x,f(x),"b-")
ylim((-2,2))
grid("on")

In [24]:
plot(x,f(x)-p,"r-")
ylim(-0.001,0.001)

Out[24]:
(-0.001,0.001)
In [ ]: