def test_feature_boundscheck_basic(self):
import numpy as np
from openmdao.api import Problem, Group, IndepVarComp, NewtonSolver, ScipyIterativeSolver, BoundsEnforceLS
from openmdao.test_suite.components.implicit_newton_linesearch import ImplCompTwoStatesArrays
top = Problem()
top.model = Group()
top.model.add_subsystem('px', IndepVarComp('x', np.ones((3, 1))))
top.model.add_subsystem('comp', ImplCompTwoStatesArrays())
top.model.connect('px.x', 'comp.x')
top.model.nonlinear_solver = NewtonSolver()
top.model.nonlinear_solver.options['maxiter'] = 10
top.model.linear_solver = ScipyIterativeSolver()
top.model.nonlinear_solver.linesearch = BoundsEnforceLS()
top.setup(check=False)
# Test lower bounds: should go to the lower bound and stall
top['px.x'] = 2.0
top['comp.y'] = 0.
top['comp.z'] = 1.6
top.run_model()
assert_rel_error(self, top['comp.z'][0], [1.5], 1e-8)
assert_rel_error(self, top['comp.z'][1], [1.5], 1e-8)
assert_rel_error(self, top['comp.z'][2], [1.5], 1e-8)
def test_feature_boundscheck_wall(self):
import numpy as np
import openmdao.api as om
from openmdao.test_suite.components.implicit_newton_linesearch import ImplCompTwoStatesArrays
top = om.Problem()
top.model.add_subsystem('comp',
ImplCompTwoStatesArrays(),
promotes_inputs=['x'])
top.model.nonlinear_solver = om.NewtonSolver(solve_subsystems=False)
top.model.nonlinear_solver.options['maxiter'] = 10
top.model.linear_solver = om.ScipyKrylov()
top.model.nonlinear_solver.linesearch = om.BoundsEnforceLS(
bound_enforcement='wall')
top.setup()
top.set_val('x', np.array([0.5, 0.5, 0.5]).reshape(3, 1))
# Test upper bounds: should go to the upper bound and stall
top.set_val('comp.y', 0.)
top.set_val('comp.z', 2.4)
top.run_model()
assert_near_equal(top.get_val('comp.z', indices=0), [2.6], 1e-8)
assert_near_equal(top.get_val('comp.z', indices=1), [2.5], 1e-8)
assert_near_equal(top.get_val('comp.z', indices=2), [2.65], 1e-8)
def test_feature_armijo_boundscheck_scalar(self):
import numpy as np
from openmdao.api import Problem, Group, IndepVarComp, NewtonSolver, ScipyKrylov, ArmijoGoldsteinLS
from openmdao.test_suite.components.implicit_newton_linesearch import ImplCompTwoStatesArrays
top = Problem()
top.model = Group()
top.model.add_subsystem('px', IndepVarComp('x', np.ones((3, 1))))
top.model.add_subsystem('comp', ImplCompTwoStatesArrays())
top.model.connect('px.x', 'comp.x')
top.model.nonlinear_solver = NewtonSolver()
top.model.nonlinear_solver.options['maxiter'] = 10
top.model.linear_solver = ScipyKrylov()
ls = top.model.nonlinear_solver.linesearch = ArmijoGoldsteinLS(
bound_enforcement='scalar')
ls.options['bound_enforcement'] = 'scalar'
top.setup(check=False)
top.run_model()
# Test lower bounds: should stop just short of the lower bound
top['px.x'] = 2.0
top['comp.y'] = 0.
top['comp.z'] = 1.6
top.run_model()
print(top['comp.z'][0])
print(top['comp.z'][1])
print(top['comp.z'][2])
def test_feature_armijo_boundscheck_wall(self):
import numpy as np
from openmdao.api import Problem, Group, IndepVarComp, NewtonSolver, ScipyKrylov, ArmijoGoldsteinLS
from openmdao.test_suite.components.implicit_newton_linesearch import ImplCompTwoStatesArrays
top = Problem()
top.model = Group()
top.model.add_subsystem('px', IndepVarComp('x', np.ones((3, 1))))
top.model.add_subsystem('comp', ImplCompTwoStatesArrays())
top.model.connect('px.x', 'comp.x')
top.model.nonlinear_solver = NewtonSolver()
top.model.nonlinear_solver.options['maxiter'] = 10
top.model.linear_solver = ScipyKrylov()
ls = top.model.nonlinear_solver.linesearch = ArmijoGoldsteinLS(
bound_enforcement='wall')
ls.options['bound_enforcement'] = 'wall'
top.setup(check=False)
# Test upper bounds: should go to the upper bound and stall
top['px.x'] = 0.5
top['comp.y'] = 0.
