# List to store the best objective value for each number of iterations
best_obj_list = []

# Range of number of iterations to test
num_iterations_range = np.logspace(0, 6, 20).astype(int)

# Run the greedy algorithm for each number of iterations and store the best objective value
for num_iterations in num_iterations_range:
    _, best_obj = greedy_optimization(TOTAL_BUDGET, alphas, betas, num_iterations)
    best_obj_list.append(best_obj)