Top person sorted by score

At this site we keep several lists of primes, most notably the list of the 5,000 largest known primes. Who found the most of these record primes? We keep separate counts for persons, projects and programs. To see these lists click on 'number' to the right.

Clearly one 100,000,000 digit prime is much harder to discover than quite a few 100,000 digit primes. Based on the usual estimates we score the top persons, provers and projects by adding ‎(log n)³ log log n‎ for each of their primes n. Click on 'score' to see these lists.

Finally, to make sense of the score values, we normalize them by dividing by the current score of the 5000th prime. See these by clicking on 'normalized score' in the table on the right.

rank person primes score 221 Mickaël Carlier 3 48.9244 222 Peter Hundhausen 2 48.9123 223 Mark Rodenkirch 8.3333 48.9110 224 Nayan Hajratwala 1 48.9067 225 Brendon Gory 5 48.9028 226 Mike Thümmler 2 48.8993 227 Chris Howell 1 48.8827 228 KaiMing Poon 3 48.8760 229 Kyle J Johnson 3 48.8629 230 Wolfram Glatte 7 48.8524 231 Jiri Jaros 1 48.8448 232 Brook Harste 2 48.8333 233 Km Kim 1 48.8277 234 Dr. sc. nat. Bernhard Pabsch 1 48.8146 235 Johnathan Chu 7 48.8106 236 David Metcalfe 15 48.8069 237 Prescott Thompson 7 48.8045 238 Martin Neujahr 1 48.8035 239 Pavel Rosický 1 48.7976 240 Jacques Molné 3 48.7940

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Notes:

Score for Primes

To find the score for a person, program or project's primes, we give each prime n the score (log n)³ log log n; and then find the sum of the scores of their primes. For persons (and for projects), if three go together to find the prime, each gets one-third of the score. Finally we take the log of the resulting sum to narrow the range of the resulting scores. (Throughout this page log is the natural logarithm.)

How did we settle on (log n)³ log log n? For most of the primes on the list the primality testing algorithms take roughly O(log(n)) steps where the steps each take a set number of multiplications. FFT multiplications take about

O( log n ^. log log n ^. log log log n )

operations. However, for practical purposes the O(log log log n) is a constant for this range number (it is the precision of numbers used during the FFT, 64 bits suffices for numbers under about 2,000,000 digits).

Next, by the prime number theorem, the number of integers we must test before finding a prime the size of n is O(log n) (only the constant is effected by prescreening using trial division).  So to get a rough estimate of the amount of time to find a prime the size of n, we just multiply these together and we get

O( (log n)³ log log n ).

Finally, for convenience when we add these scores, we take the log of the result.  This is because log n is roughly 2.3 times the number of digits in the prime n, so (log n)³ is quite large for many of the primes on the list. (The number of decimal digits in n is floor((log n)/(log 10)+1)).