如何在固定时间内求解相依线性方程组？

import numpy as np

def gaussian_elimination(A, b):
    n = len(A)
    for i in range(n):
        max_row = np.argmax(np.abs(A[i:, i])) + i
        A[[i, max_row]] = A[[max_row, i]]
        b[[i, max_row]] = b[[max_row, i]]
        for j in range(i + 1, n):
            factor = A[j, i] / A[i, i]
            A[j, i:] -= factor * A[i, i:]
            b[j] -= factor * b[i]
    x = np.zeros(n)
    for i in range(n - 1, -1, -1):
        x[i] = (b[i] - np.dot(A[i, i + 1:], x[i + 1:])) / A[i, i]
    return x

# 示例
A = np.array([[3, 2, -4], [2, 3, 3], [5, -3, 1]])
b = np.array([3, 15, 14])
x = gaussian_elimination(A, b)
print("解向量 x:", x)

import scipy.linalg as la

def lu_decomposition(A, b):
    P, L, U = la.lu(A)
    y = la.solve(L, P @ b)
    x = la.solve(U, y)
    return x

# 示例
A = np.array([[3, 2, -4], [2, 3, 3], [5, -3, 1]])
b = np.array([3, 15, 14])
x = lu_decomposition(A, b)
print("解向量 x:", x)

import multiprocessing as mp

def solve_parallel(A, b):
    n = len(A)
    pool = mp.Pool(mp.cpu_count())
    results = [pool.apply_async(la.solve, args=(A, b)) for _ in range(n)]
    x = [result.get() for result in results]
    pool.close()
    pool.join()
    return x

# 示例
A = np.array([[3, 2, -4], [2, 3, 3], [5, -3, 1]])
b = np.array([3, 15, 14])
x = solve_parallel(A, b)
print("解向量 x:", x)