def get_solution():
""" Solves the problem and returns the answer.
NOTE: At the time of writing, JAX does not enable 64 bit operations by default, required to
solve this problem. Check the configuration at the top of this script.
"""
max_prime = np.sqrt(NUMBER).astype(int) # No point looking after this one
primes_list = jnp.asarray(get_all_primes(max_prime=max_prime),
dtype=jnp.int32)
return compute_solution(NUMBER, primes_list)
def get_solution():
""" Solves the problem and returns the answer.
"""
primes_list = get_all_primes(max_prime=MAX_PRIME)
triangle_sequence = np.asarray(triangle_up_to(value=MAX_INT), dtype=int)
n_combinations = compute_solution(
primes_list=jnp.asarray(primes_list, dtype=jnp.int32),
triangle_sequence=jnp.asarray(triangle_sequence,
dtype=jnp.int32)).astype(int)
# Easier to do masks outside jax :D
return min(triangle_sequence[n_combinations > 500])
def get_solution():
""" Solves the problem and returns the answer.
"""
return np.sum(get_all_primes(max_prime=2000000))
def get_solution():
""" Solves the problem and returns the answer.
"""
primes_list = jnp.asarray(get_all_primes(max_prime=MAX_NUMBER),
dtype=jnp.int32)
return compute_solution(MAX_NUMBER, primes_list)
def get_solution():
""" Solves the problem and returns the answer.
"""
return get_all_primes(n_primes=10001)[-1]