Методы оптимизации

import matplotlib.pyplot as plt
import numpy as np

def F(x):
    return x*x-10*x+2


def PP(a, b, eps, x, y):
    #метод поразрядного поиска
    h = 0.001
    flag = False
    for i in np.arange(a, b, h):
        x.append(i)
        x.append(i + h)
        if F(x[-2]) < F(x[-1]):
            if h < eps and not flag:
                x_ans = x[-2]
                flag = True
            else:
                h /= -4

    y = [F(i) for i in x]
    plt.plot (x, y)
    plt.grid(True)
    plt.show()
    print("Метод поразрядного поиска: \nx* = ", x_ans, "\ny_min = ", F(x_ans))

def Dych(a, b, eps, x, y):
    #метод дихотомии
    delta = eps/10
    for i in np.arange(a, b, delta):
        x.append(i)
    y = [F(i) for i in x]
    h = (b-a)/2
    x_ans = a
    while h > eps:
        h = (b-a)/2
        x1 = (a+b-delta)/2
        x2 = (a+b+delta)/2
        if F(x1) <= F(x2):
            b = x2
        else:
            a = x1
        if h <= eps:
            x_ans = (a+b)/2
    print("\nМетод дихотомии: \nx* = ", x_ans, "\ny_min = ", F(x_ans), '\n')

def GoldenRatio(a, b, eps, x, y):
    #метод золотого сечения
    for i in np.arange(a, b, eps/10):
        x.append(i)
    y = [F(i) for i in x]
    h = (b-a)/2
    tau = (5**(1/2) - 1) / 2
    h = (b-a)/2
    while h > eps:
        x1 = a + (3 - 5**(1/2))/2 * (b - a)
        x2 = a + (5**(1/2) - 1)/2 * (b - a)
        if F(x1) <= F(x2):
            b = x2
            h *= tau
        else:
            a = x1
            h *= tau
        x_ans = (a+b)/2
    print("Метод золотого сечения: \nx* = ", x_ans, "\ny_min = ", F(x_ans))

def main():
    a = 3
    b = 6
    eps = 0.01
    x = []
    y = []
    PP(a, b, eps, x, y)
    Dych(a, b, eps, x, y)
    GoldenRatio(a, b, eps, x, y)

if __name__ == "__main__":
    main()