# Contraction Mapping: Kepler's Fixed Point Iteration

### If Kepler were alive right now, might he use Python to help in his quest for knowledge? We take a look at a specific example.

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Join For FreeThe contraction mapping theorem says that if a function moves points closer together, then there must be some point the function doesn’t move. We’ll make this statement more precise and give a historically important application.

## Definitions and Theorem

A function *f* on a metric space *X* is a **contraction** if there exists a constant *q* with 0 ≤ *q* < 1 such that for any pair of points *x* and *y* in *X*,

*d*( *f*(*x*), *f*(*y*) ) ≤ *q* *d*(*x*, *y*)

where *d* is the metric on *X*.

A point *x* is a **fixed point** of a function *f* if *f*(*x*) = *x*.

**Banach’s fixed point theorem**, also known as the **contraction mapping theorem**, says that every contraction on a complete metric space has a fixed point. The proof is constructive: start with any point in the space and repeatedly apply the contraction. The sequence of iterates will converge to the fixed point.

## Application: Kepler’s Equation

Kepler’s equation in for an object in an elliptical orbit says

*M* + *e* sin *E* = *E*

where *M* is the **mean anomaly**, *e* is the **eccentricity**, and *E* is the **eccentric anomaly**. These “anomalies” are parameters that describe the location of an object in orbit. Kepler solved for *E* given *M* and *e* using the contraction mapping theorem, though he didn’t call it that.

Kepler speculated that it is not possible to solve for *E* in closed form—he was right—and used a couple iterations [1] of

*f*(*E*) = *M* + *e* sin *E*

to find an approximate fixed point. Since the mean anomaly is a good approximation for the eccentric anomaly, *M* makes a good starting point for the iteration. The iteration will converge from any starting point, as we will show below, but you’ll get a useful answer sooner starting from a good approximation.

## Proof of Convergence

Kepler came up with his idea for finding *E* around 1620, and Banach stated his fixed point theorem three centuries later. Kepler had the idea of Banach’s theorem, but he didn’t have a rigorous formulation of the theorem or a proof.

In modern terminology, the real line is a complete metric space and so we only need to prove that the function *f* above is a contraction. By the mean value theorem, it suffices to show that the absolute value of its derivative is less than 1. That is, we can use an upper bound on |*f *‘| as the *q* in the definition of contraction.

Now

*f ‘* (*E*) = *e* cos *E*

and so

|*f* ‘(*E*)| ≤ *e*

for all *E*. If our object is in an elliptical orbit, *e* < 1 and so we have a contraction.

## Example

The following example comes from [2], though the author uses Newton’s method to solve Kepler’s equation. This is more efficient, but anachronistic.

Consider a satellite on a geocentric orbit with eccentricity

e= 0.37255. Determine the true anomaly at three hours after perigee passage, and calculate the position of the satellite.

The author determines that *M* = 3.6029 and solves Kepler’s equation

*M* + *e* sin *E* = *E*

for *E*, which she then uses to solve for the true anomaly and position of the satellite.

The following Python code shows the results of the first 10 iterations of Kepler’s equation.

```
from math import sin
M = 3.6029
e = 0.37255
E = M
for _ in range(10):
E = M + e*sin(E)
print(E)
```

This produces

```
3.437070
3.494414
3.474166
3.481271
3.478772
3.479650
3.479341
3.479450
3.479412
3.479425
```

and so it appears the iteration has converged to *E* = 3.4794 to four decimal places.

Note that this example has a fairly large eccentricity. Presumably Kepler would have been concerned with much smaller eccentricities. The eccentricity of Jupiter’s orbit, for example, is around 0.05. For such small values of *e* the iteration would converge more quickly.

## Related Posts

[1] Bertil Gustafsson saying in his book Scientific Computing: A Historical Perspective that Kepler only used two iterations. Since *M* gives a good starting approximation to *E*, two iterations would give a good answer. I imagine Kepler would have done more iterations if necessary but found empirically that two was enough. Incidentally, it appears Gustaffson has a sign error in his statement of Kepler’s equation.

[2] Euler Celestial Analysis by Dora Musielak.

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