Voyager’s slingshot maneuvers

Posted on by John

This post started out as a thread on X. Here I’ve edited it into a blog post. The image below and the fact cited can be found in JPL Publication 89-24.

Voyager 2 velocity relative to the sun over time

In 1960 it didn’t seem that it would be possible to explore the solar system beyond Jupiter without greatly improved propulsion.

Then the gravitational assist (“slingshot”) maneuver was discovered in 1961. With this new discovery, NASA began making plans to take advantage of an alignment of the outer planets in the 1970s. This led to the Voyager missions.

(Fun fact: In a gravitational assist, the velocity of a spacecraft with respect to the planet doesn’t change, but the velocity relative to the sun changes greatly.)

Note that before encountering Jupiter, Voyager was moving well below solar system escape velocity. As a result of gravitational assists at four planets, the spacecraft is traveling at well over solar system escape velocity.

In a gravitational assist, speed (relative to the sun) increases or decreases, depending on which direction you approach the planet. At the end of the tour, Voyager 2 no longer had a reason to increase speed—it wasn’t possible to visit Pluto—but decreasing speed allowed it to visit both Neptune and its moon Triton.

It may seem that a gravitational assist violates conservation laws: where does the additional momentum come from? From the planets. When Voyager 2 passed Jupiter, Saturn, and Uranus, each of these planets lost some momentum, transferring it to the probe. When the spacecraft passed Neptune, the planet gained some momentum and the probe lost momentum. The changes in momentum were infinitesimal relative to the momentum of the gas giants, but large relative to the momentum of Voyager.

Related post: Error-correcting codes using on Voyager missions

Advantages of Reed-Solomon codes over Golay codes

Posted on by John

When Voyager 1 and 2 left Earth, their internal computers were programmed to use Golay error correction codes. Images transmitted from Jupiter and Saturn were encoded using Golay codes. After leaving Saturn, the software was upgraded to use Reed-Solomon error correction codes.

I didn’t realize how much difference the change of encoding made until I ran across a JPL report that elaborated on the efficiency of both codes.

Encoding these data has a price, and that paid for the old Golay encoding algorithm (used at Jupiter and Saturn) was one code bit overhead for every data bit (100 percent). The new RS encoding scheme reduces this overhead to about 20 percent. In addition, it reduces the number of bit errors from 5 in 100,000 to only 1 in a million!

So switching to Reed-Solomon cut overhead by 5x and improved error correction 50x.

There’s a reason CDs use Reed-Solomon codes and not Golay codes.

Related posts

Hand calculating exp(x)

Posted on by John

The previous post mentioned that Martin Gardner announced that Ramanujan’s conjecture that exp(π√163) in an integer had been proven. This was an April Fool’s joke in 1975. Gardner said

Working by hand, he [Ramanujan] found the value to be 262537412640768743.999999999999… The calculations were tedious, and he was unable to verify the next decimal digits.

Calculating exp(π√163) without a computer—Ramanujan died in 1920—would indeed be tedious, but not insurmountable. Certainly it would not stop someone like Ramanujan from testing a conjecture.

How might you go about calculating exp(π√163) by hand?

Algorithm

One possibility is an algorithm in [1].

\exp(t) = \left(1 + r + \frac{r^2}{2!} + \frac{r^3}{3!} + \cdots \right)^{256} 2^n

where r = t′ / 256, t′ = t − n log 2, and n is chosen to minimize |t′|.

We can choose n so that |t′| < log(2)/2 and so |r| < 0.014. This means the infinite series converges rapidly and not too many terms will be needed, depending on the desired precision.

The calculation x256 can be done by squaring 8 times.

Although the context of this post is hand calculations, this would also be a viable algorithm for a program doing extended precision calculations.

Example

In our case, t = π√163 = 40.1091… and we choose n = 58 so that t′ = −0.09336….

Then r = −0.000364….

So each term in the series will contribute 3 or 4 decimal places to the desired precision at first, more once the factorial denominators get large.

Related posts

[1] Jonathan Borwein and David Bailey. Mathematics by Experiment. Volume 1.

Very accurate pi approximation

Posted on by John

Yesterday I ran across this approximation for π and posted it on X.

