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 141 Margus Sõmer 9 49.2099 142 James Schumacher 6 49.1926 143 Paul Underwood 22.4999 49.1463 144 Walter Darimont 2 49.1458 145 Marshall Bishop 9 49.1443 146 Darren Li 5 49.1393 147 Keith Reinhardt 13 49.1384 148 Todd Pickering 4 49.1319 149 Randy Ready 8 49.1258 150 Daniel Thonon 14 49.1188 151 Eudy Silva 6 49.1029 152 Liam McGonegal 6 49.1026 153 Dao Heng Liu 10 49.0798 154 Steven Wong 13 49.0722 155 Lorin Arnold 4 49.0704 156 Grzegorz Granowski 32 49.0649 157 Takeshi Nakamura 11 49.0236 158 Randy Eldredge 4 49.0085 159 Denis Iakovlev 1 48.9974 160 Roman Krompolc 10 48.9868

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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)).