def evaluate_hessian_equality_constraints(self):
c = self._input_values[1]
F = self._input_values[2]
irow = np.asarray([2, 2], dtype=np.int64)
jcol = np.asarray([1, 2], dtype=np.int64)
nonzeros = self._eq_con_mult_values[0]*np.asarray([8*F, 8*c], dtype=np.float64)
hess = spa.coo_matrix((nonzeros, (irow, jcol)), shape=(4,4))
return hess
def evaluate_jacobian_outputs(self):
c = self._input_values[1]
F = self._input_values[2]
irow = np.asarray([0, 0, 0], dtype=np.int64)
jcol = np.asarray([0, 1, 2], dtype=np.int64)
nonzeros = np.asarray([1, -4 * F**2, -4 * c * 2 * F], dtype=np.float64)
jac = spa.coo_matrix((nonzeros, (irow, jcol)), shape=(1, 3))
return jac
def evaluate_jacobian_equality_constraints(self):
c = self._input_values[1]
F = self._input_values[2]
irow = np.asarray([0, 0, 0, 0, 1, 1, 1, 1], dtype=np.int64)
jcol = np.asarray([0, 1, 2, 3, 1, 2, 3, 4], dtype=np.int64)
nonzeros = np.asarray([-1, F**2, 2*c*F, 1, 2*F**2, 4*c*F, -1, 1], dtype=np.float64)
jac = spa.coo_matrix((nonzeros, (irow, jcol)), shape=(2,5))
return jac
def evaluate_hessian_outputs(self):
c = self._input_values[1]
F = self._input_values[2]
irow = np.asarray([2, 2], dtype=np.int64)
jcol = np.asarray([1, 2], dtype=np.int64)
data = self._output_con_mult_values[0]*np.asarray([-8*F, -8*c], dtype=np.float64)
hess = spa.coo_matrix((data, (irow, jcol)), shape=(3,3))
return hess
def evaluate_hessian_outputs(self):
c = self._input_values[1]
F = self._input_values[2]
y1 = self._output_con_mult_values[0]
y2 = self._output_con_mult_values[1]
irow = np.asarray([2, 2], dtype=np.int64)
jcol = np.asarray([1, 2], dtype=np.int64)
nonzeros = np.asarray([y1*(-2*F) + y2*(-8*F), y1*(-2*c)+y2*(-8*c)], dtype=np.float64)
hess = spa.coo_matrix((nonzeros, (irow, jcol)), shape=(5,5))
return hess
def test_interface(self):
# weird, this is really a test of the test class above
# but we could add code later, so...
iom = PressureDropModel()
iom.set_inputs(np.ones(4))
o = iom.evaluate_outputs()
expected_o = np.asarray([0.0, -1.0], dtype=np.float64)
self.assertTrue(np.array_equal(o, expected_o))
jac = iom.evaluate_derivatives()
expected_jac = np.asarray([[1, -1, 0, -2], [1, -1, -1, -4]], dtype=np.float64)
self.assertTrue(np.array_equal(jac.todense(), expected_jac))
def evaluate_outputs(self):
Pin = self._input_values[0]
c = self._input_values[1]
F = self._input_values[2]
P1 = self._input_values[3]
return np.asarray([P1 - c * F**2, Pin - 4 * c * F**2],
dtype=np.float64)
def evaluate_outputs(self):
Pin = self._input_values[0]
c = self._input_values[1]
F = self._input_values[2]
P2 = Pin - 2 * c * F**2
Pout = Pin - 4 * c * F**2
return np.asarray([P2, Pout], dtype=np.float64)
def evaluate_equality_constraints(self):
Pin = self._input_values[0]
c = self._input_values[1]
F = self._input_values[2]
P1 = self._input_values[3]
P3 = self._input_values[4]
return np.asarray([P1 - (Pin - c*F**2), P3 - (P1 - 2*c*F**2)], dtype=np.float64)
def evaluate_equality_constraints(self):
Pin = self._input_values[0]
c = self._input_values[1]
F = self._input_values[2]
P2 = self._input_values[3]
Pout = self._input_values[4]
return np.asarray([P2 - (Pin - 2*c*F**2), Pout - (P2 - 2*c*F**2)], dtype=np.float64)
def evaluate_derivatives(self):
jac = [[1, -self._F**2, 0, -2 * self._c1 * self._F],
[
1, -self._F**2, -self._F**2,
-2 * self._F * (self._c1 + self._c2)
]]
jac = np.asarray(jac, dtype=np.float64)
return spa.coo_matrix(jac)
def test_util_maps(self):
anlp = AslNLP(self.filename)
full_to_compressed_mask = build_compression_mask_for_finite_values(anlp.primals_lb())
# test build_bounds_mask - should be the same as above
self.assertTrue(np.array_equal(full_to_compressed_mask, build_bounds_mask(anlp.primals_lb())))
expected_compressed_primals_lb = np.asarray([-1, 2, -3, -5, -7, -9], dtype=np.float64)
# test build_compression_matrix
C = build_compression_matrix(full_to_compressed_mask)
compressed_primals_lb = C*anlp.primals_lb()
self.assertTrue(np.array_equal(expected_compressed_primals_lb, compressed_primals_lb))
# test full_to_compressed
compressed_primals_lb = full_to_compressed(anlp.primals_lb(), full_to_compressed_mask)
self.assertTrue(np.array_equal(expected_compressed_primals_lb, compressed_primals_lb))
# test in place
compressed_primals_lb = np.zeros(len(expected_compressed_primals_lb))
ret = full_to_compressed(anlp.primals_lb(), full_to_compressed_mask, out=compressed_primals_lb)
self.assertTrue(ret is compressed_primals_lb)
self.assertTrue(np.array_equal(expected_compressed_primals_lb, compressed_primals_lb))
# test compressed_to_full
expected_full_primals_lb = np.asarray([-1, 2, -3, -np.inf, -5, -np.inf, -7, -np.inf, -9], dtype=np.float64)
full_primals_lb = compressed_to_full(compressed_primals_lb, full_to_compressed_mask, default=-np.inf)
self.assertTrue(np.array_equal(expected_full_primals_lb, full_primals_lb))
# test in place
full_primals_lb.fill(0.0)
ret = compressed_to_full(compressed_primals_lb, full_to_compressed_mask, out=full_primals_lb, default=-np.inf)
self.assertTrue(ret is full_primals_lb)
self.assertTrue(np.array_equal(expected_full_primals_lb, full_primals_lb))
# test no default
expected_full_primals_lb = np.asarray([-1, 2, -3, np.nan, -5, np.nan, -7, np.nan, -9], dtype=np.float64)
full_primals_lb = compressed_to_full(compressed_primals_lb, full_to_compressed_mask)
print(expected_full_primals_lb)
print(full_primals_lb)
np.testing.assert_array_equal(expected_full_primals_lb, full_primals_lb)
# test in place no default
expected_full_primals_lb = np.asarray([-1, 2, -3, 0.0, -5, 0.0, -7, 0.0, -9], dtype=np.float64)
full_primals_lb.fill(0.0)
ret = compressed_to_full(compressed_primals_lb, full_to_compressed_mask, out=full_primals_lb)
self.assertTrue(ret is full_primals_lb)
self.assertTrue(np.array_equal(expected_full_primals_lb, full_primals_lb))
def test_repeated_row_col(self):
data = [1.0, 0.0, 2.0]
row = [1, 2, 1]
col = [2, 2, 2]
A = scipy.sparse.coo_matrix((data, (row, col)), shape=(3, 3))
data = [3.0, 0.0]
row = [1, 2]
col = [2, 2]
B = scipy.sparse.coo_matrix((data, (row, col)), shape=(3, 3))
# Our CondensedSparseSummation should not remove any
# structural nonzeros
sparse_sum = utils.CondensedSparseSummation([A, B])
C = sparse_sum.sum([A, B])
expected_data = np.asarray([6.0, 0.0], dtype=np.float64)
expected_row = np.asarray([1, 2], dtype=np.int64)
expected_col = np.asarray([2, 2], dtype=np.int64)
self.assertTrue(np.array_equal(expected_data, C.data))
self.assertTrue(np.array_equal(expected_row, C.row))
self.assertTrue(np.array_equal(expected_col, C.col))
def test_nlp_interface(self):
nlp = PyomoNLP(self.pm)
execute_extended_nlp_interface(self, nlp)
self.assertTrue(nlp.pyomo_model() is self.pm)
self.assertEqual(float(nlp.get_obj_scaling()), 5.0)
xs = nlp.get_primals_scaling()
expected_xs = np.asarray([2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0])
self.assertTrue(np.array_equal(xs, expected_xs))
cs = nlp.get_constraints_scaling()
expected_cs = np.asarray([ 3.0, 6.0, 9.0, 12.0, 15.0, 18.0, 21.0, 24.0, 27.0 ])
self.assertTrue(np.array_equal(cs, expected_cs))
eqcs = nlp.get_eq_constraints_scaling()
expected_eqcs = np.asarray([ 6.0, 18.0 ])
self.assertTrue(np.array_equal(eqcs, expected_eqcs))
ineqcs = nlp.get_ineq_constraints_scaling()
expected_ineqcs = np.asarray([ 3.0, 9.0, 12.0, 15.0, 21.0, 24.0, 27.0 ])
self.assertTrue(np.array_equal(ineqcs, expected_ineqcs))
def test_condensed_sparse_summation(self):
data = [1.0, 0.0]
row = [1, 2]
col = [2, 2]
A = scipy.sparse.coo_matrix((data, (row, col)), shape=(3, 3))
data = [3.0, 0.0]
B = scipy.sparse.coo_matrix((data, (row, col)), shape=(3, 3))
# By default, scipy will remove structural nonzeros that
# have zero values
C = A + B
self.assertEqual(C.nnz, 1)
# Our CondensedSparseSummation should not remove any
# structural nonzeros
sparse_sum = utils.CondensedSparseSummation([A, B])
C = sparse_sum.sum([A, B])
expected_data = np.asarray([4.0, 0.0], dtype=np.float64)
expected_row = np.asarray([1, 2], dtype=np.int64)
expected_col = np.asarray([2, 2], dtype=np.int64)
self.assertTrue(np.array_equal(expected_data, C.data))
self.assertTrue(np.array_equal(expected_row, C.row))
self.assertTrue(np.array_equal(expected_col, C.col))
B.data[1] = 5.0
C = sparse_sum.sum([A, B])
expected_data = np.asarray([4.0, 5.0], dtype=np.float64)
self.assertTrue(np.array_equal(expected_data, C.data))
self.assertTrue(np.array_equal(expected_row, C.row))
self.assertTrue(np.array_equal(expected_col, C.col))
B.data[1] = 0.0
C = sparse_sum.sum([A, B])
expected_data = np.asarray([4.0, 0.0], dtype=np.float64)
self.assertTrue(np.array_equal(expected_data, C.data))
self.assertTrue(np.array_equal(expected_row, C.row))
self.assertTrue(np.array_equal(expected_col, C.col))
def evaluate_equality_constraints(self):
return np.asarray([self._u**2 - 1])
def test_indices_methods(self):
nlp = PyomoNLP(self.pm)
# get_pyomo_variables
variables = nlp.get_pyomo_variables()
expected_ids = [id(self.pm.x[i]) for i in range(1, 10)]
ids = [id(variables[i]) for i in range(9)]
self.assertTrue(expected_ids == ids)
variable_names = nlp.variable_names()
expected_names = [self.pm.x[i].getname() for i in range(1, 10)]
self.assertTrue(variable_names == expected_names)
# get_pyomo_constraints
constraints = nlp.get_pyomo_constraints()
expected_ids = [id(self.pm.c[i]) for i in range(1, 10)]
ids = [id(constraints[i]) for i in range(9)]
self.assertTrue(expected_ids == ids)
constraint_names = nlp.constraint_names()
expected_names = [c.getname() for c in nlp.get_pyomo_constraints()]
self.assertTrue(constraint_names == expected_names)
# get_primal_indices
expected_primal_indices = [i for i in range(9)]
self.assertTrue(
expected_primal_indices == nlp.get_primal_indices([self.pm.x]))
expected_primal_indices = [0, 3, 8, 4]
variables = [self.pm.x[1], self.pm.x[4], self.pm.x[9], self.pm.x[5]]
self.assertTrue(
expected_primal_indices == nlp.get_primal_indices(variables))
# get_constraint_indices
expected_constraint_indices = [i for i in range(9)]
self.assertTrue(expected_constraint_indices ==
nlp.get_constraint_indices([self.pm.c]))
expected_constraint_indices = [0, 3, 8, 4]
constraints = [self.pm.c[1], self.pm.c[4], self.pm.c[9], self.pm.c[5]]
self.assertTrue(expected_constraint_indices ==
nlp.get_constraint_indices(constraints))
# extract_subvector_grad_objective
expected_gradient = np.asarray(
[2 * sum((i + 1) * (j + 1) for j in range(9)) for i in range(9)],
dtype=np.float64)
grad_obj = nlp.extract_subvector_grad_objective([self.pm.x])
self.assertTrue(np.array_equal(expected_gradient, grad_obj))
