def main():
model = create_model()
nlp = PyomoNLP(model)
# initial guesses
x = nlp.init_primals()
lam = nlp.init_duals()
nlp.set_primals(x)
nlp.set_duals(lam)
# NLP function evaluations
f = nlp.evaluate_objective()
print("Objective Function\n", f)
df = nlp.evaluate_grad_objective()
print("Gradient of Objective Function:\n", df)
c = nlp.evaluate_constraints()
print("Constraint Values:\n", c)
c_eq = nlp.evaluate_eq_constraints()
print("Equality Constraint Values:\n", c_eq)
c_ineq = nlp.evaluate_ineq_constraints()
print("Inequality Constraint Values:\n", c_ineq)
jac = nlp.evaluate_jacobian()
print("Jacobian of Constraints:\n", jac.toarray())
jac_eq = nlp.evaluate_jacobian_eq()
print("Jacobian of Equality Constraints:\n", jac_eq.toarray())
jac_ineq = nlp.evaluate_jacobian_ineq()
print("Jacobian of Inequality Constraints:\n", jac_ineq.toarray())
hess_lag = nlp.evaluate_hessian_lag()
print("Hessian of Lagrangian\n", hess_lag.toarray())
nlp.set_primals(x)
nlp.set_duals(lam)
# NLP function evaluations
f = nlp.evaluate_objective()
print("Objective Function\n", f)
df = nlp.evaluate_grad_objective()
print("Gradient of Objective Function:\n", df)
c = nlp.evaluate_constraints()
print("Constraint Values:\n", c)
c_eq = nlp.evaluate_eq_constraints()
print("Equality Constraint Values:\n", c_eq)
c_ineq = nlp.evaluate_ineq_constraints()
print("Inequality Constraint Values:\n", c_ineq)
jac = nlp.evaluate_jacobian()
print("Jacobian of Constraints:\n", jac.toarray())
jac_eq = nlp.evaluate_jacobian_eq()
print("Jacobian of Equality Constraints:\n", jac_eq.toarray())
jac_ineq = nlp.evaluate_jacobian_ineq()
print("Jacobian of Inequality Constraints:\n", jac_ineq.toarray())
hess_lag = nlp.evaluate_hessian_lag()
print("Hessian of Lagrangian\n", hess_lag.toarray())