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 961 Adrian Reber 1 43.8386 962 David Abrahmi 1 43.7287 963 Joachim Sander 1 43.7287 964 Bruce Dodson 1 43.6220 965 Takahiro Nohara 0.3333 43.6148 965 Satoshi Noda 0.3333 43.6148 967 David Slowinski 1 43.2279 967 Paul Gage 1 43.2279 969 Darren Bedwell 1 43.0827 970 Zsuzsi Mate 0.3333 43.0267 971 Boris Iskra 1 42.9211 972 Michael Richard Eaton 1 42.8233 973 Robert Boniecki 1 42.6428 974 Tom Wu 22 42.0783 975 Manfred Toplic 1 41.7008 976 Masakatu Morii 3 40.7006 977 Nuutti Kuosa 1.3333 40.2057 978 Darren Smith 2 40.1303 979 Andreas Enge 4.5 40.1249 980 Stephan Vink 3.3334 39.9249

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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)).