gradient_des.py

from typing import List
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from gui_gradient import gui_table
from tools import gradient, norm, fxy, backtracking_line_search, beale_function_vectorized, beale_function_gradient_vectorized, extend_list
from animation import animar_puntos

def max_descent(tol:float, max_iter:int, x0:float, y0:float)->List[float]:
    k:int = 0
    solution = np.array([x0, y0])

    x_s = [x0]
    y_s = [y0]

    alphas = []
    ks = []
    pks = []
    points = [solution]
    norms = []

    while k <= max_iter:
        pk = -gradient(solution[0], solution[1])

        if norm(beale_function_gradient_vectorized(solution)) < tol:
            break

        alpha = backtracking_line_search(beale_function_vectorized, beale_function_gradient_vectorized, solution, pk)
        solution = solution + (alpha * pk)

        x_s.append(solution[0])
        y_s.append(solution[1])

        ks.append(k)
        pks.append(pk)
        points.append(solution)
        norms.append(norm(beale_function_gradient_vectorized(solution)))
        alphas.append(alpha)
        k += 1

    plotting(x_s, y_s)
    pts = points
    max_length = max(len(ks), len(points), len(pks), len(norms), len(alphas))

    ks = extend_list(ks, max_length, 0)
    points = extend_list(points, max_length, 0)
    pks = extend_list(pks, max_length, 0)
    norms = extend_list(norms, max_length, 0)
    alphas = extend_list(alphas, max_length, 0)

    data = {
        'k' : ks,
        'x_k': points,
        'p_k': pks,
        '||∇f(x)||': norms,
        'alpha': alphas
    }

    df = pd.DataFrame(data)

    gui_table(df, 'Gradiente descendiente')
    animar_puntos(pts)
    return solution


def plotting(x_sol:np.ndarray, y_sol:np.ndarray):
    x = np.linspace(-4.5, 4.5, 100)
    y = np.linspace(-4.5, 4.5, 100)
    X, Y = np.meshgrid(x, y)
    Z = fxy(X, Y)
    plt.figure()
    contour = plt.contour(X, Y, Z)
    plt.plot(x_sol, y_sol)
    plt.title('Beale Function')
    plt.clabel(contour, inline=True, fontsize=8)
    plt.xlabel('X')
    plt.ylabel('Y')
    plt.plot(x_sol, y_sol, 'ro-', label='Soluciones en búsqueda en línea')
    plt.legend()
    plt.show()