expected_gradient = np.asarray([
2 * sum((i + 1) * (j + 1) for j in range(9)) for i in [0, 3, 8, 4]
],
dtype=np.float64)
variables = [self.pm.x[1], self.pm.x[4], self.pm.x[9], self.pm.x[5]]
grad_obj = nlp.extract_subvector_grad_objective(variables)
self.assertTrue(np.array_equal(expected_gradient, grad_obj))
# extract_subvector_constraints
expected_con = np.asarray(
[45, 88, 3 * 45, 4 * 45, 5 * 45, 276, 7 * 45, 8 * 45, 9 * 45],
dtype=np.float64)
con = nlp.extract_subvector_constraints([self.pm.c])
self.assertTrue(np.array_equal(expected_con, con))
expected_con = np.asarray([45, 4 * 45, 9 * 45, 5 * 45],
dtype=np.float64)
constraints = [self.pm.c[1], self.pm.c[4], self.pm.c[9], self.pm.c[5]]
con = nlp.extract_subvector_constraints(constraints)
self.assertTrue(np.array_equal(expected_con, con))
# extract_submatrix_jacobian
expected_jac = [[(i) * (j) for j in range(1, 10)]
for i in range(1, 10)]
expected_jac = np.asarray(expected_jac, dtype=np.float64)
jac = nlp.extract_submatrix_jacobian(pyomo_variables=[self.pm.x],
pyomo_constraints=[self.pm.c])
dense_jac = jac.todense()
self.assertTrue(np.array_equal(dense_jac, expected_jac))
expected_jac = [[(i) * (j) for j in [1, 4, 9, 5]] for i in [2, 6, 4]]
expected_jac = np.asarray(expected_jac, dtype=np.float64)
variables = [self.pm.x[1], self.pm.x[4], self.pm.x[9], self.pm.x[5]]
constraints = [self.pm.c[2], self.pm.c[6], self.pm.c[4]]
jac = nlp.extract_submatrix_jacobian(pyomo_variables=variables,
pyomo_constraints=constraints)
dense_jac = jac.todense()
self.assertTrue(np.array_equal(dense_jac, expected_jac))
# extract_submatrix_hessian_lag
expected_hess = [[2.0 * i * j for j in range(1, 10)]
for i in range(1, 10)]
expected_hess = np.asarray(expected_hess, dtype=np.float64)
hess = nlp.extract_submatrix_hessian_lag(
pyomo_variables_rows=[self.pm.x], pyomo_variables_cols=[self.pm.x])
dense_hess = hess.todense()
self.assertTrue(np.array_equal(dense_hess, expected_hess))
expected_hess = [[2.0 * i * j for j in [1, 4, 9, 5]]
for i in [1, 4, 9, 5]]
expected_hess = np.asarray(expected_hess, dtype=np.float64)
variables = [self.pm.x[1], self.pm.x[4], self.pm.x[9], self.pm.x[5]]
hess = nlp.extract_submatrix_hessian_lag(
pyomo_variables_rows=variables, pyomo_variables_cols=variables)
dense_hess = hess.todense()
self.assertTrue(np.array_equal(dense_hess, expected_hess))
def execute_extended_nlp_interface(self, anlp):
self.assertEqual(anlp.n_primals(), 9)
self.assertEqual(anlp.n_constraints(), 9)
self.assertEqual(anlp.n_eq_constraints(), 2)
self.assertEqual(anlp.n_ineq_constraints(), 7)
self.assertEqual(anlp.nnz_jacobian(), 9 * 9)
self.assertEqual(anlp.nnz_jacobian_eq(), 2 * 9)
self.assertEqual(anlp.nnz_jacobian_ineq(), 7 * 9)
self.assertEqual(anlp.nnz_hessian_lag(), 9 * 9)
expected_primals_lb = np.asarray(
[-1, -np.inf, -3, -np.inf, -5, -np.inf, -7, -np.inf, -9],
dtype=np.float64)
expected_primals_ub = np.asarray(
[1, 2, np.inf, np.inf, 5, 6, np.inf, np.inf, 9], dtype=np.float64)
self.assertTrue(np.array_equal(expected_primals_lb, anlp.primals_lb()))
self.assertTrue(np.array_equal(expected_primals_ub, anlp.primals_ub()))
expected_constraints_lb = np.asarray(
[-1, 0, -3, -np.inf, -5, 0, -7, -np.inf, -9], dtype=np.float64)
expected_constraints_ub = np.asarray([1, 0, np.inf, 4, 5, 0, np.inf, 8, 9],
dtype=np.float64)
self.assertTrue(
np.array_equal(expected_constraints_lb, anlp.constraints_lb()))
self.assertTrue(
np.array_equal(expected_constraints_ub, anlp.constraints_ub()))
expected_ineq_lb = np.asarray([-1, -3, -np.inf, -5, -7, -np.inf, -9],
dtype=np.float64)
expected_ineq_ub = np.asarray([1, np.inf, 4, 5, np.inf, 8, 9],
dtype=np.float64)
self.assertTrue(np.array_equal(expected_ineq_lb, anlp.ineq_lb()))
self.assertTrue(np.array_equal(expected_ineq_ub, anlp.ineq_ub()))
expected_init_primals = np.ones(9)
self.assertTrue(np.array_equal(expected_init_primals, anlp.init_primals()))
expected_init_duals = np.asarray([1, 2, 3, 4, 5, 6, 7, 8, 9],
dtype=np.float64)
self.assertTrue(np.array_equal(expected_init_duals, anlp.init_duals()))
expected_init_duals_ineq = np.asarray([1, 3, 4, 5, 7, 8, 9],
dtype=np.float64)
self.assertTrue(
np.array_equal(expected_init_duals_ineq, anlp.init_duals_ineq()))
expected_init_duals_eq = np.asarray([2, 6], dtype=np.float64)
self.assertTrue(
np.array_equal(expected_init_duals_eq, anlp.init_duals_eq()))
t = anlp.create_new_vector('primals')
self.assertTrue(t.size == 9)
t = anlp.create_new_vector('constraints')
self.assertTrue(t.size == 9)
t = anlp.create_new_vector('eq_constraints')
self.assertTrue(t.size == 2)
t = anlp.create_new_vector('ineq_constraints')
self.assertTrue(t.size == 7)
t = anlp.create_new_vector('duals')
self.assertTrue(t.size == 9)
t = anlp.create_new_vector('duals_eq')
self.assertTrue(t.size == 2)
t = anlp.create_new_vector('duals_ineq')
self.assertTrue(t.size == 7)
expected_primals = [i + 1 for i in range(9)]
new_primals = np.asarray(expected_primals, dtype=np.float64)
expected_primals = np.asarray(expected_primals, dtype=np.float64)
anlp.set_primals(new_primals)
ret = anlp.get_primals()
self.assertTrue(np.array_equal(new_primals, ret))
self.assertTrue(np.array_equal(expected_primals, anlp._primals))
anlp.set_primals(np.ones(9))
expected_duals = [i + 1 for i in range(9)]
