ЛР / ЛР2 / PMO_LR2

import numpy as np
import math as m
from matplotlib import pyplot as plt


def f(x):
    return -x**2- 10*x + 5


eps = 2 ** (-15)
a = -6.6
b = -4
eps0 = [[], [], [], []]
res = []

# Алгоритм равномерного поиска
r = 1
a1 = a
b1 = b
n = 50
iterations = [0] * 4

while abs(b1 - a1) > eps:
    x = np.arange(a1, b1, (b1 - a1) / n)
    vals = [f(i) for i in x[0:-1]]
    ch = vals.index(min(vals))
    a1, b1 = x[ch - 1], x[ch + 1]
    iterations[0] += 1
    eps0[0].append(b1 - a1)
res.append([a1, b1, iterations[0]])

print(res)

# Алгоритм дихтомии(алгоритм деления пополам)
a1, b1 = a, b
while abs(b1 - a1) > eps:
    n = (a1 + b1) / 2
    x = [(a1 + n) / 2, (b1 + n) / 2]
    vals = [f(i) for i in x]
    if vals[0] < vals[1]:
        b1 = n
    else:
        a1 = n
    iterations[1] += 1
    eps0[1].append(b1 - a1)
res.append([a1, b1, iterations[1]])
# print(eps0)
# print(res)

# Метод золотого сечения
a1, b1 = a, b
while abs(b1 - a1) > eps:
    x = [b1 - (b1 - a1) * 0.618, a1 + (b1 - a1) * 0.618]
    vals = [f(i) for i in x]
    if vals[0] < vals[1]:
        b1 = x[1]
    else:
        a1 = x[0]
    iterations[2] += 1
    eps0[2].append(b1 - a1)
res.append([a1, b1, iterations[2]])

# Метод Фибоначчи
iterations[3] = 1
a1, b1 = a, b
fib = [1, 1]
while (b1 - a1) / fib[-1] > eps:
    fib.append(fib[-2] + fib[-1])
while abs(b1 - a1) > eps:
    x = [a1 + (b1 - a1) * (fib[len(fib) - 1 - iterations[3]] / fib[len(fib) - iterations[3]]),
         a1 + (b1 - a1) * (fib[len(fib) - 1 - iterations[3]] / fib[len(fib) - iterations[3]])]
    if x[0] == x[1]:
        x[1] = x[0] + (b1 - a1) / 10
    vals = [f(i) for i in x]
    if vals[1] < vals[0]:
        a1 = x[0]
    else:
        b1 = x[1]
        iterations[3] += 1
        eps0[3].append(b1 - a1)
iterations[3] -= 1
res.append([a1, b1, iterations[3]])

lbls = ['Алгоритм равномерного поиска', 'Метод дихтомии', 'Метод золотого сечения',
        'Метод Фибоначчи']
[print(lbls[i], '\n', res[i]) for i in range(len(res))]
plt.plot(eps0[0], range(iterations[0]), label=lbls[0])
plt.plot(eps0[1], range(iterations[1]), label=lbls[1])
plt.plot(eps0[2], range(iterations[2]), label=lbls[2])
plt.plot(eps0[3], range(iterations[3]), label=lbls[3])
plt.legend()
plt.show()

f_ax = []
x_ax = np.arange(a, b, 0.01)

for j in x_ax:
    f_ax.append(f(j))

print(f_ax)