from scipy import optimize

def area(w):
    """ Given the weights (lengths of each side) of a rectangle, it returns
        the area.
    """
    return w[0] * w[1]

def calculate_error(w):
    """ Since we want the greatest area possible, but are performing a
        minimization oprtation, we turn the area into a negative as the loss
    """
    return -area(w)

def add2history(w):
    """ Add a new set of weights to the list of historical weight estimates """
    historical_weights.append(w)

# constraint 1 - the sum of the two sides must be less than 5
def constraint_func1(w):
    return 5 - w[0] - w[1]

# constraint 2 - the length of one side must be twice as long as the other
def constraint_func2(w):
    return w[1] - 2*w[0]

constraint = ({"type": "eq", "fun": constraint_func1},
              {"type": "ineq", "fun": constraint_func2}
              )


w0 = [0.1,4.9]               # initial guess
historical_weights = [w0]    # estimates at each iteration of optimization
result = optimize.minimize(
    fun=calculate_error,     # func that calculates value we want to optimize
    x0=w0,                   # Initial weights
    method="SLSQP",          # Optimization algorithm to use
    # args=(data),           # other positional args to pass to error function
    # bounds=((0,5), (0,5)), # constrain upper and lower values for weights
    constraints=constraint,  # Constraints
    callback=add2history,    # A function called after every iteration
    options={"disp":True}    # print feedback
    )


print("Optimal Weights: {}\nOptimal Area: {}\nSum of Lengths: {}".format(result.x, area(result.x), result.x.sum()))
print("Historical weights: ", historical_weights)

[OUTPUT]
Optimization terminated successfully.    (Exit mode 0)
            Current function value: -5.555555555555555
            Iterations: 2
            Function evaluations: 8
            Gradient evaluations: 2


Optimal Weights: [1.66666667 3.33333333]
Optimal Area: 5.555555555555555
Sum of Lengths: 5.0
Historical weights:  [[0.1, 4.9], array([1.66666667, 3.33333333]), array([1.66666667, 3.33333333]), array([1.66666667, 3.33333333]), array([1.66666667, 3.33333333])]