new_duals = np.asarray(expected_duals, dtype=np.float64)
expected_duals = np.asarray(expected_duals, dtype=np.float64)
anlp.set_duals(new_duals)
self.assertTrue(np.array_equal(expected_duals, anlp._duals_full))
ret = anlp.get_duals()
self.assertTrue(np.array_equal(new_duals, ret))
expected_duals_eq = np.asarray([2, 6], dtype=np.float64)
self.assertTrue(np.array_equal(expected_duals_eq, anlp._duals_eq))
expected_duals_ineq = np.asarray([1, 3, 4, 5, 7, 8, 9], dtype=np.float64)
self.assertTrue(np.array_equal(expected_duals_ineq, anlp._duals_ineq))
anlp.set_duals(np.ones(9))
expected_duals_eq = [i + 1 for i in range(2)]
new_duals_eq = np.asarray(expected_duals_eq, dtype=np.float64)
anlp.set_duals_eq(new_duals_eq)
ret = anlp.get_duals_eq()
self.assertTrue(np.array_equal(new_duals_eq, ret))
self.assertTrue(np.array_equal(expected_duals_eq, anlp._duals_eq))
expected_duals = np.asarray([1, 1, 1, 1, 1, 2, 1, 1, 1], dtype=np.float64)
self.assertTrue(np.array_equal(expected_duals, anlp._duals_full))
expected_duals_ineq = np.asarray([1, 1, 1, 1, 1, 1, 1], dtype=np.float64)
self.assertTrue(np.array_equal(expected_duals_ineq, anlp._duals_ineq))
anlp.set_duals_eq(np.ones(2))
expected_duals = np.asarray([1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=np.float64)
self.assertTrue(np.array_equal(expected_duals, anlp._duals_full))
expected_duals_ineq = [i + 1 for i in range(7)]
new_duals_ineq = np.asarray(expected_duals_ineq, dtype=np.float64)
anlp.set_duals_ineq(new_duals_ineq)
ret = anlp.get_duals_ineq()
self.assertTrue(np.array_equal(new_duals_ineq, ret))
self.assertTrue(np.array_equal(expected_duals_ineq, anlp._duals_ineq))
expected_duals = np.asarray([1, 1, 2, 3, 4, 1, 5, 6, 7], dtype=np.float64)
self.assertTrue(np.array_equal(expected_duals, anlp._duals_full))
expected_duals_eq = np.asarray([1, 1], dtype=np.float64)
self.assertTrue(np.array_equal(expected_duals_eq, anlp._duals_eq))
anlp.set_duals_ineq(np.ones(7))
expected_duals = np.asarray([1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=np.float64)
self.assertTrue(np.array_equal(expected_duals, anlp._duals_full))
# objective function
expected_objective = sum(
(i + 1) * (j + 1) for i in range(9) for j in range(9))
self.assertEqual(expected_objective, anlp.evaluate_objective())
# change the value of the primals
anlp.set_primals(2.0 * np.ones(9))
expected_objective = sum(2.0**2 * (i + 1) * (j + 1) for i in range(9)
for j in range(9))
self.assertEqual(expected_objective, anlp.evaluate_objective())
anlp.set_primals(np.ones(9))
# gradient of the objective
expected_gradient = np.asarray(
[2 * sum((i + 1) * (j + 1) for j in range(9)) for i in range(9)],
dtype=np.float64)
grad_obj = anlp.evaluate_grad_objective()
self.assertTrue(np.array_equal(expected_gradient, grad_obj))
# test inplace
grad_obj = np.ones(9)
ret = anlp.evaluate_grad_objective(out=grad_obj)
self.assertTrue(ret is grad_obj)
self.assertTrue(np.array_equal(expected_gradient, grad_obj))
# change the value of the primals
anlp.set_primals(2.0 * np.ones(9))
expected_gradient = np.asarray(
[2 * 2 * sum((i + 1) * (j + 1) for j in range(9)) for i in range(9)],
dtype=np.float64)
grad_obj = np.ones(9)
anlp.evaluate_grad_objective(out=grad_obj)
self.assertTrue(np.array_equal(expected_gradient, grad_obj))
anlp.set_primals(np.ones(9))
# full constraints
con = anlp.evaluate_constraints()
expected_con = np.asarray(
[45, 88, 3 * 45, 4 * 45, 5 * 45, 276, 7 * 45, 8 * 45, 9 * 45],
dtype=np.float64)
self.assertTrue(np.array_equal(expected_con, con))
# test inplace
con = np.zeros(9)
ret = anlp.evaluate_constraints(out=con)
self.assertTrue(ret is con)
self.assertTrue(np.array_equal(expected_con, con))
# change the value of the primals
anlp.set_primals(2.0 * np.ones(9))
con = np.zeros(9)
anlp.evaluate_constraints(out=con)
expected_con = np.asarray([
2 * 45, 2 * (88 + 2) - 2, 2 * 3 * 45, 2 * 4 * 45, 2 * 5 * 45, 2 *
(276 - 6) + 6, 2 * 7 * 45, 2 * 8 * 45, 2 * 9 * 45
],
dtype=np.float64)
self.assertTrue(np.array_equal(expected_con, con))
anlp.set_primals(np.ones(9))
# equality constraints
con_eq = anlp.evaluate_eq_constraints()
expected_con_eq = np.asarray([88, 276], dtype=np.float64)
self.assertTrue(np.array_equal(expected_con_eq, con_eq))
# test inplace
con_eq = np.zeros(2)
ret = anlp.evaluate_eq_constraints(out=con_eq)
self.assertTrue(ret is con_eq)
self.assertTrue(np.array_equal(expected_con_eq, con_eq))
# change the value of the primals
anlp.set_primals(2.0 * np.ones(9))
con_eq = np.zeros(2)
anlp.evaluate_eq_constraints(out=con_eq)
expected_con_eq = np.asarray([2 * (88 + 2) - 2, 2 * (276 - 6) + 6],
dtype=np.float64)
self.assertTrue(np.array_equal(expected_con_eq, con_eq))
anlp.set_primals(np.ones(9))
# inequality constraints
con_ineq = anlp.evaluate_ineq_constraints()
expected_con_ineq = np.asarray(
[45, 3 * 45, 4 * 45, 5 * 45, 7 * 45, 8 * 45, 9 * 45], dtype=np.float64)
self.assertTrue(np.array_equal(expected_con_ineq, con_ineq))
# test inplace
con_ineq = np.zeros(7)
ret = anlp.evaluate_ineq_constraints(out=con_ineq)
self.assertTrue(ret is con_ineq)
self.assertTrue(np.array_equal(expected_con_ineq, con_ineq))
# change the value of the primals
anlp.set_primals(2.0 * np.ones(9))
con_ineq = np.zeros(7)
anlp.evaluate_ineq_constraints(out=con_ineq)
expected_con_ineq = 2.0 * expected_con_ineq
