import numpy as np

# %% Def of objective function:

def fun_f(x, L):
    A = (L/2 - x)*x
    f = -A

    return f

# %% Model param:
    
L = 40  # [m^2]

# %% Initialization:

x_lb = 0
x_ub = L/2
N_x = 100
x_array = np.linspace(x_lb, x_ub, N_x)

f_min = np.inf
x_opt = 0  # Strictly not necessary to define here

# %% Grid method of optimization:

for x in x_array:
    f = fun_f(x, L)
    if (f < f_min):  # Improvement of solution
        f_min = f  # Updating objective function
        x_opt = x  # Updating optimal solution

# %% Presenting results:

print('x_opt =', f'{x_opt:.6f}')
print('f_min =', f'{f_min:.6f}')