Interpolation is widely used in mathematics and in the sciences – mathematics tends to concentrate on the theoretical side of things and the methods are then used by scientists with the confidence that what they are using works.

What is interpolation? Well an example would be if you are measuring the temperature outside on a particular day then you would take the temperature at certain times and record the temperature – but what about the times in between? We can’t record everything but we might need to know what the temperature was at one of these intermediate times – this is where interpolation comes in. Interpolation is a way of “joining the dots” within (not outside) a dataset so that estimates can be made about the behaviour. This can be done in hundreds of different ways using different methods each with their pros and cons.

In a previous post I talked about the limitations of Euler’s method – well here I’m going to talk about the limitations of polynomial interpolation and specifically the Lagrange representation and one way that the problem can be partially resolved. Don’t misunderstand me – the Lagrange representation is very useful but it, as with almost any numerical technique, has its own problems.

Let’s look at the function $f(x)=\dfrac{1}{1+25x^{2}}$ on the interval $[-1,1]$. This is what it looks like

Graph of f(x)

A graph of f(x) drawn using SAGE Math

 

Now given $n+1$ distinct points $x_{0}, x_{1}, \ldots , x_{n}$ in $[-1,1]$ and their corresponding $y$-values $y_{0}, y_{1}, \ldots , y_{n}$ then the Lagrange representation is defined as $$P_{n}=\sum_{j=0}^{n}y_{j}\ell_{j}$$ where $$\ell_{j}=\prod_{i=0,i\neq k}^{n}\dfrac{x-x_{i}}{x_{k}-x_{i}}$$

This representation can be proved to be unique and that $P(x_{i})=y_{i}$ but I don’t want to get sidetracked with this. So going back to the function $f(x)$ – first I’m going to choose $5$ equally-spaced points along the interval [-1,1]. The resulting interpolating polynomial looks as follows (I am not going to try to write an expression for the interpolating polynomials because they are very difficult to find explicitly and they don’t really tell us anything anyway)

Lagrange representation of f(x)

Lagrange representation of f(x) with 5 equally spaced points

The interpolating curve is shown in red and $f(x)$ is shown in blue. This is not bad, after all we only have $5$ points to work with so we might expect that as the number of points increases we get a more accurate picture – right? Well…no. As the number of points increases we have to increase the degree of the interpolating polynomial and if we choose $10$ equally spaced points on the interval $[-1,1]$ we get this picture

Lagrange representation of f(x)

Lagrange representation of f(x) with 10 equally spaced points

Things are starting to go a bit awry – maybe if we increase the number of points even further then things will start to settle down. Let’s look what happens when we have $16$ equally spaced points.

Lagrange representation of f(x)

Lagrange representation of f(x) with 16 equally spaced points

Maybe that wasn’t such a good idea. Things are starting to get so out of control that as the number of interpolation points (and consequently the degree of the interpolating polynomial) increases, the interpolating polynomial oscillates wildly between both extremely large positive and extremely large negative values near the edges of the interval $[-1,1]$. This behaviour gets so bad $n$ increases that the interpolating polynomial grows without bound near the edges of the interval – this is known as Runge’s phenomenon and makes the interpolating polynomial practically useless.

One way around this is to choose a different set of interpolation points. One of the problems is the equal spacing of the points – to resolve this in part we can choose Chebyshev nodes. Using these Chebyshev nodes we get a very different picture – the following diagram shows what things look like when $10$ points are chosen

Lagrange representation of f(x)

Lagrange representation of f(x) using 10 Chebyshev nodes

Now compare that to what we had before and we see something that is much better behaved. Has Runge’s phenomenon been eliminated? Mostly, yes – but in truth it can never be completely eliminated; the Chebyshev nodes, however, massively reduce the effects of Runge’s phenomenon. Runge’s phenomenon does not always occur but it is something that can go wrong from time-to-time, so as with all numerical methods you have to take care when applying the method to solve problems; there may be another more suitable method that needs to be applied.

Comments closed.