self.assertTrue(np.array_equal(expected_con_ineq, con_ineq))
anlp.set_primals(np.ones(9))
# jacobian of all constraints
jac = anlp.evaluate_jacobian()
dense_jac = jac.todense()
expected_jac = [[(i) * (j) for j in range(1, 10)] for i in range(1, 10)]
expected_jac = np.asarray(expected_jac, dtype=np.float64)
self.assertTrue(np.array_equal(dense_jac, expected_jac))
# test inplace
jac.data = 0 * jac.data
ret = anlp.evaluate_jacobian(out=jac)
self.assertTrue(ret is jac)
dense_jac = jac.todense()
self.assertTrue(np.array_equal(dense_jac, expected_jac))
# change the value of the primals
# ToDo: not a great test since this problem is linear
anlp.set_primals(2.0 * np.ones(9))
anlp.evaluate_jacobian(out=jac)
dense_jac = jac.todense()
self.assertTrue(np.array_equal(dense_jac, expected_jac))
# jacobian of equality constraints
jac_eq = anlp.evaluate_jacobian_eq()
dense_jac_eq = jac_eq.todense()
expected_jac_eq = np.asarray([[2, 4, 6, 8, 10, 12, 14, 16, 18],
[6, 12, 18, 24, 30, 36, 42, 48, 54]],
dtype=np.float64)
self.assertTrue(np.array_equal(dense_jac_eq, expected_jac_eq))
# test inplace
jac_eq.data = 0 * jac_eq.data
ret = anlp.evaluate_jacobian_eq(out=jac_eq)
self.assertTrue(ret is jac_eq)
dense_jac_eq = jac_eq.todense()
self.assertTrue(np.array_equal(dense_jac_eq, expected_jac_eq))
# change the value of the primals
# ToDo: not a great test since this problem is linear
anlp.set_primals(2.0 * np.ones(9))
anlp.evaluate_jacobian_eq(out=jac_eq)
dense_jac_eq = jac_eq.todense()
self.assertTrue(np.array_equal(dense_jac_eq, expected_jac_eq))
# jacobian of inequality constraints
jac_ineq = anlp.evaluate_jacobian_ineq()
dense_jac_ineq = jac_ineq.todense()
expected_jac_ineq = [[(i) * (j) for j in range(1, 10)]
for i in [1, 3, 4, 5, 7, 8, 9]]
expected_jac_ineq = np.asarray(expected_jac_ineq, dtype=np.float64)
self.assertTrue(np.array_equal(dense_jac_ineq, expected_jac_ineq))
# test inplace
jac_ineq.data = 0 * jac_ineq.data
ret = anlp.evaluate_jacobian_ineq(out=jac_ineq)
self.assertTrue(ret is jac_ineq)
dense_jac_ineq = jac_ineq.todense()
self.assertTrue(np.array_equal(dense_jac_ineq, expected_jac_ineq))
# change the value of the primals
# ToDo: not a great test since this problem is linear
anlp.set_primals(2.0 * np.ones(9))
anlp.evaluate_jacobian_ineq(out=jac_ineq)
dense_jac_ineq = jac_ineq.todense()
self.assertTrue(np.array_equal(dense_jac_ineq, expected_jac_ineq))
# hessian
hess = anlp.evaluate_hessian_lag()
dense_hess = hess.todense()
expected_hess = [[2.0 * i * j for j in range(1, 10)] for i in range(1, 10)]
expected_hess = np.asarray(expected_hess, dtype=np.float64)
self.assertTrue(np.array_equal(dense_hess, expected_hess))
# test inplace
hess.data = np.zeros(len(hess.data))
ret = anlp.evaluate_hessian_lag(out=hess)
self.assertTrue(ret is hess)
dense_hess = hess.todense()
self.assertTrue(np.array_equal(dense_hess, expected_hess))
# change the value of the primals
anlp.set_primals(2.0 * np.ones(9))
anlp.evaluate_hessian_lag(out=hess)
dense_hess = hess.todense()
self.assertTrue(np.array_equal(dense_hess, expected_hess))
# change the value of the obj factor
anlp.set_obj_factor(2.0)
hess = anlp.evaluate_hessian_lag()
dense_hess = hess.todense()
expected_hess = [[4.0 * i * j for j in range(1, 10)] for i in range(1, 10)]
expected_hess = np.asarray(expected_hess, dtype=np.float64)
self.assertTrue(np.array_equal(dense_hess, expected_hess))
def evaluate_outputs(self):
P1 = self._Pin - self._c1*self._F**2
P2 = P1 - self._c2*self._F**2
return np.asarray([P1, P2], dtype=np.float64)
def test_model1(self):
model = create_model1()
nlp = PyomoNLP(model)
cynlp = CyIpoptNLP(nlp)
# test x_init
expected_xinit = np.asarray([4.0, 4.0, 4.0], dtype=np.float64)
xinit = cynlp.x_init()
self.assertTrue(np.array_equal(xinit, expected_xinit))
# test x_lb
expected_xlb = list()
for v in nlp.get_pyomo_variables():
if v.lb == None:
expected_xlb.append(-np.inf)
else:
expected_xlb.append(v.lb)
expected_xlb = np.asarray(expected_xlb)
xlb = cynlp.x_lb()
self.assertTrue(np.array_equal(xlb, expected_xlb))
# test x_ub
expected_xub = list()
for v in nlp.get_pyomo_variables():
if v.ub == None:
expected_xub.append(np.inf)
else:
expected_xub.append(v.ub)
expected_xub = np.asarray(expected_xub)
xub = cynlp.x_ub()
self.assertTrue(np.array_equal(xub, expected_xub))
# test g_lb
expected_glb = np.asarray([-np.inf, 0.0], dtype=np.float64)
glb = cynlp.g_lb()
self.assertTrue(np.array_equal(glb, expected_glb))
# test g_ub
expected_gub = np.asarray([18, 0.0], dtype=np.float64)
gub = cynlp.g_ub()
print(expected_gub)
print(gub)
self.assertTrue(np.array_equal(gub, expected_gub))
x = cynlp.x_init()
# test objective
self.assertEqual(cynlp.objective(x), -504)
# test gradient
expected = np.asarray([-576, 8, 64], dtype=np.float64)
self.assertTrue(np.allclose(expected, cynlp.gradient(x)))
# test constraints
expected = np.asarray([20, -5], dtype=np.float64)
constraints = cynlp.constraints(x)
self.assertTrue(np.allclose(expected, constraints))
# test jacobian
expected = np.asarray([[8.0, 0, 1.0],[0.0, 8.0, 1.0]])
spexpected = spa.coo_matrix(expected).todense()
rows, cols = cynlp.jacobianstructure()
values = cynlp.jacobian(x)
jac = spa.coo_matrix((values, (rows,cols)), shape=(len(constraints), len(x))).todense()
