The quest to decode the Mandelbrot set
quantamagazine.org
quantamagazine.org
You’ve got one guy with a relentless spirit to continue with mathematics in is spare time after being blacklisted from mainstream academia because of antisemitism.
Another is a childhood prodigy, who set the record for the youngest American IMO team member, but got burned out as an adult and went into finance but found his way back through the mentorship of another mathematician.
And a third was a biology major. After graduating, he worked as a baker. But he wanted a career change so he entered a master’s program in math and proved an impressive result.
Nobody could do that, Jews or not.
I donated time on PA-risc workstations to the effort and was surprised to hear that the 2 machines contributed more to the final answer then 100s of other contributors. Something about how HP's compiler/chip preserved more accurate in the intermediate results than others. That surprised me since AFAIK the PA-risc is just a normal 64 bit floating point unit, which doesn't every have more precision for intermediate results. I believe PCs at the time often used the x86, which has 80 bits of precision for the intermediate results.
I believe the project was a success, but I don't remember the conclusion.
[...] by computing the area of the M-set using lots of terms in a series (Laurent Series?), the upper bound of the area seems to converge about at 1.72 (the graph gets quite flat, and seems to have an asymptote there), and by counting pixals more and more accurately, you seem to get a lower bound of very close to 1.52. Both these bounds are close to the values the methods would produce in the limit - that is, it is NOT the case that these numbers would get closer if a finer grid were used, or more terms were taken in the series. So, why the difference of 10% or so? No one knows.
I found a viewer that works in the browser:
> Mandelbrot Viewer is a universal (desktop and mobile) vanilla JS implementation of a plain Mandelbrot set renderer – supporting mouse, touch and keyboard interaction.
https://brazzy.de/en/Mandelbrot.php
Even for that, the actual Mandelbrot calculations were the smaller part. It's really amazing how such a trivially simple formula spawns such endless complexity.
Which implies something important, no doubt.
fragColor = vec4(vec3(cos(c), cos(1.1214 * c) , cos(.8 * c)) / 2. + .5, 1.);
Where c is the number of iterations divided by the scale.(I don't know if this is a known compromise of your technique (I couldn't find it mentioned), but I occassionally get a ring of varying thickness centred in the render with an inverted coluring to the rest of the pixels, or sometimes a solid colour. It varies is size and comes and goes without an obvious correlation to zoom level.)
I'm representing complex numbers as (real_mantissa + i * imag_mantissa) * 2^exp where real_mantissa and imag_mantissa are themselves 32 bit floats, and I can reduce the frequency of the colored rings in my shader by keeping the mantissas around ~1000 instead of ~1 (and reducing the exponent to keep the value the same) so that they are less likely to go subnormal, but I can't seem to get rid of the colored rings entirely.
I don't actually know the pathway through the code from subnormal underflow -> colored rings, but if you run a CUDA renderer with the same algorithm, the rings appear when you set the flag for "round subnormal floats to zero."
https://mandeljs.hgreer.com/?;re=-0.751165720536567020384363...
Now people write viewers that run lighting fast in a browser thanks to WebGPU! Like:
https://www.reddit.com/r/fractals/comments/o7l4bm/please_try...
At that time I didn't know what was a complex number, but was fascinated by the whole concept of fractals and how complex structures could be created with a relative simple program.
I ran in to the precision limit pretty quickly, same as you. I didn't understand computers well enough to know that's what the problem was, and I remember spending hours pouring over my code, trying to figure out where the bug was. Good times. :D
Software emulated floats on Amiga 2000 were really, really slow.
I still have that A500 but who knows where I put those floppies, I'm tempted to turn it on but I'm scared the PSU will blow itself...
The A500 themselves are built like tanks. Chances are it just works.
But watch out for trapdoor expansion. Most likely has a varta barrel battery in it, which will eventually leak and damage the expansion, and possibly also the computer itself.
I would recommend opening that trapdoor and removing/inspecting anything installed there as soon as possible.
These barrel batteries are only used to keep RTC, and the board will be fine w/o.
Now that's slow.
I thought "this will never work", but I was amazed that it did in fact work. I remember letting it run overnight to find a complete 160x200 4-color image the next morning. Just clearing the screen using a loop to fill 8192 bytes with 0s took over a minute.
Everything about the computers at the time was so terrible, I don't feel any nostalgia for those days or that technology.
FRACTINT was great because I could use my 286 and it was fairly fast because used integer whenever possible.
https://en.wikipedia.org/wiki/Buddhabrot
It is the probability distribution (i.e. the most frequent locations visited) over the trajectory of the points that escape the plane.
