Yesterday, I finally made progress on the pivot detection algorithm. I did the brute-force, memory-intensive, and SLOW way of doing it, so it will need to be replaced, but at least I can start building pivot-chord graphs and trying my approximation algorithms. I'll have to time what activities are taking so long. It might also be that I am simply having issues with such increased volume that the algorithms can't keep up with the input size, let alone the horrible complexity. I've also rebuilt my 2D-slice viewer so I can see what these pivots look like with the random boxes I use.
Theoretically, I have two major problems. The first big one is that my chords are not always convex themselves for dimensions higher than 3. I don't know if the chords will automatically fix this or not. We'll have to see when I start to implement a few algorithms. If only 4 dimensions were easier to visualize. The worst-case scenario will be if I have to perform partitioning on all pivots of lower dimension recursively and find all possible partitions (possibly not minimal) of the pivots and the combinations thereof.
The second problem is my definition of bands. Bands are paths within the interior that are wrapped around some of the exterior and can't be pulled away without cutting the path. These normally do exactly what I expect them to do. However, I've discovered deceptive bands (bands that appear during partitioning) and pseudo-bands (bands that can be cut twice by the same chord). These both cause my formulas to change. Deceptive bands decrease the region count, but simultaneously increases the chord count (i think, has not yet been proven). Pseudo-bands are hard to detect and increase the number of regions by lowering the number of bands that actually cause problems. Hopefully I can find some redefinition of these things in order to solidify a counting mechanism. Of course, these issues have only been visualized in three dimensions, there may be even worse examples in more dimensions.