self.assertTrue(np.allclose(spexpected, jac))
# test hessian
y = constraints.copy()
y.fill(1.0)
rows, cols = cynlp.hessianstructure()
values = cynlp.hessian(x, y, obj_factor=1.0)
hess_lower = spa.coo_matrix((values, (rows,cols)), shape=(len(x), len(x))).todense()
expected_hess_lower = np.asarray([[-286.0, 0.0, 0.0], [0.0, 4.0, 0.0], [-144.0, 0.0, 192.0]], dtype=np.float64)
self.assertTrue(np.allclose(expected_hess_lower, hess_lower))
def test_indices_methods(self):
nlp = PyomoNLP(self.pm)
# get_pyomo_variables
variables = nlp.get_pyomo_variables()
expected_ids = [id(self.pm.x[i]) for i in range(1, 10)]
ids = [id(variables[i]) for i in range(9)]
self.assertTrue(expected_ids == ids)
variable_names = nlp.variable_names()
expected_names = [self.pm.x[i].getname() for i in range(1, 10)]
self.assertTrue(variable_names == expected_names)
# get_pyomo_constraints
constraints = nlp.get_pyomo_constraints()
expected_ids = [id(self.pm.c[i]) for i in range(1, 10)]
ids = [id(constraints[i]) for i in range(9)]
self.assertTrue(expected_ids == ids)
constraint_names = nlp.constraint_names()
expected_names = [c.getname() for c in nlp.get_pyomo_constraints()]
self.assertTrue(constraint_names == expected_names)
# get_pyomo_equality_constraints
eq_constraints = nlp.get_pyomo_equality_constraints()
# 2 and 6 are the equality constraints
eq_indices = [2, 6] # "indices" here is a bit overloaded
expected_eq_ids = [id(self.pm.c[i]) for i in eq_indices]
eq_ids = [id(con) for con in eq_constraints]
self.assertEqual(eq_ids, expected_eq_ids)
eq_constraint_names = nlp.equality_constraint_names()
expected_eq_names = [
c.getname(fully_qualified=True)
for c in nlp.get_pyomo_equality_constraints()
]
self.assertEqual(eq_constraint_names, expected_eq_names)
# get_pyomo_inequality_constraints
ineq_constraints = nlp.get_pyomo_inequality_constraints()
# 1, 3, 4, 5, 7, 8, and 9 are the inequality constraints
ineq_indices = [1, 3, 4, 5, 7, 8, 9]
expected_ineq_ids = [id(self.pm.c[i]) for i in ineq_indices]
ineq_ids = [id(con) for con in ineq_constraints]
self.assertEqual(eq_ids, expected_eq_ids)
# get_primal_indices
expected_primal_indices = [i for i in range(9)]
self.assertTrue(
expected_primal_indices == nlp.get_primal_indices([self.pm.x]))
expected_primal_indices = [0, 3, 8, 4]
variables = [self.pm.x[1], self.pm.x[4], self.pm.x[9], self.pm.x[5]]
self.assertTrue(
expected_primal_indices == nlp.get_primal_indices(variables))
# get_constraint_indices
expected_constraint_indices = [i for i in range(9)]
self.assertTrue(expected_constraint_indices ==
nlp.get_constraint_indices([self.pm.c]))
expected_constraint_indices = [0, 3, 8, 4]
constraints = [self.pm.c[1], self.pm.c[4], self.pm.c[9], self.pm.c[5]]
self.assertTrue(expected_constraint_indices ==
nlp.get_constraint_indices(constraints))
# get_equality_constraint_indices
pyomo_eq_indices = [2, 6]
with self.assertRaises(KeyError):
# At least one data object in container is not an equality
nlp.get_equality_constraint_indices([self.pm.c])
eq_constraints = [self.pm.c[i] for i in pyomo_eq_indices]
expected_eq_indices = [0, 1]
# ^indices in the list of equality constraints
eq_constraint_indices = nlp.get_equality_constraint_indices(
eq_constraints)
self.assertEqual(expected_eq_indices, eq_constraint_indices)
# get_inequality_constraint_indices
pyomo_ineq_indices = [1, 3, 4, 5, 7, 9]
with self.assertRaises(KeyError):
# At least one data object in container is not an equality
nlp.get_inequality_constraint_indices([self.pm.c])
ineq_constraints = [self.pm.c[i] for i in pyomo_ineq_indices]
expected_ineq_indices = [0, 1, 2, 3, 4, 6]
# ^indices in the list of equality constraints; didn't include 8
ineq_constraint_indices = nlp.get_inequality_constraint_indices(
ineq_constraints)
self.assertEqual(expected_ineq_indices, ineq_constraint_indices)
# extract_subvector_grad_objective
expected_gradient = np.asarray(
[2 * sum((i + 1) * (j + 1) for j in range(9)) for i in range(9)],
dtype=np.float64)
grad_obj = nlp.extract_subvector_grad_objective([self.pm.x])
self.assertTrue(np.array_equal(expected_gradient, grad_obj))
expected_gradient = np.asarray([
2 * sum((i + 1) * (j + 1) for j in range(9)) for i in [0, 3, 8, 4]
],
dtype=np.float64)
variables = [self.pm.x[1], self.pm.x[4], self.pm.x[9], self.pm.x[5]]
grad_obj = nlp.extract_subvector_grad_objective(variables)
self.assertTrue(np.array_equal(expected_gradient, grad_obj))
# extract_subvector_constraints
expected_con = np.asarray(
[45, 88, 3 * 45, 4 * 45, 5 * 45, 276, 7 * 45, 8 * 45, 9 * 45],
dtype=np.float64)
con = nlp.extract_subvector_constraints([self.pm.c])
self.assertTrue(np.array_equal(expected_con, con))
expected_con = np.asarray([45, 4 * 45, 9 * 45, 5 * 45],
dtype=np.float64)
constraints = [self.pm.c[1], self.pm.c[4], self.pm.c[9], self.pm.c[5]]
con = nlp.extract_subvector_constraints(constraints)
self.assertTrue(np.array_equal(expected_con, con))
# extract_submatrix_jacobian
expected_jac = [[(i) * (j) for j in range(1, 10)]
for i in range(1, 10)]
expected_jac = np.asarray(expected_jac, dtype=np.float64)
jac = nlp.extract_submatrix_jacobian(pyomo_variables=[self.pm.x],
pyomo_constraints=[self.pm.c])
dense_jac = jac.todense()
self.assertTrue(np.array_equal(dense_jac, expected_jac))
expected_jac = [[(i) * (j) for j in [1, 4, 9, 5]] for i in [2, 6, 4]]
