from scipy.optimize import minimize

#Define objective function
def objective_function(x):
    return 0.5 * x @ Q @ x + p @ x

#Define constraints
def constraint(x):
    return G @ x - h

#Define optimization
con = {'type': 'ineq', 'fun': constraint} 
b = (0,10); bnds = (b,b)
opt = {'maxiter':1000}
res = minimize(objective_function, x0,
               constraints=con,bounds=bnds,
               method='SLSQP',options=opt)

#print results
print(f'Optimal solution: x = {res.x}')
print(f'Minimum weight = {res.fun}')