top['comp.z'] = 2.4
top.run_model()
assert_rel_error(self, top['comp.z'][0], [2.6], 1e-8)
assert_rel_error(self, top['comp.z'][1], [2.5], 1e-8)
assert_rel_error(self, top['comp.z'][2], [2.65], 1e-8)
def test_feature_boundscheck_wall(self):
top = Problem()
top.model = Group()
top.model.add_subsystem('px', IndepVarComp('x', np.ones((3, 1))))
top.model.add_subsystem('comp', ImplCompTwoStatesArrays())
top.model.connect('px.x', 'comp.x')
top.model.nonlinear_solver = NewtonSolver()
top.model.nonlinear_solver.options['maxiter'] = 10
top.model.linear_solver = ScipyIterativeSolver()
ls = top.model.nonlinear_solver.linesearch = BoundsEnforceLS()
ls.options['bound_enforcement'] = 'wall'
top.setup(check=False)
# Test upper bounds: should go to the upper bound and stall
top['px.x'] = 0.5
top['comp.y'] = 0.
top['comp.z'] = 2.4
top.run_model()
assert_rel_error(self, top['comp.z'][0], [2.6], 1e-8)
assert_rel_error(self, top['comp.z'][1], [2.5], 1e-8)
assert_rel_error(self, top['comp.z'][2], [2.65], 1e-8)
def test_feature_armijo_boundscheck_vector(self):
import numpy as np
import openmdao.api as om
from openmdao.test_suite.components.implicit_newton_linesearch import ImplCompTwoStatesArrays
top = om.Problem()
top.model.add_subsystem('px', om.IndepVarComp('x', np.ones((3, 1))))
top.model.add_subsystem('comp', ImplCompTwoStatesArrays())
top.model.connect('px.x', 'comp.x')
top.model.nonlinear_solver = om.NewtonSolver()
top.model.nonlinear_solver.options['maxiter'] = 10
top.model.linear_solver = om.ScipyKrylov()
top.model.nonlinear_solver.linesearch = om.ArmijoGoldsteinLS(
bound_enforcement='vector')
top.setup()
# Test lower bounds: should go to the lower bound and stall
top['px.x'] = 2.0
top['comp.y'] = 0.
top['comp.z'] = 1.6
top.run_model()
for ind in range(3):
assert_rel_error(self, top['comp.z'][ind], [1.5], 1e-8)
def test_feature_print_bound_enforce(self):
import numpy as np
from openmdao.api import Problem, Group, IndepVarComp, NewtonSolver, ScipyKrylov, BoundsEnforceLS
from openmdao.test_suite.components.implicit_newton_linesearch import ImplCompTwoStatesArrays
top = Problem()
top.model = Group()
top.model.add_subsystem('px', IndepVarComp('x', np.ones((3, 1))))
top.model.add_subsystem('comp', ImplCompTwoStatesArrays())
top.model.connect('px.x', 'comp.x')
newt = top.model.nonlinear_solver = NewtonSolver()
top.model.nonlinear_solver.options['maxiter'] = 2
top.model.linear_solver = ScipyKrylov()
ls = newt.linesearch = BoundsEnforceLS(bound_enforcement='vector')
ls.options['print_bound_enforce'] = True
top.set_solver_print(level=2)
top.setup()
# Test lower bounds: should go to the lower bound and stall
top['px.x'] = 2.0
top['comp.y'] = 0.
top['comp.z'] = 1.6
top.run_model()
assert_rel_error(self, top['comp.z'][0], [1.5], 1e-8)
assert_rel_error(self, top['comp.z'][1], [1.5], 1e-8)
assert_rel_error(self, top['comp.z'][2], [1.5], 1e-8)
def test_feature_boundscheck_wall(self):
import numpy as np
import openmdao.api as om
from openmdao.test_suite.components.implicit_newton_linesearch import ImplCompTwoStatesArrays
top = om.Problem()
top.model.add_subsystem('px', om.IndepVarComp('x', np.ones((3, 1))))
top.model.add_subsystem('comp', ImplCompTwoStatesArrays())
top.model.connect('px.x', 'comp.x')
top.model.nonlinear_solver = om.NewtonSolver()
top.model.nonlinear_solver.options['maxiter'] = 10
top.model.linear_solver = om.ScipyKrylov()
top.model.nonlinear_solver.linesearch = om.BoundsEnforceLS(
bound_enforcement='wall')
top.setup()
# Test upper bounds: should go to the upper bound and stall
top['px.x'] = 0.5
top['comp.y'] = 0.