expected_jac = np.asarray(expected_jac, dtype=np.float64)
variables = [self.pm.x[1], self.pm.x[4], self.pm.x[9], self.pm.x[5]]
constraints = [self.pm.c[2], self.pm.c[6], self.pm.c[4]]
jac = nlp.extract_submatrix_jacobian(pyomo_variables=variables,
pyomo_constraints=constraints)
dense_jac = jac.todense()
self.assertTrue(np.array_equal(dense_jac, expected_jac))
# extract_submatrix_hessian_lag
expected_hess = [[2.0 * i * j for j in range(1, 10)]
for i in range(1, 10)]
expected_hess = np.asarray(expected_hess, dtype=np.float64)
hess = nlp.extract_submatrix_hessian_lag(
pyomo_variables_rows=[self.pm.x], pyomo_variables_cols=[self.pm.x])
dense_hess = hess.todense()
self.assertTrue(np.array_equal(dense_hess, expected_hess))
expected_hess = [[2.0 * i * j for j in [1, 4, 9, 5]]
for i in [1, 4, 9, 5]]
expected_hess = np.asarray(expected_hess, dtype=np.float64)
variables = [self.pm.x[1], self.pm.x[4], self.pm.x[9], self.pm.x[5]]
hess = nlp.extract_submatrix_hessian_lag(
pyomo_variables_rows=variables, pyomo_variables_cols=variables)
dense_hess = hess.todense()
self.assertTrue(np.array_equal(dense_hess, expected_hess))
def test_pyomo_external_model_ndarray_scaling(self):
m = pyo.ConcreteModel()
m.Pin = pyo.Var(initialize=100, bounds=(0, None))
m.c1 = pyo.Var(initialize=1.0, bounds=(0, None))
m.c2 = pyo.Var(initialize=1.0, bounds=(0, None))
m.F = pyo.Var(initialize=10, bounds=(0, None))
m.P1 = pyo.Var()
m.P2 = pyo.Var()
m.F_con = pyo.Constraint(expr=m.F == 10)
m.Pin_con = pyo.Constraint(expr=m.Pin == 100)
# simple parameter estimation test
m.obj = pyo.Objective(expr=(m.P1 - 90)**2 + (m.P2 - 40)**2)
# set scaling parameters for the pyomo variables and constraints
m.scaling_factor = pyo.Suffix(direction=pyo.Suffix.EXPORT)
m.scaling_factor[m.obj] = 0.1 # scale the objective
m.scaling_factor[m.Pin] = 2.0 # scale the variable
m.scaling_factor[m.c1] = 3.0 # scale the variable
m.scaling_factor[m.c2] = 4.0 # scale the variable
m.scaling_factor[m.F] = 5.0 # scale the variable
m.scaling_factor[m.P1] = 6.0 # scale the variable
m.scaling_factor[m.P2] = 7.0 # scale the variable
m.scaling_factor[m.F_con] = 8.0 # scale the pyomo constraint
m.scaling_factor[m.Pin_con] = 9.0 # scale the pyomo constraint
# test that this all works with ndarray input as well
cyipopt_problem = \
PyomoExternalCyIpoptProblem(pyomo_model=m,
ex_input_output_model=PressureDropModel(),
inputs=[m.Pin, m.c1, m.c2, m.F],
outputs=[m.P1, m.P2],
outputs_eqn_scaling=np.asarray([10.0, 11.0], dtype=np.float64)
)
# solve the problem
options = {
'hessian_approximation': 'limited-memory',
'nlp_scaling_method': 'user-scaling',
'output_file': '_cyipopt-pyomo-ext-scaling-ndarray.log',
'file_print_level': 10,
'max_iter': 0
}
solver = CyIpoptSolver(cyipopt_problem, options=options)
x, info = solver.solve(tee=False)
with open('_cyipopt-pyomo-ext-scaling-ndarray.log', 'r') as fd:
solver_trace = fd.read()
os.remove('_cyipopt-pyomo-ext-scaling-ndarray.log')
self.assertIn('nlp_scaling_method = user-scaling', solver_trace)
self.assertIn('output_file = _cyipopt-pyomo-ext-scaling-ndarray.log',
solver_trace)
self.assertIn('objective scaling factor = 0.1', solver_trace)
self.assertIn('x scaling provided', solver_trace)
self.assertIn('c scaling provided', solver_trace)
self.assertIn('d scaling provided', solver_trace)
self.assertIn('DenseVector "x scaling vector" with 7 elements:',
solver_trace)
self.assertIn('x scaling vector[ 1]= 6.0000000000000000e+00',
solver_trace)
self.assertIn('x scaling vector[ 2]= 7.0000000000000000e+00',
solver_trace)
self.assertIn('x scaling vector[ 3]= 2.0000000000000000e+00',
solver_trace)
self.assertIn('x scaling vector[ 4]= 3.0000000000000000e+00',
solver_trace)
self.assertIn('x scaling vector[ 5]= 4.0000000000000000e+00',
solver_trace)
self.assertIn('x scaling vector[ 6]= 5.0000000000000000e+00',
solver_trace)
self.assertIn('x scaling vector[ 7]= 1.0000000000000000e+00',
solver_trace)
self.assertIn('DenseVector "c scaling vector" with 5 elements:',
solver_trace)
self.assertIn('c scaling vector[ 1]= 8.0000000000000000e+00',
solver_trace)
self.assertIn('c scaling vector[ 2]= 9.0000000000000000e+00',
solver_trace)
self.assertIn('c scaling vector[ 3]= 1.0000000000000000e+00',
solver_trace)
self.assertIn('c scaling vector[ 4]= 1.0000000000000000e+01',
solver_trace)
self.assertIn('c scaling vector[ 5]= 1.1000000000000000e+01',
solver_trace)
def test_projected(self):
m = create_pyomo_model()
nlp = PyomoNLP(m)
projected_nlp = ProjectedNLP(nlp, ['x[0]', 'x[1]', 'x[2]'])
expected_names = ['x[0]', 'x[1]', 'x[2]']
self.assertEqual(projected_nlp.primals_names(), expected_names)
self.assertTrue(
np.array_equal(projected_nlp.get_primals(),
np.asarray([1.0, 2.0, 4.0])))
self.assertTrue(
np.array_equal(projected_nlp.evaluate_grad_objective(),
np.asarray([8.0, 1.0, 9.0])))
self.assertEqual(projected_nlp.nnz_jacobian(), 5)
self.assertEqual(projected_nlp.nnz_hessian_lag(), 6)
J = projected_nlp.evaluate_jacobian()
self.assertEqual(len(J.data), 5)
denseJ = J.todense()
expected_jac = np.asarray([[6.0, 1.0, 1.0], [1.0, 0.0, 1.0]])
self.assertTrue(np.array_equal(denseJ, expected_jac))
# test the use of "out"
J = 0.0 * J
projected_nlp.evaluate_jacobian(out=J)
denseJ = J.todense()
self.assertTrue(np.array_equal(denseJ, expected_jac))
