However, if the list is sorted (in ascending order, say) and the tree is not one of the self-balancing varieties, insertion is , because the “tree” created by the successive insertions of sorted key is in fact a degenerate tree, a list. So, what if the list is already sorted and don’t really want to have a self-balancing tree? Well, it turns out that you can build a(n almost perfectly) balanced tree in .
Some time ago, I presented a piece on compressing voxel worlds and I just realized that I discussed different types of variable length codes quite a few times, but that I never took the time to present you the basic method of Huffman coding!
The problem of finding an optimal variable length code is to find an uniquely decodable binary code (that is, a code using only 0 and 1 for which there exists only one way of decoding a message) that encodes each symbol in a number of bits that is proportional to its probability of occurrence. A first strategy was devised by Robert Fano, Huffman’s teacher at one point. The so-called Shannon-Fano code is a top-down approach to solving the problem but it was shown to be suboptimal. The code proposed by David Huffman solves the problem optimally by taking exactly the opposite approach, that is, building the code bottom-up.
A friend of mine, Arthur, is developing a voxel-based game and found himself having to deal with large volumes of data. Unlike 2½D games where the map is essentially a 2D expanse with occasional relief, his game allows the definition of things in full (discrete) 3D.
To avoid loading the entire map in memory, he made the wise design decision of making his world tile-based, so that it can, simultaneously, reduce the working set as well as having an essentially open map, by loading only a small set of visible world blocks at all time. So together we took a look at compressing these world blocks, and it turned out that we can do a lot with fairly simple algorithms and VLCs.