π ≈ 3 log(640320) / √163

This approximation is good to 15 decimal places, and so the approximation is exact to within the limits of floating point arithmetic.

I said in a follow up comment that the √163 term looked familiar from Heegner numbers and near integers. I was so close but didn’t make the full connection.

It’s well known that exp(π√163) is very nearly an integer N:

exp(π√163) = N − ε

where N = 262537412640768744 and ε is less than 10−12. The left-hand side of this equation is known as Ramanujan’s constant.

Incidentally, Martin Gardner announced on April Fool’s day in 1975 that Ramanujan’s constant had been proven to be an integer.

There are deep reasons for why Ramanujan’s constant is nearly an integer, but the connection between this fact and the approximation for π is not deep at all: the former is basically the latter, solved for π. Because

N = 640320³ + 744,

log(N) is very nearly 3 log(640320).

Interior of a conic

Posted on by John

What is the interior of a circle? Obvious.

What is the interior of a parabola? Not quite as obvious.

What is the interior of a hyperbola? Not at all obvious.

Is it possible to define interior in a way that applies to all conic sections?

Circles

If you remove a circle from the plane, there are two components left. Which one is the interior and which one is the exterior?

Obviously the bounded part is the interior and the unbounded part is the exterior. But using boundedness as our criteria runs into immediate problems.

Parabolas

If you remove a parabola in the plane, which component is the interior and which is the exterior? You might say there is no interior because both components of the plane minus the parabola are unbounded. Still, if you had to label one of the components the interior, you’d probably say the smaller one.

But is the “smaller” component really smaller? Both components have infinite area. You could patch this up by taking a square centered at the origin and letting its size grow to infinity. The interior of the parabola is the component that has smaller area inside the square all along the way.

Hyperbolas

A hyperbola divides the plane into three regions. Which of these is the interior? If we try to look at area inside an expanding square, it’s not clear which component(s) will have more or less area. Seems like it may depend on the location of the center of the square relative to the position of the hyperbola.

Tangents to a circle

Here’s another way to define the interior of a circle. Look at the set of all lines that are tangent to a point on the circle. None of them go through the interior of the circle. We can define the interior of the circle as the set of points that no tangent line passes through.

This clearly works for a circle, and it’s almost as clear that it would work for an ellipse.

How do we define the exterior of a circle? We could just say it’s the part of the plane that isn’t the interior or the circle itself. But there is a more interesting definition. If the interior of the circle consists of points that tangent lines don’t pass through, the exterior of the circle consists of the set of points that tangent lines do pass thorough. Twice in fact: every point outside the circle is at the intersection of two lines tangent to the circle.

To put it another way, consider the set of all tangent lines to a circle. Every point in the plane is part of zero, one, or two of these lines. The interior of the circle is the set of points that belong to zero tangent lines. The circle is the set of points that belong to one tangent line. The exterior of the circle is the set of points that belong to two tangent lines.

Tangents to a parabola

If we apply the analogous definition to a parabola, the interior of the parabola works out to be the part we’d like to call the interior.

It’s not obvious that every point of the plane not on the parabola and not in the interior lies at the intersection of two tangent lines, but it’s true.

Tangents to a hyperbola

If we look at the hyperbola x² − y² = 1 and draw tangent lines, the interior, the portion of the plane with no crossing tangent lines, is the union of two components, one containing (−∞, −1) and one containing (1, ∞). The exterior is then the component containing the line y = x. In the image above, the pink and lavender components are the interior and the green component is the exterior.

It’s unsatisfying that the interior of the hyperbola is disconnected. Also, I believe the exterior is missing the origin. Both of these annoyances go away when we add points at infinity. In the projective plane, the complement of a conic section consists of two connected components, the interior and the exterior. The origin lies on two tangent lines: one connecting (−∞, −∞) to (∞, ∞) and one connecting (−∞, ∞) to (∞, −∞).

