# Perturbation TheoryΒΆ

The idea of perturbation is to start with the orbit of a reference point, the so called *reference orbit*, and to express the orbits of all other points in form of deviations to it. More precisely, let

be the orbit of the reference point \(x \in \mathbb{C}\) and \(y\) be a point in the neighborhood of \(x\). Then we can calculate the orbit of \(y\) in the following form:

The sequence

is called the *delta orbit* of \(y\). Once the reference orbit \((1)\) has been calculated, it is sufficient to know the delta orbit \((\Delta_n)\) to fully reconstruct \((y_n)\).

Delta orbits can be computed with a simple iteration formula that can be derived directly from the iteration formula of the Mandelbrot set:

Equation \((2)\) shows that the deltas can be calculated with a sum whose summands are all very small. In fact, the numbers are so small that it is usually sufficient to represent them with low-precision data types. This outlines the core of the algorithm: Instead of slowly calculating all points in high precision, an expensive calculation of this kind is carried out for a single reference point, only. All other orbits are derived from their delta orbits which are calculated in low precision.

Unfortunately, the outlined approach does not always succeed. A general problem concerns the choice of the reference point \(x\). If the orbit of \(x\) escapes, i.e., if \(x\) does not belong to the Mandelbrot set itself, equation \((2)\) can only be applied to the elements of the non-escaping initial segment of \((x_n)\). As a result, \(x\) cannot serve as the reference for any other point \(y\) that either belongs to the Mandelbrot set itself, or diverges later than \(x\). Even worse, delta calculation can fail with non-volatile reference points, too. In some cases, rounding errors lead to visible pixel errors in the generated image. Such pixels are referred to as *glitches*. If a pixel is a glitch, there is an increased probability that this is also true for its neighbors. As a result, affected image often contain areas that look extremely noisy or areas that have no detail at all, thus looking like ill-placed color blobs.

Fortunately, both problems can be solved by recomputing the affected pixels with a different reference point. Modern Mandelbrot generators thus calculate images in multiple rounds. In each round a new reference point is selected and applied to all pixels for which it turns out to be appropriate. If a glitch a detected, the pixel is set aside and revisited in the next round. This procedure is repeated until the proportion of glitch pixels drops below a predefined acceptance level.

Besides choosing a suitable reference point, we face a challenging problem. How can we decide for a point whether the currently selected reference point results in a glitch? An answer to this question is provided by the *Pauldelbrot criterion*, which was formulated in a forum post in 2014. The criterion is based on the hypothesis that pixel errors only occur if the magnitude of \(x_n + \Delta_n\) is way smaller than the magnitude of \(x_n\). Symbolically, this is is denoted by:

To apply the criterion in practice, a constant \(\tau\) is chosen and a test added to the delta orbit calculation, which checks the following condition after each iteration:

Once the condition hits, calculation is aborted. The affected point is put aside and revisited in the next round. Although the criterion is based on a hypothesis that is still lacking a rigid proof (as far as I know), it has turned out to work extremely well in practice. As of today, the Pauldelbrot criterion is the preferred method for glitch detection in modern Mandelbrot generators.