This actually sounds to me to be fine goal. An AI that sounds out unexplored depths to reveal interesting sights that maybe resemble what we see in the world at our level or cool patterns that potentially are a delight to the eye would be pretty cool.
My understanding is that there's a certain number of bulbs, each centred around a point which becomes periodic with period p after k steps. But how do they all stick together?
A consequence of MLC is that the combinatorial picture given by Lavaur's algorithm and related analyses is "complete" -- all dynamical information is available from the combinatorial models.
The last part is about fractals, especially the Mandelbrot set. With some theoretical and some practical articles.
For some reason reading this article is making me wonder about the difference between the information required to generate something like a mandelbrot, knowing the underlying rule, and the information required to represent it as it is, without following the rule. Or e.g., the difference between the information of the generating rule and the information implicitly represented through the time or number of operations needed to generate it.
It seems like there's some analogy between potential and kinetic energy, and kolmogorov complexity and something else, that I'm having trouble putting my finger on. Even if you have a simple generating algorithm that might be small in a kolmogorov complexity sense, if that algorithm entails a repeating something over a large number of operations, the resulting object would be complex, so there's an implied total complexity as well as an "generating" one.
Maybe this is some basic computational complexity concept but if so I'm not recalling this, or am being dense. E.g., I'm used to discussions of "compressibility" but not of the "generating representation information cost" versus "execution cost".
Perhaps you’d be interested in https://en.wikipedia.org/wiki/Landauer%27s_principle. Turns out there may be a minimum energy required to decrease entropy. Jade has a really good overview https://youtu.be/XY-mbr-aAZE?si=7DvSs2DMudsh6gk8
Dessalles's algorithmic simplicity theory of (cognitive) relevance is formulated in these terms.
>Situations are relevant to human beings when they appear simpler to describe than to generate
The discrepancy between generation complexity
>the complexity (minimal description) of all parameters that have to be set for the situation s to exist in the "world"
i.e, the "pixels"
and description complexity
> the length of the shortest available description of s (that makes s unique)
i.e. the mandelbrot formula
is named Unexpectedness in this framework.
https://telecom-paris.hal.science/hal-03814119/document
https://simplicitytheory.telecom-paris.fr/
Dessalles published a paper in 2022, Unexpectedness and Bayes’ Rule
https://cifma.github.io/Papers-2021/CIFMA_2021_paper_13.pdf
>A great number of methods and of accounts of rationality consider at their foundations some form of Bayesian inference. Yet, Bayes’ rule, because it relies upon probability theory, requires specific axioms to hold (e.g. a measurable space of events). This short document hypothesizes that Bayes’ rule can be seen as a specific instance of a more general inferential template, that can be expressed also in terms of algorithmic complexities, namely through the measure of unexpectedness proposed by Simplicity Theory.
Maybe there is a way to plug this into the https://en.wikipedia.org/wiki/Buddhabrot fractal someone mentioned above.
... It's all elephants. The -R spike at the main disc is period 2, the fork around +/-i is period 3, and so on to infinity at 0.25+0i.
MLC might just be one of those facts that is true but unprovable!
I still don't understand.
https://en.wikipedia.org/wiki/Locally_connected_space
Unfortunately I don't have the slightest idea what it actually means... that article does not have any ELI5 sentence within it.
Not just me then? ;0)
"MLC posits that the Mandelbrot set isn’t just connected; it’s locally connected — no matter how much you zoom in on the Mandelbrot set, it will always look like one connected piece. For instance, a circle is locally connected. An extremely fine-toothed comb, on the other hand, is not. Though the entire shape is connected, if you skip over the shaft and instead zoom in on the tips of some of its teeth, you’ll just see a bunch of separate line segments."
Strictly speaking, you would do this coloring with all connected components of the intersection of your rectangle and M. (And the rectangle could be any region.)
The example messes this up, although it is a correct example, the square on the diagram showing the local piece containing non-connected parts is wrong. The comb has more and more teeth, infinitely many in a bounded space, on the left side. Only a rectangle which includes the left edge properly shows why the set isn't locally connected. The rectangle pictured includes finitely many teeth which have a separation between them. A rectangle overlapping the left edge of the comb would include separate components which get arbitrarily close to that left edge and so can't be separated by a border.
-take any rectanglular section of the complex plain that includes part or all of the Mandelbrot set
-draw the Mandelbrot set in black
-pick an arbitary black point and colour it red
-recursively colour every black point touching a red point (flood fill)
Then every black point would be recoloured red. And this would work with a pixel based image of the mandelbrot if the image had a high enough resolution. Is that right?
