# Constrained optimization using scipy¶

The problem is \begin{equation*} \max\{-x_0^2 - (x_1-1)^2 - 3x_0 + 2\} \end{equation*} subject to \begin{align*} 4x_0 + x_1 &\leq 0.5\\ x_0^2 + x_0x_1 &\leq 2.0\\ x_0 &\geq 0 \\ x_1 &\geq 0 \end{align*}

## Using scipy¶

The scipy.optimize.minimize function minimizes functions subject to equality constraints, inequality constraints, and bounds on the choice variables.

In [1]:
import numpy as np
from scipy.optimize import minimize

np.set_printoptions(precision=4,suppress=True)

• First, we define the objective function, changing its sign so we can minimize it
In [2]:
def f(x):
return x[0]**2 + (x[1]-1)**2 + 3*x[0] - 2

• Second, we specify the inequality constraints using a tuple of two dictionaries (one per constraint), writing each of them in the form $g_i(x) \geq 0$, that is \begin{align*} 0.5 - 4x_0 - x_1 &\geq 0\\ 2.0 - x_0^2 - x_0x_1 &\geq 0 \end{align*}
In [3]:
cons = ({'type': 'ineq', 'fun': lambda x: 0.5 - 4*x[0] - x[1]},
{'type': 'ineq', 'fun': lambda x: 2.0 - x[0]**2 - x[0]*x[1]})

• Third, we specify the bounds on $x$: \begin{align*} 0 &\leq x_0 \leq \infty\\ 0 &\leq x_1 \leq \infty \end{align*}
In [4]:
bnds = ((0, None), (0, None))

• Finally, we minimize the problem, using the SLSQP method, starting from $x=[0,1]$
In [5]:
x0 = [0.0, 1.0]
res = minimize(f, x0, method='SLSQP', bounds=bnds, constraints=cons)
print(res)

     fun: -1.7499999999999876
jac: array([ 3., -1.])
message: 'Optimization terminated successfully.'
nfev: 13
nit: 3
njev: 3
status: 0
success: True
x: array([0. , 0.5])