Lempel ziv — where it appears
Named by 5 essays across 4 fields — each of them below, with the objects they name alongside it.
The entropy that cannot see a copy
Two copies of a text have exactly the same symbol statistics as one, so every entropy on this site doubles when the second copy arrives and the second copy carries no information at all. The number of runs in the Burrows-Wheeler transform is 224 at two copies and 224 at thirty-two.
The number that would choose a cap
A depth histogram is one linear pass — 3.16 operations a character over thirty-two thousand of them — and it says the whole text sits at a mean depth of 3.98 with a worst of ten. Nobody prints it, and every choice of cap in this collection was made without it.
The cap an automaton cannot see
A state of a suffix automaton stands for a set of occurrences and hands back one of them. So a capped parse driven by it can ask whether the earliest occurrence is shallow enough and cannot ask whether any occurrence is — which costs up to 9.8% of the phrases, and only at the caps that bind.
The parse in one pass of the text
The same parse, phrase for phrase, from 1,324,336 character comparisons or from 25,420 transitions and suffix-link steps. One of those numbers grows with the text and the other grows with its square, and the difference is why every measurement about a depth cap here was taken on a few thousand characters.
What a quadratic construction was setting
A depth cap of one costs 90% of an 8,192-character text in phrases, and 86% of a 65,536-character one. The ladder's conclusions hold at thirty-two times the size — and the number the ladder could not reach, the deepest chain a real collection produces, turns out to be fourteen.
Named alongside it
The objects these essays reach for when they reach for this one.
MeasurementPhraseRepetitionSuffix automatonTrade offConstructionDepth capParseDefinitionExtractionAlphabetAmortised