The Mandelbrot set is the set of complex numbers such that for the sequence doesn’t diverge to infinity. A starting value is not in if for some . When approximating this set using a computer, we give each Pixel a coordinate in the complex numbers and calculate the sequence up to some Iteration and look at all numbers such that we have .
Since it suffices to discard any points at iteration for which holds. Therefore the following definitions are justified:
where .
We know, that is compact and connected (Douady and Hubbard, or Topological proof by Jeremy Kahn), and simply connected. We here assume that is also compact, connected (which follows from the proof of Jeremy Kahn) and simply connected. For our (bad) Optimization on finding points inside the set we define
and . We note, that we get If for some , then . For a variable is a Polynomial and therefore holomorphic (and continuous).
Lemma 1.
Proof. We know and .
follows from being compact.
Now, to show that , we can construct a sequence of points such that as follows: We use the fact that since is a polynomial, so holomorphic it is continuous and therefore for every iteration and any point , for example 0 or 0.5 and , for example 4, we have some point such that . Now since we get, that , so and .
Lemma 2. Let , then
Proof. Let and , then , because else . Therefore , so holds trivially.
This also shows, that is well-defined and
Lemma 3.
Proof. Let and . So . And therefore . Since , so we get
Lemma 4. Let , then
Proof. Assume , then , which is a contradiction to .
Lemma 5. Let , then
Proof. Assume for some , we want to show which is a contradiction. By definition . So we know, that , so . So . Let , then , so , so , but which is a contradiction to the boundaries only intersecting in .
Lemma 6. Let , then
Proof. as . Assume , we want to show which is a contradiction. Then there is a such that and with and . We get, that , so and therefore , which is a contradiction to .
Lemma 7.
Proof. Since is strictly decreasing and at least , we know, that it must converge. Assume there is some such that , so for all . Then there exists some such that and for all , which is a contradiction.
Conjecture 8. Let , then
The goal of this project is to find upper and lower bounds for . Upper bounds are useful, because with them, we know, that any point that hasn’t escaped at iteration N is within at most distance from , and assuming infinite Resolution, we can take any point inside with a distance to of at least (or at least our upper bound) and know they are in the set , therefore leaving them out of any further computing. Of course we do not have infinite Resolution, but it still gives us a good approximation and optimization. (Which is worse then border tracing, as border tracing doesn’t check any points of apart from the border)
Conjecture 9. For it holds, that a more precice, though still rough upper bound would be:
While trying to prove the theorems, and optimizing the code, I encountered the following equivalent formulations/characterizations of . I use the next characterization in my code because it is easier to work with, though I couldn’t prove it yet.
Here is the direction that I could prove:
Proof. Assume the set is empty, then we get, that
, since is compact we would get which is a contradiction to :
Let , then , we show by constructing a sequence of elements in that converge to .
We know, that .
So let , then since , that , therefore , but which is a contradiction.
Let . To show: : Assume .
Then there exists some and some
(because ),
but , so which is a contradiction.
The direction we would still need to show is . or equivalently
To give some experimental “evidence” for Conjecture 8 using code: “Dist_local_maxima.py”. In the code, we look at the distance of any Point in to . (More accurately, we look at the distance to , where is the Box of our Image). We then take locally maximal Points in and and colour them accordingly. (for we colour them orange to red increasingly, for , we colour them blue to violet). Also visible in the following Images is the violetly coloured circle (that is maximal in ) around the mouse-cursor, here positioned on .
Looking at the images, we can see, that any point that connects two sections (so a point, such that removing it disconnects the set) is a local maximizer of the distance function. The “larger” the “shapes”, the greater its distance value. is the point that connects the largest two shapes, the main cardioid, and sphere around .
Now to give some experimental “evidence” for Conjecture 9 using code: “rN_List.py”. The algorithm without optimizations works like this: given the Iteration , we want to approximately calculate . We do a Binary search of the radius between Given, that the Conjecture 8 holds, and our characterization does too, we check if for a radius .
If it is, we try a bigger radius, if it is not, we try a smaller radius (using Binary Search, we half our step each time until it is smaller then our threshold ). Since we can only calculate finite Points on , we have a step_amount, and take step_amount many evenly spaced samples of .
In the following graphs, we can see, that , so therefore we know, that , so is an upper bound for , and that it goes towards .