Kepler and the contraction mapping theorem

Johannes Kepler

The 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(xy)

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

Me sin EE

where M is the mean anomalye 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 |‘| 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

Me sin EE

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.

John Napier

Julian Havil has written a new book John Napier: Life, Logarithms, and Legacy.

I haven’t read more than the introduction yet — a review copy arrived just yesterday — but I imagine it’s good judging by who wrote it. Havil’s book Gamma is my favorite popular math book. (Maybe I should say “semi-popular.” Havil’s books have more mathematical substance than most popular books, but they’re still aimed at a wide audience. I think he strikes a nice balance.) His latest book is a scientific biography, a biography with an unusual number of equations and diagrams.

Napier is best known for his discovery of logarithms. (People debate endlessly whether mathematics is discovered or invented. Logarithms are so natural — pardon the pun — that I say they were discovered. I might describe other mathematical objects, such as Grothendieck’s schemes, as inventions.) He is also known for his work with spherical trigonometry, such as Napier’s mnemonic. Maybe Napier should be known for other things I won’t know about until I finish reading Havil’s book.

Fermat’s proof of his last theorem

Fermat famously claimed to have a proof of his last theorem that he didn’t have room to write down. Mathematicians have speculated ever since what this proof must have been, though everyone is convinced the proof must have been wrong.

The usual argument for Fermat being wrong is that since it took over 350 years, and some very sophisticated mathematics, to prove the theorem, it’s highly unlikely that Fermat had a simple proof. That’s a reasonable argument, but somewhat unsatisfying because it’s risky business to speculate on what a proof must require. Who knows how complex the proof of FLT in The Book is?

André Weil offers what I find to be a more satisfying argument that Fermat did not have a proof, based on our knowledge of Fermat himself. Dana Mackinzie summarizes Weil’s argument as follows.

Fermat repeatedly bragged about the n = 3 and n = 4 cases and posed them as challenges to other mathematicians … But the never mentioned the general case, n = 5 and higher, in any of his letters. Why such restraint? Most likely, Weil argues, because Fermat had realized that his “truly wonderful proof” did not work in those cases.

Dana comments:

Every mathematician has had days like this. You think you have a great insight, but then you go out for a walk, or you come back to the problem the next day, and you realize that your great idea has a flaw. Sometimes you can go back and fix it. And sometimes you can’t.

The quotes above come from The Universe in Zero Words. I met Dana Mackinzie in Heidelberg a few weeks ago, and when I came home I looked for this book and his book on the formation of the moon, The Big Splat.

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NYT Book of Physics and Astronomy

I’ve enjoyed reading The New York Times Book of Physics and Astronomy, ISBN 1402793200, a collection of 129 articles written between 1888 and 2012. Its been much more interesting than its mathematical predecessor. I’m not objective — I have more to learn from a book on physics and astronomy than a book on math — but I think other readers might also find this new book more interesting.

I was surprised by the articles on the bombing of Hiroshima and Nagasaki. New York Times reporter William Lawrence was allowed to go on the mission over Nagasaki. He was not on the plane that dropped the bomb, but was in one of the other B-29 Superfortresses that were part of the mission. Lawrence’s story was published September 9, 1945, exactly one month later. Lawrence was also allowed to tour the ruins of Hiroshima. His article on the experience was published September 5, 1945. I was surprised how candid these articles were and how quickly they were published. Apparently military secrecy evaporated rapidly once WWII was over.

Another thing that surprised me was that some stories were newsworthy more recently than I would have thought. I suppose I underestimated how long it took to work out the consequences of a major discovery. I think we’re also biased to think that whatever we learned as children must have been known for generations, even though the dust may have only settled shortly before we were born.

Concepts, explosions, and developments

Paul Halmos divided progress in math into three categories: concepts, explosions, and developments. This was in his 1990 article “Has progress in mathematics slowed down?”. (His conclusion was no.) This three-part classification not limited to math and could be useful in other areas.

Concepts are organizational ideas, frameworks, new vocabulary. Some of his examples were category theory and distributions (generalized functions).

Explosions solve old problems and generate a lot of attention among mathematicians and in the popular press. As Halmos puts is, “hot news not only for the Transactions, but also for the Times for a day, for Time for a week, and for student mathematics clubs for many months.” He cites the solution to the Four Color Theorem as an example. He no doubt would have cited Fermat’s Last Theorem had he written his article five years later.

Developments are “deep and in some cases even breathtaking developments (but not explosions) of the kind that might not make the Times, but could possibly get Fields medals for their discoverers.” One example he gives is the Atiyah-Singer index theorem.

The popular impression of math and science is that progress is all about explosions though it’s more about concepts and developments.

Related: Birds and Frogs by Freeman Dyson [pdf]

Social networks in fact and fiction

SIAM News arrived this afternoon and had an interesting story on the front page: Applying math to myth helps separate fact from fiction.

In a nutshell, the authors hope to get some insight into whether a myth is based on fact by seeing whether the social network of characters in the myth looks more like a real social network or like the social network in a work of deliberate fiction. For instance, the social networks of the Iliad and Beowulf look more like actual social networks than does the social network of Harry Potter. Real social networks follow a power law distribution more closely than do social networks in works of fiction.

This could be interesting. For example, the article points out that some scholars believe Beowulf has a basis in historical events, though they don’t believe that Beowulf the character corresponds to a historical person. The network approach lends support to this position: the Beowulf social network looks more realistic when Beowulf himself is removed.

It seems however that an accurate historical account might have a suspicious social network, not because the events in it were made up but because they were filtered according to what the historian thought was important.

Click to learn more about consulting for complex networks

 

Baroque computers

From an interview with Neal Stephenson, giving some background for his Baroque Cycle:

Leibniz [1646-1716] actually thought about symbolic logic and why it was powerful and how it could be put to use. He went from that to building a machine that could carry out logical operations on bits. He knew about binary arithmetic. I found that quite startling. Up till then I hadn’t been that well informed about the history of logic and computing. I hadn’t been aware that anyone was thinking about those things so far in the past. I thought it all started with [Alan] Turing. So, I had computers in the 17th century.

Fourier series before Fourier

I always thought that Fourier was the first to come up with the idea of expressing general functions as infinite sums of sines and cosines. Apparently this isn’t true.

The idea that various functions can be described in terms of Fourier series … was for the first time proposed by Daniel Bernoulli (1700–1782) to solve the one-dimensional wave equation (the equation of motion of a string) about 50 years before Fourier. … However, no one contemporaneous to D. Bernoulli accepted the idea as a general method, and soon the study was forgotten.

Source: The Nonlinear World

Perhaps Fourier’s name stuck to the idea because he developed it further than Bernoulli did.

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History of weather prediction

I’ve just started reading Invisible in the Storm: The Role of Mathematics in Understanding Weather, ISBN 0691152721.

The subtitle may be a little misleading. There is a fair amount of math in the book, but the ratio of history to math is pretty high. You might say the book is more about the role of mathematicians than the role of mathematics. As Roger Penrose says on the back cover, the book has “illuminating descriptions and minimal technicality.”

Someone interested in weather prediction but without a strong math background would enjoy reading the book, though someone who knows more math will recognize some familiar names and theorems and will better appreciate how they fit into the narrative.

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