Lessons Learned With the Z3 SAT/SMT Solver

Posted on by Wayne Joubert

Community best practices are useful for helping use a software product more effectively. I’ve just completed a small project using the Z3 solver. Here are some things I’ve learned:

  • My project involves an optimization problem: for a subset of Boolean variables, maximize the count of how many are true. My specific problem is solved much faster with Z3 by converting to a decision problem: set up a base problem to solve for the count being at least a certain fixed number, and iterate using bisection search to find the highest number satisfied. Bisection has been used for this problem before. Also, certain methods may possibly reduce the number of bisection steps.
  • Using Z3  “tactics” can greatly speed up the solve process. I found a good combination of tactics by trial and error, guided in part by the descriptions of the tactics. ChatGPT was of some help in finding good selections to try. An interesting paper discusses use of Monte Carlo tree search to define a good chain of tactics. The branching factor here is high, perhaps around 1000, though there are some redundancies in this number. Training multi-step MCTS might be expensive, but doing this once to get a good static chain of tactics might be worthwhile.
  • The strength of Z3 is in its extremely broad functionality, more so than its raw compute performance. It would be a daunting task for the Z3 team to fully optimize every possible solve option. I examined some of the SMT solver competitions to find faster codes. CVC5 on one case I tried was about twice as fast as Z3; I’ve seen similar reports in the literature. Presently I don’t find it worth the switching costs to use CVC5. One approach might be to use the very capable tactics engine of Z3 and pass the resulting modified problem to CVC5.
  • The specific formulation of the problem can make a big difference in solver performance. I’ve already seen this in the area of iterative linear solvers, for example diagonal matrix scaling can dramatically help (conjugate gradients) or hurt (multigrid) solver performance. Same thing here. Hence the huge importance in good “preprocessing“ for SAT/SMT solvers. One could wish the solver could handle all this automatically without user intervention. However, these powerful tools must be brandished very carefully for maximum effect.
  • Generally, one should move as much of the problem outside of the solver as possible, since the solver is the long pole in the tent in terms of scalability. For example if there is a Z3 integer that must be limited to a certain range and additionally some values in the interval must be blacklisted, it’s better, if possible, to compress all of the valid values into a single interval, to make testing for validity simpler in the Z3 code.
  • Along these lines: the Z3 tactics for propagating constants are not perfect; thus it can help to manually propagate constants (though this unfortunately makes the code more messy). This propagation can also sometimes allow for removal of unneeded constraints, further speeding up performance. Relatedly, some intriguing work by Benjamin Mikek shows how one can use the LLVM code optimizer to optimize the SMT problem in a way that is complementary to Z3 tactics, achieving significant speedup (for more info see here, here and here). I haven’t tried this but it seems promising.
  • Because of the scalability challenge of SMT solvers, various simplifying heuristics to modify the problem can be helpful. For example: solving a subproblem of the main problem and holding the resulting variables fixed in order to solve the rest of the problem. Or solving a simpler, smaller problem first to determine variable presets for the full problem. With these heuristics, one does not in general find the true global optimum; but the result may be adequate.
  • CPU threading does not work for my case (Z3 Python, macOS). Perfect parallelization of SAT and SMP is an unsolved (and perhaps in some sense not fully solvable) problem. One can naïvely parallelize bisection search by converting to trisection, etc., but this does not give perfect speedup (specif., log(P) speedup on P threads). Improvements to parallel bisection in some cases may be possible. Recent work by Armin Biere and colleagues looks promising; as I read it, near perfect speedup up to eight threads (at least for some problems).
  • Some of the main developers of Z3 are on Stack Overflow and have been active in the past answering questions. This seems very useful.

Resources like Handbook of Satisfiability and the proceedings of various SAT/SMT conferences seem helpful. More information on best practices for non-expert practitioners would be a great help to the community. If you know of any good resources, please share in the comments.

Grokking the James Bond chord

Posted on by John

The last chord of the James Bond theme is known as the James Bond chord. If you’ve ever seen a Bond movie, you’ll recognize it. Here’s a quick video showing how to play it on guitar.

I tried asking Grok 3 to generate some LilyPond code for sheet music a few days ago. LilyPond is pretty obscure as programming languages go—it’s not like asking for Python code—so I was skeptical, but Grok did well.

