#338 Minimization using Newton’s method on the non-quadratic problem in python

ex338.py

import numpy as np
import math
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')

def my_f(x):
    #$f(x_1, x_2) = e^{x_1+3x_2-0.1}+e^{x_1-3x_2-0.1}+e^{-x_1-0.1}$
    x1 = x[0, 0]
    x2 = x[1, 0]
    return math.exp(x1+3*x2-0.1)+math.exp(x1-3*x2-0.1)+math.exp(-x1-0.1)

def my_gradient_f(x):
    #$\nabla f(x_1, x_2)$
    x1 = x[0, 0]
    x2 = x[1, 0]
    gradient_1=1*math.exp(x1+3*x2-0.1)+1*math.exp(x1-3*x2-0.1)-math.exp(-x1-0.1)
    gradient_2=3*math.exp(x1+3*x2-0.1)-3*math.exp(x1-3*x2-0.1)
    return np.array([[gradient_1], [gradient_2]])

def my_hessian_f(x):
    #$\nabla^2 f(x_1, x_2)$
    x1 = x[0, 0]
    x2 = x[1, 0]
    x11 = math.exp(x1 + 3 * x2 - 0.1) + math.exp(x1 - 3 * x2 - 0.1) + math.exp(-x1 - 0.1)
    x12 = 3 * math.exp(x1 + 3 * x2 - 0.1) - 3 * math.exp(x1 - 3 * x2 - 0.1)
    x21 = 3 * math.exp(x1 + 3 * x2 - 0.1) - 3 * math.exp(x1 - 3 * x2 - 0.1)
    x22 = 9 * math.exp(x1 + 3 * x2 - 0.1) + 9 * math.exp(x1 - 3 * x2 - 0.1)
    return np.array([[x11, x12], [x21, x22]])

def my_newton_step_decrement(x):
    # calculate newton step $\Delta x_{nt} = -[\nabla^2 f(x)]^{-1}\nabla f(x) $
    gradient = my_gradient_f(x)
    hessian = my_hessian_f(x)
    hessian_inv = np.linalg.inv(hessian)
    x_nt = -1 * hessian_inv @ gradient
    decrement = -1 * gradient.T @ x_nt
    return x_nt, decrement.item()

def backtracking_line_search(f, df, x, delta_x, alpha, beta):
    # backtracking line search
    t = 1
    while f(x + delta_x * t) > f(x) + alpha * t * ((df(x).T @ delta_x).item()):
        t = beta * t
    return t


def plot_ellipse_by_matrix(P, N):
    # Plot a 2D ellipse according to the $X^TPX=1$
    W, V = np.linalg.eig(P)
    a = 1 / np.sqrt(W[0])
    b = 1 / np.sqrt(W[1])
    n_p = N
    y = np.zeros((n_p + 1, 2, 1), dtype='complex_')
    for i in range(n_p + 1):
        omega = i * 2 * np.pi / n_p
        y[i, 0, 0] = a * np.cos(omega)
        y[i, 1, 0] = b * np.sin(omega)

    x = np.zeros((n_p + 1, 2, 1), dtype='complex_')
    for i in range(n_p+1):
        x[i] = V @ y[i]

    return x
        
def plot_convergence_Newton_method_non_quadratic_problem(ax1, ax2):
    # Plot the convergence of the Newton's method on the non-quadratic problem
    min_x1 = -6
    max_x1 = 5
    min_x2 = -4
    max_x2 = 4
    delta = 0.1
    x = np.arange(min_x1, max_x1, delta)
    y = np.arange(min_x2, max_x2, delta)
    X, Y = np.meshgrid(x, y)
    Z = np.zeros(X.shape)
    f_min = my_f(np.array([[0.5 * math.log(0.5)], [0]]))
    for x1_i in range(len(x)):
        for x2_i in range(len(y)):
            x1 = x[x1_i]
            x2 = y[x2_i]
            Z[x2_i][x1_i] = my_f(np.array([[x1], [x2]]))

    levels = np.arange(0, 200, 1)
    CS = ax1.contour(X, Y, Z, linestyles='dashed', levels=levels, vmin=0,vmax=100)
    ax1.clabel(CS, fontsize=4, inline=True)
    alpha = 0.1
    beta = 0.7
    x0 = np.array([[-2], [2]])
    x = x0
    epsilon = 1e-10
    x_nt, decrement = my_newton_step_decrement(x)
    x_points = [x]
    fx = [my_f(x)]
    iter = 0
    while decrement/2 > epsilon:
        if iter < 4:
            hessian = my_hessian_f(x)
            v = plot_ellipse_by_matrix(hessian, 100)
            if iter == 0:
                ax1.plot(v[:,0, 0] + x[0, 0], v[:, 1, 0] + x[1, 0],color='red',
                         label=r'ellipsoid {$x^{(k)}$+x||$x^T\nabla^2f(x) x$|=1}')
            else:
                ax1.plot(v[:, 0, 0] + x[0, 0], v[:, 1, 0] + x[1, 0],color='red')
        print('Info: decrement = {}, fx={}'.format(decrement, fx[-1]))
        t = backtracking_line_search(my_f, my_gradient_f, x, x_nt, alpha, beta)
        x_next = x + t * x_nt
        x = x_next
        x_nt, decrement = my_newton_step_decrement(x)
        x_points.append(x)
        fx.append(my_f(x))
        iter = iter + 1
    x_points_arr = np.array(x_points)
    ax1.plot(x_points_arr[:, 0], x_points_arr[:, 1], color='black',marker='o',
             label='Newton descent')
    ax1.set_aspect('auto', adjustable='box')
    ax1.legend()
    ax1.set_xlabel(r'$x_1$')
    ax1.set_ylabel(r'$x_2$')
    ax1.set_title('f(x)')
    ax1.grid()
    ax2.semilogy(np.array(fx) - f_min, marker='o', label=r"Newton's method")
    ax2.grid()
    ax2.legend()
    ax2.set_xlabel('iteration')
    ax2.set_title(r'$f(x)-p^*$')

if __name__ == "__main__":
    plt.figure(figsize=(16, 8))
    ax1 = plt.subplot(1, 2, 1)
    ax2 = plt.subplot(1, 2, 2)
    plot_convergence_Newton_method_non_quadratic_problem(ax1, ax2)
    plt.savefig('ex338.png', dpi=100)
    plt.show()

Last Updated on 2023-01-16 by gantovnik