Do those first four steps. You wouldn't (necessarily) cover every black point in your rectangle. Choose a remaining black point and flood fill from that, say green. Keep on doing this with different colours until you've covered every black point in your rectangle. You have a bunch of regions of different colours.
Now, if the different coloured regions are all nicely separate, then your set is locally connected. Because each point is either cleanly in one component or cleanly in the other.
If on the other hand your drawing looks like https://commons.m.wikimedia.org/wiki/File:Julia_set_for_the_... with mixed up boundaries where some points are infinitesimally close to more than one colour, then it's not locally connected.
The difficulty with intuition is that in our intuition, coloured regions always have reasonable boundaries (think countries in a map: the border can be wiggly but there's never infinitely many tiny bits of one country mixed up in the boundary of two others). In fractal geometry, things like the Newton fractal picture above are quite usual.
Sadly, I can't run MMCE to test, since I'm using a PC and gfx card from 2009.
There's a whole world of this as well as iterating different functions.
https://en.wikipedia.org/wiki/Renormalization
Rampant conjecture/speculation: In the future, perhaps some Mathematician might discover a link between Renormalization Theory -- and the Digits Of Pi... since they seem related...
More specifically, between Renormalization Theory -- and algorithms for the Digits of Pi.
Of which, one notable one is The Chudnovsky algorithm:
https://en.wikipedia.org/wiki/Chudnovsky_algorithm
Which leads to Binary Splitting:
https://en.wikipedia.org/wiki/Binary_splitting
Which leads to Hypergeometric Series:
https://en.wikipedia.org/wiki/Hypergeometric_function#The_hy...
Which leads to Gauss' continued fraction:
https://wikimedia.org/api/rest_v1/media/math/render/svg/4d54...
https://en.wikipedia.org/wiki/Hypergeometric_function#:~:tex...
https://en.wikipedia.org/wiki/Gauss%27s_continued_fraction
Which leads to Analytic continuation of 3F2, 4F3 and higher functions:
https://fredrikj.net/blog/2009/12/analytic-continuation-of-3...
Which leads to my brain hurting ("Put down that Math book and step away from the Math!" <g>) -- because I can't handle all of this Math for now! :-) <g> :-)
But there is this very cool picture there:
https://3.bp.blogspot.com/_rh0QblLk0C0/SzEG9q5FxaI/AAAAAAAAA...
Yet something I learned recently blew my mind. It's about the uncanny resemblance between the images generated by the Mandelbrot set, and among all things, the popular image of Buddha.
For example: https://en.wikipedia.org/wiki/Buddhabrot
Even when looking at the 2D Mandelbrot set renderings, I can't help but wonder whether the similarity of the "bulbs" to the rather unique Buddha "hairstyle" (of allegedly funny lumps of hair) was just a coincidence.
Also, the tower-like makuṭa headdress in some Buddhist traditions look exactly like the thin threads that connect the bulbs at around (-2, 0).
I'm not saying they mean anything, but just something uncanny, and once I learned about the resemblance, it's hard to unsee it...
https://abcnews.go.com/Technology/WhosCounting/story?id=9861...
Honest question: what else do you think it could be, if not a coincidence?
I mean, I don't think this is likely, but that's the best I got.
I hear the brain likes to go into geometry mode when hallucinogens are ingested, and I suppose the brain is theoretically powerful enough to compute the Mandelbrot sets...
The prominent modes and paths of this 2D probability distribution also show some resemblance to the kabbalistic tree of life, which is its own, but fairly related topic of study. DMT use within a connected strand of this "inner science" has been suspected.
Drawing more of these far-fetching connections: The complex plane is related to several areas of physics, which might somehow find expression in electromagnetic brain dynamics.
In any case, the buddhabrot distribution seems quite understudied both from a scientific / mathematical PoV, and from the perspectives of the occluded study of the "inner realms".
I can't rule this hypothesis out entirely, but I'm very skeptical. Pareidolia seems more likely to me.
I found these sorts of things really helpful in getting my kids interested in fractals. They love the idea that there are "things" they can find that are only viewable through math.
But that's not what "fractal" means; it means "fractional dimension". To say the word "fractal" evoked something is subjective - evoked it for whom?
Compare to the etymology section here
Fractal art of all kinds was part of a certain trend in 80s/90s culture, which also influenced the look of the early Internet. And electronic dance music, clubs, and raves.
It was maximalist, psychedelic, colourful, busy, inclusive, recursive, and complex.
Whatever the math was doing, it was a very popular signifier of certain kinds of experience.
I suspect it's not a surprise that if faded into the background when the Internet began to commercialise and blandify in the later 90s.