This afternoon I asked Grok to create sheet music for the James Bond chord. Specifically, I asked

  1. What is the “James Bond chord”?
  2. Create sheet music for the James Bond chord using Lilypond.
  3. Move the chord up an octave.

Here’s the output of each step.

What is the chord?

According to Grok,

The “James Bond chord” is a distinctive musical chord famously associated with the James Bond film franchise. It’s not just a single chord but rather a specific voicing and progression that evokes a sense of mystery, danger, and sophistication—perfectly matching the suave spy’s persona. Officially, it’s often identified as an E minor major 9 chord (EmMaj9), though its exact interpretation can vary slightly depending on the context.

The chord was first introduced by composer Monty Norman in the “James Bond Theme” for Dr. No (1962), arranged by John Barry, whose orchestration gave it its iconic swagger. …

First output

Grok produced the following LilyPond code

\version "2.22.0"

\score {
  \new Staff {
    \clef treble
    \key c \major % Default key, no sharps/flats; we'll specify accidentals manually
    \time 4/4
     <e g b dis' fis'>1 % EmMaj9 chord: E G B D# F#
  }
  \layout {}
  \midi {}
}

When compiled this gave the following music.

Grok also produce a MIDI file.

Second output

I wanted to move the chord up an octave for aesthetic reasons, putting the notes inside the staff. Grok complied, changing one line in the code, essentially adding an extra prime mark after each note.

<e' g' b' dis'' fis''>1 % EmMaj9 chord moved up an octave: E' G' B' D#'' F#''

This compiled to the following music.

Problems and prospects

Grok’s not perfect. In another experiment it produced code that wouldn’t compile. But when I told Grok that the code didn’t compile and asked it to try again, it worked.

I tried to remove the time signature, the C symbol. I asked Grok to remove it, and it did not. I asked Grok “How do you get LilyPond to produce music without a time signature?” and it told me two ways, neither of which worked.

I’ve used LilyPond occasionally for years, not to produce full sheets of music but to produce little fragments for blog posts. I’ve always found it a bit mysterious, in part because I jumped in and used it as needed without studying it systematically. There have been times when I thought about including some music notation in a blog post and didn’t want to go to the effort of using LilyPond (or rather the effort of debugging LilyPond if what I tried didn’t work). I may go to the effort more often now that I have a fairly reliable code generator.

Posts using LilyPond

Rates of convergence

Posted on by John

The last several posts have looked at counting two kinds of permutations: those that leave no consecutive integers and those that leave no integer fixed. As n grows large, the proportion of permutations of n elements that fall into both classes approaches 1/e. This post will look a little closer and as how fast each proportion approaches 1/e.

(Incidentally, if you’re looking for an problem to explore, take any statement of the form “X converges to Y” and ask how does X converge to Y. Several of the posts on this blog follow that pattern.)

Definitions

To review our definitions, Q(n) is the number of permutations p of 1, 2, 3, …, n + 1 such that there are no consecutive integers, i.e. for no k does p(k + 1) = p(k) + 1. So, for example, 13254 counts, but 15423 does not count because 2 and 3 are consecutive.

Let D(n) be the number of derangements of 1, 2, 3, …, n, i.e. permutations p such that for no k does p(k) = k. So, for example, 23451 counts, but 21354 does not because 3 remains fixed.

Note that Q(n) counts a certain kind of permutation of n + 1 elements and D(n) counts a certain kind of permutation of n elements. This is a little awkward, but I chose my definition of Q to match OEIS. In order to count the proportion of permutations of n elements with no consecutive elements we should look at  Q(n − 1)/n!. Both Q(n − 1)/n! and D(n)/n! converge to 1/e, but not at the same rate.

D convergence

The convergence of D(n)/n! to 1/e is very fast. In fact,

D(n) = [n! / e]

where [x] is x rounded to the nearest integer. So the difference between D(n)/n! and 1/e is less than 0.5/n!. For n greater than or equal to 18, D(n)/n! = 1/e within the limits of floating point precision. As a rule of thumb, you can see about three significant figures in a plot, so for n greater than about 6 you can’t see the difference between D(n)/n! and 1/e.

