Closed form — where it appears
Named by 11 essays across 6 fields — each of them below, with the objects they name alongside it.
A limit is not a prediction
Measured from n = 64 to n = 4,096, this site's hybrid merge sort fits a linear class better than n log n. Measured out to n = 65,536, the ranking reverses. Nothing changed but the range — and this is not a flaw in the method, it is the method finding the exact place where measurement stops being able to help.
The probe formula nobody checks
The expected number of probes to insert into a hash table under linear probing is ½(1 + 1/(1−α)²). It is quoted constantly, it is correct, and applied to a table of 256 slots at 95% load it overstates the measured cost by nearly half — because it is an asymptotic result and a real table is not asymptotic.
A filter that is allowed to be wrong
A Bloom filter holding four thousand keys in five thousand bytes answers membership in four memory probes and gets 1.14% of its negative answers wrong. It never gets a positive one wrong. That asymmetry is the whole design, and the rate it makes errors at is a third quantity beside the operation count and the space.
A hash is a family, not a function
Two thousand and forty-eight keys into two hundred and fifty-six buckets. Under a hash that takes the low bits of the key, all 2,048 land in bucket zero and 255 buckets are empty. Under a multiplier drawn at random, the worst bucket holds 11. The keys are the same keys, and they are the multiples of the table size.
The correction that makes it work
HyperLogLog and LogLog read the same registers and differ only in how they average them. The harmonic mean is worth 30% of the error for nothing, and below two and a half registers' worth of keys the estimator both are built on is 137% high and has to be abandoned.
The formula everybody sizes filters with
Fill a Bloom filter with four thousand random keys and its measured false-positive rate is within 4% of the textbook formula. Fill the same filter with the integers 1 to 4,000 and the rate is 30% worse than the formula says — not because the hash is bad, but because it is too good on that input.
A match decided by a number
Rabin–Karp replaces a comparison of two strings with a comparison of two integers, and pays for it only when the integers agree by accident. On 65,521 windows at a modulus of 1,009 it verified 79 times against a predicted 64.9. On a text of 1,024 characters chosen with the modulus in hand it verified at 64 windows out of 64 possible, each one costing fifteen of the pattern's sixteen characters — and the closed form is silent about that, because it assumes nobody chose the text.
What a window costs in bits
The approximate structure grows like the square of a logarithm and the exact one grows like the window, so the approximation wins eventually. Eventually is a window of 6,000 at a 2% tolerance — and below it the summary is larger than the thing it is summarising.
A distribution computed rather than sampled
Quicksort with a random pivot makes a random number of comparisons, and every plate on this site reports one draw from that distribution. The distribution itself can be computed exactly — every pivot, every split, weighed — and on sixty-four elements its mean is 360.706 and its standard deviation 36.741. A hundred real runs average 365.98; two thousand average 360.74. The exact answer says how many runs a sample needs, and it is fewer as the input grows.
The filter that proposes everything
Seed-and-extend saves two thousand times the work at zero errors and costs more than doing nothing at four. Between them the selectivity falls through the floor, and where it falls is set by two numbers that can be computed before the filter is run — one of which does not contain the length of the text at all.
Two hash values and the keys they copy
A Bloom filter that makes its k bit positions from two hash values, as h₁ + i·h₂, answers yes to 1.6% of absent keys on a 64-bit filter where k independent hashes answer 0.69%. The penalty is not the one expected. With the step forced odd no key ever repeats a bit, while a quarter of independent keys do. What costs the filter is a query whose start and step reproduce a stored key's whole progression, which happens with probability 4n/m², measured to within a few per cent from 64 bits to 4,096. The penalty fades as the filter grows and returns as the hash count rises — 1.13 times at seven positions on 1,024 bits, 5.35 times at thirteen.
Named alongside it
The objects these essays reach for when they reach for this one.
Load factorBloom filterHash functionFalse-positive rateFinite size effectHash tableRegimeRelative errorThresholdAdversarial inputDistributionHash family