# here we set them to the initial values, but we could
# set them to anything
nlp.set_primals(x0)
# evaluate residual of equality constraints
print(nlp.constraint_names())
res_eq = nlp.evaluate_eq_constraints()
print("Residuals of equality constraints:", res_eq)
# evaluate residual of inequality constraints
res_ineq = nlp.evaluate_ineq_constraints()
# demonstrate the use of compression from full set of
# lower and upper bounds on the inequality constraints
# to only the finite values using masks
ineqlb_mask = build_bounds_mask(nlp.ineq_lb())
inequb_mask = build_bounds_mask(nlp.ineq_ub())
# get the compressed vector
compressed_ineq_lb = full_to_compressed(nlp.ineq_lb(), ineqlb_mask)
compressed_ineq_ub = full_to_compressed(nlp.ineq_ub(), inequb_mask)
# we can also build compression matrices
Cineq_ineqlb = build_compression_matrix(ineqlb_mask)
Cineq_inequb = build_compression_matrix(inequb_mask)
# lower and upper inequalities residual
res_ineq_lb = Cineq_ineqlb * res_ineq - compressed_ineq_lb
res_ineq_ub = compressed_ineq_ub - Cineq_inequb*res_ineq
print("Residuals of inequality constraints lower bounds:", res_ineq_lb)
print("Residuals of inequality constraints upper bounds:", res_ineq_ub)
feasible = False
def main():
model = create_basic_model()
solver = pyo.SolverFactory('ipopt')
solver.solve(model, tee=True)
# build nlp initialized at the solution
nlp = PyomoNLP(model)
# get initial point
print(nlp.primals_names())
x0 = nlp.get_primals()
# vectors of lower and upper bounds
xl = nlp.primals_lb()
xu = nlp.primals_ub()
# demonstrate use of compression from full set of bounds
# to only finite bounds using masks
xlb_mask = build_bounds_mask(xl)
xub_mask = build_bounds_mask(xu)
# get the compressed vector
compressed_xl = full_to_compressed(xl, xlb_mask)
compressed_xu = full_to_compressed(xu, xub_mask)
# we can also build compression matrices
Cx_xl = build_compression_matrix(xlb_mask)
Cx_xu = build_compression_matrix(xub_mask)
# lower and upper bounds residual
res_xl = Cx_xl * x0 - compressed_xl
res_xu = compressed_xu - Cx_xu * x0
print("Residuals lower bounds x-xl:", res_xl)
print("Residuals upper bounds xu-x:", res_xu)
# set the value of the primals (we can skip the duals)
# here we set them to the initial values, but we could
# set them to anything
nlp.set_primals(x0)
# evaluate residual of equality constraints
print(nlp.constraint_names())
res_eq = nlp.evaluate_eq_constraints()
print("Residuals of equality constraints:", res_eq)
# evaluate residual of inequality constraints
res_ineq = nlp.evaluate_ineq_constraints()
# demonstrate the use of compression from full set of
# lower and upper bounds on the inequality constraints
# to only the finite values using masks
ineqlb_mask = build_bounds_mask(nlp.ineq_lb())
inequb_mask = build_bounds_mask(nlp.ineq_ub())
# get the compressed vector
compressed_ineq_lb = full_to_compressed(nlp.ineq_lb(), ineqlb_mask)
compressed_ineq_ub = full_to_compressed(nlp.ineq_ub(), inequb_mask)
# we can also build compression matrices
Cineq_ineqlb = build_compression_matrix(ineqlb_mask)
Cineq_inequb = build_compression_matrix(inequb_mask)
# lower and upper inequalities residual
res_ineq_lb = Cineq_ineqlb * res_ineq - compressed_ineq_lb
res_ineq_ub = compressed_ineq_ub - Cineq_inequb * res_ineq
print("Residuals of inequality constraints lower bounds:", res_ineq_lb)
print("Residuals of inequality constraints upper bounds:", res_ineq_ub)
feasible = False
if np.all(res_xl >= 0) and np.all(res_xu >= 0) \
and np.all(res_ineq_lb >= 0) and np.all(res_ineq_ub >= 0) and \
np.allclose(res_eq, np.zeros(nlp.n_eq_constraints()), atol=1e-5):
feasible = True
print("Is x0 feasible:", feasible)
return feasible
