scipy: minimize returns success with a result full of nans

scipy.minimize(method='SLSQP') returns success even though the result is nan. Note that the constraints are not satisfiable.

Reproducing code example:

from scipy.optimize import minimize
import numpy as np

if __name__ == "__main__":
    np.random.seed(10)

    cons = ({'type': 'ineq', 'fun': lambda x: -x[0] - x[1] - 3},
            {'type': 'ineq', 'fun': lambda x: x[1] + x[2] - 2})
    bnds = ((-2, 2), (-2, 2), (-2, 2))

    target = lambda x: 1
    res = minimize(target, [-1.8869783504471584, -0.640096352696244, -0.8174212253407696], method='SLSQP', bounds=bnds, constraints=cons, options={'disp':False, 'maxiter':10000})
    for i, c in enumerate(cons):
        print("the value of constraint function {} is {}".format(i, c['fun'](res.x)))
    print(res)

Error message:

the value of constraint function 0 is nan
the value of constraint function 1 is nan
     fun: 1.0
     jac: array([0., 0., 0.])
 message: 'Optimization terminated successfully.'
    nfev: 106
     nit: 10
    njev: 9
  status: 0
 success: True
       x: array([nan, nan, nan])

Scipy/Numpy/Python version information:

scipy 1.1.0 numpy 1.15.4 python 3.6.6