Q convergence

The convergence of Q(n − 1)/n! to 1/e is leisurely by comparison.

Here’s why. In this post I showed Q(n − 1) is asymptotic to (n + 1)(n − 1)! / e.

So it is true that Q(n − 1)/n! is asymptotic to 1/e, but it is more accurate to say that it is asymptotic to (n + 1) / ne.

D and Q convergence

If we look at the ratio of (n + 1) Q(n) / n to n! then it converges to 1/e about as fast as the ratio of D(n) to n! does.

Said another way,

(n + 1) Q(n) / n ~ D(n)

The ratio of (n + 1) Q(n) / n to D(n) converges to 1 very quickly, with an error on the order of 10−n.

On to something else

I intend to end the series here. My next post will probably be about something else, something less mathematical.

So here’s the complete series.

  1. Permutations with no consecutive elements
  2. Asymptotic behavior via generating functions
  3. Gluons, quarks, envelopes, and letters
  4. Rates of convergence (this post)

Gluons, quarks, letters, and envelopes

Posted on by John

Yesterday I wrote a couple of posts about a combinatorics question that lead to OEIS sequence A000255. That page has this intriguing line:

This comment derives from a family of recurrences found by Malin Sjodahl for a combinatorial problem for certain quark and gluon diagrams (Feb 27 2010)

I love how pulling on a thread can lead you to unexpected places. What in the world does my little permutation problem have to do with particle physics‽

I found Malin Sjödahl’s website, and apparently the citation above refers to this paper from 2009. The author’s site doesn’t list any papers from 2010. Maybe the paper was published electronically in 2009 and in print in 2010.

Counting tensors

The paper is mostly opaque to me since I don’t know particle physics, but at one point Sjödahl says

The problem of finding all such topologies is equivalent to the number of ways of mapping N elements to each other without mapping a single one to itself …

and says that the solution is

N! \sum_{i=0}^N \frac{(-1)^i}{i!} \to \frac{N!}{e}

Sjödahl is not counting physical permutations but the number possible tensors associated with gluons and quark interaction diagrams.

The right-hand side above is essentially the same as the asymptotic estimate for the function Q(n) in the previous post.

I didn’t find the recurrence that the OEIS comment alluded to. Perhaps I’m not looking at the same paper. Perhaps I’m not looking hard enough because I’m skimming a paper whose contents I don’t understand.

The Montmort letter problem

In more mathematical terminology, Sjödahl is counting the number of permutations of N objects with no fixed point, known as the number derangements of a set N objects.

If you divide by the number of possible permutations N! you have the probability that a random permutation moves every object.

This was historically known as the Montmort letter problem, named after Pierre-Remond Montmort who asked the following question. If N letters are randomly assigned to N envelopes, what is the probability that no letter ends up in the correct envelope?

The probability converges to 1/e as N approaches infinity. It approaches 1/e quickly, and so this is a good approximation even for moderate N. More on the rate of convergence in the next post.

Previous posts in this series

Example using a generating function to find asymptotic behavior

Posted on by John

The previous post looked at how to compute Q(n), the number of permutations of 1, 2, 3, …, n + 1 that contain no consecutive integers. We found a way to numerically compute Q(n) but no analytic expression that would let us compute asymptotics.

The sequence Q(n) is sequence A000255 in OEIS, and OEIS gives the exponential generating function of Q:

\sum_{n=0}^\infty \frac{Q(n)}{n!} = \frac{\exp(-x)}{(1-x)^2}

We can use this function and Theorem 5.2.1 from [1] to get the asymptotic form of Q(n). According to that theorem, the coefficient of xn in our generating function is asymptotically the same as the coefficient of xn in the principle part at the singularity at 1. This principle part is

\frac{1}{e(1-x)^2} + \frac{1}{e(1-x)} = \frac{1}{e}\left(2 + 3x + 4x^2 + 5x^3 + \cdots\right)

and so the coefficient of xn is (n + 2)/e.

So

Q(n) \sim \frac{n + 2}{e} n!

and Q(n) / (n + 1)! approaches 1/e for large n, as conjectured in the previous post.

[1] Herbert S. Wilf. Generatingfunctionology. Second edition.