grad[1] = 0.
return grad
Infinity = numpy.inf
# Create an instance of an NLPModel
nlp = NLPModel(n=2, m=2, name='Rosenbrock',
Lvar=numpy.array([-1000,-Infinity]), Uvar=numpy.array([Infinity,1000]),
Lcon=numpy.array([-2000,-1000]), Ucon=numpy.array([2000,1000]),
x0 = numpy.array([-1.2, 1]))
# Assign an objective function and its gradient
nlp.obj = rosenbrock
nlp.grad = gradient_rosenbrock
# Assign constraints and its gradients
nlp.cons = constraint
nlp.igrad = gradient_constraint
# Translate this NLPy problem in a pyOpt problem
opt_prob = PyOpt_From_NLPModel(nlp)
print opt_prob
# Call the imported solver SNOPT
snopt = SNOPT()
