Algorithms & Data Structures
the bound is a claim, and it is checkable
Concept

Move to front — where it appears

A recoding that replaces each symbol by its position in a list of recently seen symbols and then moves it to the front. Runs of one symbol become runs of zeros, so clustering a coder could not use becomes skew it can — and on a stream with no locality it charges for the assumption instead.

Named by 5 essays across 2 fields — each of them below, with the objects they name alongside it.

each row is one rotation · the table is sorted · the transform is the last columnfirstlast◇the-order-is-the-message-is-the-message◇the-order-message◇the-order-is-the-order-is-the-message◇the-the-message◇the-order-isage◇the-order-is-the-messder-is-the-message◇the-ore◇the-order-is-the-message-message◇the-order-is-the-order-is-the-message◇ther-is-the-message◇the-ordessage◇the-order-is-the-mge◇the-order-is-the-messahe-message◇the-order-is-t… 11 further rotationsmeasured on 8,192 symbols of the same source:H₀ of the text3.899 bitsH₀ of the last column3.899 bitsH₀ after move-to-front, before4.156 bitsH₀ after move-to-front, after1.802 bitsmodel: order 0, before and after a permutationmean run 1.01 → 3.38

The transform that emits nothing

The Burrows–Wheeler transform outputs exactly the characters it was given, in a different order. Its zeroth-order entropy is therefore identical to its input's, to fifteen decimal places, and by that measure it has done nothing at all. A Huffman coder handed the result spends 1.935 bits per symbol where the same coder on the same data spends 4.209, and the difference is entirely in the order.

text · Bits thousands of bits · the floor is 31.9kthe text as it is34.0k streamto undo itnothinga shuffle agreed in advance34.4k streamto undo itnothingthe characters sorted0.3k streamto undo it31.8kthe Burrows–Wheeler transform14.8k streamto undo itnothingmodel: order 0 after move-to-front · Words from a fixed vocabularydashed: coding the text as it stands

What a reordering costs to undo

Sorting the characters of a text clusters them perfectly: a move-to-front pass then leaves 289 bits where the text's own floor is 31,931. Naming which arrangement of those characters the text was costs 31,827 bits, and the two numbers add to the floor it started from. The Burrows–Wheeler transform clusters less and costs nothing to undo, which is the only reason it is the one that is used.

floors · Bits 01234characters of context the sort reads, kbits a symbol after move-to-front012481632the full transform, 1.802ties kept in text orderties sorted by symbol: the stream alonethe text's own floormodel: order 0 after move-to-front · Words from a fixed vocabularyexactly the transform from k = 24

The order inside a tie

Sort the rotations of a text by their first four characters rather than by everything that follows, and the output clusters slightly better than the full Burrows–Wheeler transform: 1.780 bits a symbol against 1.802, from 63% of the character reads. It also costs nothing to undo. The prediction that a shorter context leaves ties for the inverse to pay for was wrong. The cost to undo comes from how a tie is ordered, not from how long the context is, and at k = 0 the wrong tie rule is exactly the sort.

floors · Bits words drawn independentlymean +0.0038 ± 0.011ahead on 10 of 16next word set by the last lettermean +0.0064 ± 0.011ahead on 12 of 16no difference−0.0176+0.0000+0.0251bits a symbol the short context savesthe stream reported beforek = 4 · 16 streams a source · bands: ±1 standard deviationone draw, not a result

A result the size of its own noise

Sorting rotations by their first four characters beat the full Burrows–Wheeler transform by 0.022 bits a symbol. On sixteen streams of the same source the mean difference is 0.004 with a standard deviation of 0.011, and the short context is ahead on ten of them. The 1.2% was one draw. What survives is sharper than the result it replaces: from four characters upwards, every run break either rule makes is at a word boundary, and nothing above four characters decides anything at all.

floors · Bits 123456810121624context length k, characterswords drawn independentlynext word set by the last letterwords from the whole vocabularythe corpus of essaysthe corpus of technical writingthe sweep flattensopen ambiguity under 1%all ambiguity under 5%eight streams a sourcethresholds: 0.01 bits, 1%, 5%

Where a context stops naming the letter before

A sort of rotations by their first k characters matters only where those k characters fail to decide the character before them, and that share of positions can be counted in one pass with no sorting. It was proposed as a replacement for sweeping k. Counted as proposed, it misses by a factor of four: on a stream of independent words 37% of positions are still ambiguous at four characters, where the sweep has already gone flat. Counted only where the context sits inside a word that goes on past it — the one place more context can still help — it lands within a step of the sweep on four sources of five.

floors · Bits

Named alongside it

The objects these essays reach for when they reach for this one.

Burrows-wheeler transformEntropyBits per symbolContext modelInvertibilityMeasurement designPermutationHonest limitModel orderSide informationCounting argumentPrediction

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