H = projected_nlp.evaluate_hessian_lag()
self.assertEqual(len(H.data), 6)
expectedH = np.asarray([[2.0, 1.0, 1.0], [1.0, 0.0, 0.0],
[1.0, 0.0, 2.0]])
denseH = H.todense()
self.assertTrue(np.array_equal(denseH, expectedH))
# test the use of "out"
H = 0.0 * H
projected_nlp.evaluate_hessian_lag(out=H)
denseH = H.todense()
self.assertTrue(np.array_equal(denseH, expectedH))
# now test a reordering
projected_nlp = ProjectedNLP(nlp, ['x[0]', 'x[2]', 'x[1]'])
expected_names = ['x[0]', 'x[2]', 'x[1]']
self.assertEqual(projected_nlp.primals_names(), expected_names)
self.assertTrue(
np.array_equal(projected_nlp.get_primals(),
np.asarray([1.0, 4.0, 2.0])))
self.assertTrue(
np.array_equal(projected_nlp.evaluate_grad_objective(),
np.asarray([8.0, 9.0, 1.0])))
self.assertEqual(projected_nlp.nnz_jacobian(), 5)
self.assertEqual(projected_nlp.nnz_hessian_lag(), 6)
J = projected_nlp.evaluate_jacobian()
self.assertEqual(len(J.data), 5)
denseJ = J.todense()
expected_jac = np.asarray([[6.0, 1.0, 1.0], [1.0, 1.0, 0.0]])
self.assertTrue(np.array_equal(denseJ, expected_jac))
# test the use of "out"
J = 0.0 * J
projected_nlp.evaluate_jacobian(out=J)
denseJ = J.todense()
self.assertTrue(np.array_equal(denseJ, expected_jac))
H = projected_nlp.evaluate_hessian_lag()
self.assertEqual(len(H.data), 6)
expectedH = np.asarray([[2.0, 1.0, 1.0], [1.0, 2.0, 0.0],
[1.0, 0.0, 0.0]])
denseH = H.todense()
self.assertTrue(np.array_equal(denseH, expectedH))
# test the use of "out"
H = 0.0 * H
projected_nlp.evaluate_hessian_lag(out=H)
denseH = H.todense()
self.assertTrue(np.array_equal(denseH, expectedH))
# now test an expansion
projected_nlp = ProjectedNLP(nlp, ['x[0]', 'x[2]', 'y', 'x[1]'])
expected_names = ['x[0]', 'x[2]', 'y', 'x[1]']
self.assertEqual(projected_nlp.primals_names(), expected_names)
np.testing.assert_equal(projected_nlp.get_primals(),
np.asarray([1.0, 4.0, np.nan, 2.0]))
self.assertTrue(
np.array_equal(projected_nlp.evaluate_grad_objective(),
np.asarray([8.0, 9.0, 0.0, 1.0])))
self.assertEqual(projected_nlp.nnz_jacobian(), 5)
self.assertEqual(projected_nlp.nnz_hessian_lag(), 6)
J = projected_nlp.evaluate_jacobian()
self.assertEqual(len(J.data), 5)
denseJ = J.todense()
expected_jac = np.asarray([[6.0, 1.0, 0.0, 1.0], [1.0, 1.0, 0.0, 0.0]])
self.assertTrue(np.array_equal(denseJ, expected_jac))
# test the use of "out"
J = 0.0 * J
projected_nlp.evaluate_jacobian(out=J)
denseJ = J.todense()
self.assertTrue(np.array_equal(denseJ, expected_jac))
H = projected_nlp.evaluate_hessian_lag()
self.assertEqual(len(H.data), 6)
expectedH = np.asarray([[2.0, 1.0, 0.0, 1.0], [1.0, 2.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0]])
denseH = H.todense()
self.assertTrue(np.array_equal(denseH, expectedH))
# test the use of "out"
H = 0.0 * H
projected_nlp.evaluate_hessian_lag(out=H)
denseH = H.todense()
self.assertTrue(np.array_equal(denseH, expectedH))
# now test an expansion
projected_nlp = ProjectedNLP(nlp, ['x[0]', 'x[2]'])
expected_names = ['x[0]', 'x[2]']
self.assertEqual(projected_nlp.primals_names(), expected_names)
np.testing.assert_equal(projected_nlp.get_primals(),
np.asarray([1.0, 4.0]))
self.assertTrue(
np.array_equal(projected_nlp.evaluate_grad_objective(),
np.asarray([8.0, 9.0])))
self.assertEqual(projected_nlp.nnz_jacobian(), 4)
self.assertEqual(projected_nlp.nnz_hessian_lag(), 4)
J = projected_nlp.evaluate_jacobian()
self.assertEqual(len(J.data), 4)
denseJ = J.todense()
expected_jac = np.asarray([[6.0, 1.0], [1.0, 1.0]])
self.assertTrue(np.array_equal(denseJ, expected_jac))
# test the use of "out"
J = 0.0 * J
projected_nlp.evaluate_jacobian(out=J)
denseJ = J.todense()
self.assertTrue(np.array_equal(denseJ, expected_jac))
H = projected_nlp.evaluate_hessian_lag()
self.assertEqual(len(H.data), 4)
expectedH = np.asarray([[2.0, 1.0], [1.0, 2.0]])
denseH = H.todense()
self.assertTrue(np.array_equal(denseH, expectedH))
# test the use of "out"
H = 0.0 * H
projected_nlp.evaluate_hessian_lag(out=H)
denseH = H.todense()
self.assertTrue(np.array_equal(denseH, expectedH))
def evaluate_hessian_equality_constraints(self):
irow = np.asarray([0], dtype=np.int64)
jcol = np.asarray([0], dtype=np.int64)
data = np.asarray([self._equality_mult * 2.0], dtype=np.float64)
hess = spa.coo_matrix((data, (irow, jcol)), shape=(1, 1))
return hess
def evaluate_hessian_outputs(self):
irow = np.asarray([], dtype=np.int64)
jcol = np.asarray([], dtype=np.int64)
data = np.asarray([], dtype=np.float64)
hess = spa.coo_matrix((data, (irow, jcol)), shape=(1, 1))
return hess
def evaluate_jacobian_outputs(self):
irow = np.asarray([0], dtype=np.int64)
jcol = np.asarray([0], dtype=np.int64)
nonzeros = np.asarray([5.0], dtype=np.float64)
jac = spa.coo_matrix((nonzeros, (irow, jcol)), shape=(1, 1))
return jac
def evaluate_jacobian_equality_constraints(self):
irow = np.asarray([0], dtype=np.int64)
jcol = np.asarray([0], dtype=np.int64)
nonzeros = np.asarray([2 * self._u], dtype=np.float64)
jac = spa.coo_matrix((nonzeros, (irow, jcol)), shape=(1, 1))
return jac
def evaluate_outputs(self):
return np.asarray([5 * self._u])
def get_equality_constraint_scaling_factors(self):
return np.asarray([3.1, 3.2], dtype=np.float64)
def get_output_constraint_scaling_factors(self):
return np.asarray([4.1, 4.2])
