Naturally, for bool isprime(number) there would be a data structure I could query.
I define the best algorithm, to be the algorithm that produces a data structure with lowest memory consumption for the range (1, N], where N is a constant.
Just an example of what I am looking for: I could represent every odd number with one bit e.g. for the given range of numbers (1, 10], starts at 3: 1110
The following dictionary can be squeezed more, right? I could eliminate multiples of five with some work, but numbers that end with 1, 3, 7 or 9 must be there in the array of bits.
How do I solve the problem?
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The fastest algorithm for general prime testing is AKS. The Wikipedia article describes it at lengths and links to the original paper.
If you want to find big numbers, look into primes that have special forms like Mersenne primes.
The algorithm I usually implement (easy to understand and code) is as follows (in Python):
def isprime(n):
"""Returns True if n is prime."""
if n == 2:
return True
if n == 3:
return True
if n % 2 == 0:
return False
if n % 3 == 0:
return False
i = 5
w = 2
while i * i <= n:
if n % i == 0:
return False
i += w
w = 6 - w
return True
It's a variant of the classic O(sqrt(N)) algorithm. It uses the fact that a prime (except 2 and 3) is of form 6k - 1 or 6k + 1 and looks only at divisors of this form.
Sometimes, If I really want speed and the range is limited, I implement a pseudo-prime test based on Fermat's little theorem. If I really want more speed (i.e. avoid O(sqrt(N)) algorithm altogether), I precompute the false positives (see Carmichael numbers) and do a binary search. This is by far the fastest test I've ever implemented, the only drawback is that the range is limited.