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In Python, the NZMATH [23] library has the strong pseudoprime and Lucas tests, but does not have a combined function. The SymPy [24] library does implement this. As of 6.2.0, GNU Multiple Precision Arithmetic Library's mpz_probab_prime_p function uses a strong Lucas test and a Miller–Rabin test; previous versions did not make use of Baillie ...
SymPy is an open-source Python library for symbolic computation. It provides computer algebra capabilities either as a standalone application, as a library to other applications, or live on the web as SymPy Live [2] or SymPy Gamma. [3] SymPy is simple to install and to inspect because it is written entirely in Python with few dependencies.
A primality test is an algorithm for determining whether an input number is prime.Among other fields of mathematics, it is used for cryptography.Unlike integer factorization, primality tests do not generally give prime factors, only stating whether the input number is prime or not.
The isPrime function was inaccurate, as range doesn't include the higher end, so e.g. if checking for primality of 9, it would try numbers from 2 to 2, and conclude it was prime. I've added 1 to the upper end of the range so that the isPrime function works, in case anyone else comes along and tries to use it.
The Miller–Rabin primality test or Rabin–Miller primality test is a probabilistic primality test: an algorithm which determines whether a given number is likely to be prime, similar to the Fermat primality test and the Solovay–Strassen primality test.
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A prime sieve or prime number sieve is a fast type of algorithm for finding primes. There are many prime sieves. The simple sieve of Eratosthenes (250s BCE), the sieve of Sundaram (1934), the still faster but more complicated sieve of Atkin [1] (2003), sieve of Pritchard (1979), and various wheel sieves [2] are most common.
Of great interest in number theory is the growth rate of the prime-counting function. [3] [4] It was conjectured in the end of the 18th century by Gauss and by Legendre to be approximately where log is the natural logarithm, in the sense that / =