5 Best Ways to Check if n is a Strong Prime in Python

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πŸ’‘ Problem Formulation: This article addresses the challenge of determining whether a given number n is a strong prime. A strong prime is a prime that is greater than the arithmetic mean of the nearest prime numbers above and below it. For example, for input n = 17, the expected output is True since it is stronger than the primes 13 and 19 that surround it.

Method 1: Brute Force Prime Checking

This brute force method checks if the number n is a strong prime by first ensuring it is a prime number and then comparing it with its neighboring primes. It requires determining the closest primes above and below n to make the comparison.

Here’s an example:

def is_prime(num):
    if num < 2: return False
    for i in range(2, int(num**0.5) + 1):
        if num % i == 0: return False
    return True

def closest_prime_below(n):
    prime = n - 1
    while not is_prime(prime):
        prime -= 1
    return prime

def closest_prime_above(n):
    prime = n + 1
    while not is_prime(prime):
        prime += 1
    return prime

def is_strong_prime(n):
    if not is_prime(n):
        return False
    lower = closest_prime_below(n)
    upper = closest_prime_above(n)
    return n > (lower + upper) / 2

# Test with n = 17
print(is_strong_prime(17))

Output:

True

This code snippet defines a function is_strong_prime() that uses helper functions to check if n is a prime and to find the closest primes surrounding it. If n is greater than the arithmetic mean of those primes, it is deemed a strong prime.

Method 2: Optimized Prime Checking with Sieve of Eratosthenes

The Sieve of Eratosthenes is a highly efficient algorithm for finding all primes up to a certain limit. It can be employed to find the closest primes around n, facilitating the strong prime check.

Here’s an example:

def sieve_of_eratosthenes(limit):
    sieve = [True] * (limit + 1)
    sieve[0] = sieve[1] = False
    for i in range(2, int(limit**0.5) + 1):
        if sieve[i]:
            for j in range(i*i, limit + 1, i):
                sieve[j] = False
    return sieve

def get_neighboring_primes(sieve, n):
    lower, upper = n - 1, n + 1
    while not sieve[lower]: lower -= 1
    while not sieve[upper]: upper += 1
    return lower, upper

def is_strong_prime_optimized(n):
    sieve = sieve_of_eratosthenes(n + 20)  # +20 as an arbitrary buffer for the next prime
    if not sieve[n]:
        return False
    lower, upper = get_neighboring_primes(sieve, n)
    return n > (lower + upper) / 2

# Test with n = 17
print(is_strong_prime_optimized(17))

Output:

True

This code snippet employs the Sieve of Eratosthenes to create a sieve up to n + 20 then uses it to find the closest primes around n quickly. If n is greater than the arithmetic mean of these primes, it’s a strong prime.

Method 3: Utilizing Pre-computed Prime Lists

For repeated strong prime checks, using a pre-computed list of prime numbers can save time. The list can be loaded from an external source or generated in advance using methods like the Sieve of Eratosthenes.

Here’s an example:

precomputed_primes = [2, 3, 5, 7, 11, 13, 17, 19]  # This should be a sufficiently long list

def is_strong_prime_precomputed(n):
    if n not in precomputed_primes:
        return False
    idx = precomputed_primes.index(n)
    lower, upper = precomputed_primes[idx - 1], precomputed_primes[idx + 1]
    return n > (lower + upper) / 2

# Test with n = 17
print(is_strong_prime_precomputed(17))

Output:

True

In this code snippet, the function is_strong_prime_precomputed() makes use of a precomputed list of primes to find the nearest primes around n. It checks if n is greater than the average of its neighbors to determine if it’s a strong prime.

Method 4: Efficient Search with Binary Search

Binary search can be applied to a sorted list of prime numbers to quickly locate the nearest primes around n. This method assumes the availability of a sorted list of prime numbers up to a sufficient limit.

Here’s an example:

import bisect

precomputed_primes = [2, 3, 5, 7, 11, 13, 17, 19]  # This should be a sorted list of primes

def is_strong_prime_binary_search(n):
    idx = bisect.bisect_left(precomputed_primes, n)
    if idx == len(precomputed_primes) or precomputed_primes[idx] != n:
        return False
    lower, upper = precomputed_primes[idx - 1], precomputed_primes[idx + 1]
    return n > (lower + upper) / 2

# Test with n = 17
print(is_strong_prime_binary_search(17))

Output:

True

The function is_strong_prime_binary_search() here uses the bisect module to perform a binary search on the precomputed list of primes. If n is not found or is not greater than the mean of its adjacent primes, it is not a strong prime.

Bonus One-Liner Method 5: Using List Comprehensions and Functions

A Python one-liner employing list comprehensions and built-in functions can be a clever way to solve the problem, provided the scale of primes is manageable.

Here’s an example:

precomputed_primes = [2, 3, 5, 7, 11, 13, 17, 19]  # This should be a comprehensive list

is_strong_prime_one_liner = lambda n: True if n in precomputed_primes and \
    n > sum([precomputed_primes[i] for i in (precomputed_primes.index(n)-1, precomputed_primes.index(n)+1)]) / 2 else False

# Test with n = 17
print(is_strong_prime_one_liner(17))

Output:

True

The one-liner is_strong_prime_one_liner checks if n is in a precomputed list of primes and if it’s greater than the average of the primes immediately before and after its index using list comprehensions

Summary/Discussion

  • Method 1: Brute Force Prime Checking. Strengths: Simple implementation, no need for precomputation. Weaknesses: Inefficient for large numbers and repetitive checks.
  • Method 2: Optimized Prime Checking with Sieve of Eratosthenes. Strengths: Efficient for large ranges of numbers. Weaknesses: Requires more memory and computation upfront.
  • Method 3: Utilizing Pre-computed Prime Lists. Strengths: Very fast for repeated checks. Weaknesses: Limited by the range of precomputed primes.
  • Method 4: Efficient Search with Binary Search. Strengths: Fast search time, good for large precomputed lists. Weaknesses: Dependent on the precomputed primes being sorted.
  • Method 5: One-Liner Using List Comprehensions. Strengths: Compact and easy to understand. Weaknesses: Not practical for very large lists or without a comprehensive list of primes.