is this code correct for gradient descent for two vectors (two dimensions)?

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import numpy as np from matplotlib import pyplot as plt

class GradientDescent: def __init__(self, function, gradient, initial_solution, learning_rate=0.1, max_iter=100, tolerance=0.0000001): self.function = function self.gradient = gradient self.solution = initial_solution self.learning_rate = learning_rate self.max_iter = max_iter self.tolerance = tolerance def run(self): t = 0 while t < self.max_iter: diff = -self.learning_rate * self.gradient(*self.solution) if np.linalg.norm(diff) < self.tolerance: break self.solution = tuple([self.solution[i] + diff[i] for i in range(len(diff))]) t += 1 return self.solution, self.function(*self.solution)

def fun1(x, y): return x ** 2 + y ** 2

def gradient1(x, y): return np.array([2 * x, 2 * y])

bounds = [-3, 3]

plt.figure()

x, y = np.meshgrid(np.linspace(bounds[0], bounds[1], 100), np.linspace(bounds[0], bounds[1], 100)) z = fun1(x, y) plt.contour(x, y, z,levels=20)

random_solution = np.random.uniform(bounds[0], bounds[1], size=2)

gd = GradientDescent(fun1, gradient1, random_solution)

best_solution, best_value = gd.run()

plt.plot(best_solution[0], best_solution[1])