top['comp.z'] = 2.4
top.run_model()
assert_rel_error(self, top['comp.z'][0], [2.6], 1e-8)
assert_rel_error(self, top['comp.z'][1], [2.5], 1e-8)
assert_rel_error(self, top['comp.z'][2], [2.65], 1e-8)
def test_feature_boundscheck_scalar(self):
import numpy as np
import openmdao.api as om
from openmdao.test_suite.components.implicit_newton_linesearch import ImplCompTwoStatesArrays
top = om.Problem()
top.model.add_subsystem('px', om.IndepVarComp('x', np.ones((3, 1))))
top.model.add_subsystem('comp', ImplCompTwoStatesArrays())
top.model.connect('px.x', 'comp.x')
top.model.nonlinear_solver = om.NewtonSolver()
top.model.nonlinear_solver.options['maxiter'] = 10
top.model.linear_solver = om.ScipyKrylov()
top.model.nonlinear_solver.linesearch = om.BoundsEnforceLS(
bound_enforcement='scalar')
top.setup()
top.run_model()
# Test lower bounds: should stop just short of the lower bound
top['px.x'] = 2.0
top['comp.y'] = 0.
top['comp.z'] = 1.6
top.run_model()
def test_feature_boundscheck_scalar(self):
import numpy as np
import openmdao.api as om
from openmdao.test_suite.components.implicit_newton_linesearch import ImplCompTwoStatesArrays
top = om.Problem()
top.model.add_subsystem('comp',
ImplCompTwoStatesArrays(),
promotes_inputs=['x'])
top.model.nonlinear_solver = om.NewtonSolver(solve_subsystems=False)
top.model.nonlinear_solver.options['maxiter'] = 10
top.model.linear_solver = om.ScipyKrylov()
top.model.nonlinear_solver.linesearch = om.BoundsEnforceLS(
bound_enforcement='scalar')
top.setup()
top.set_val('x', np.array([2., 2, 2]).reshape(3, 1))
top.run_model()
# Test lower bounds: should stop just short of the lower bound
top.set_val('comp.y', 0.)
top.set_val('comp.z', 1.6)
top.run_model()
def test_feature_boundscheck_basic(self):
import numpy as np
import openmdao.api as om
from openmdao.test_suite.components.implicit_newton_linesearch import ImplCompTwoStatesArrays
top = om.Problem()
top.model.add_subsystem('comp',
ImplCompTwoStatesArrays(),
promotes_inputs=['x'])
top.model.nonlinear_solver = om.NewtonSolver(solve_subsystems=False)
top.model.nonlinear_solver.options['maxiter'] = 10
top.model.linear_solver = om.ScipyKrylov()
top.model.nonlinear_solver.linesearch = om.BoundsEnforceLS()
top.setup()
top.set_val('x', np.array([2., 2, 2]).reshape(3, 1))
# Test lower bounds: should go to the lower bound and stall
top.set_val('comp.y', 0.)
top.set_val('comp.z', 1.6)
top.run_model()
for ind in range(3):
assert_near_equal(top.get_val('comp.z', indices=ind), [1.5], 1e-8)
def test_feature_boundscheck_scalar(self):
top = Problem()
top.model = Group()
top.model.add_subsystem('px', IndepVarComp('x', np.ones((3, 1))))
top.model.add_subsystem('comp', ImplCompTwoStatesArrays())
top.model.connect('px.x', 'comp.x')
top.model.nonlinear_solver = NewtonSolver()
top.model.nonlinear_solver.options['maxiter'] = 10
top.model.linear_solver = ScipyIterativeSolver()
ls = top.model.nonlinear_solver.linesearch = BoundsEnforceLS()
ls.options['bound_enforcement'] = 'scalar'
top.setup(check=False)
top.run_model()
# Test lower bounds: should stop just short of the lower bound
top['px.x'] = 2.0
top['comp.y'] = 0.
top['comp.z'] = 1.6
top.run_model()
print(top['comp.z'][0])
print(top['comp.z'][1])
print(top['comp.z'][2])
def test_feature_armijo_print_bound_enforce(self):
import numpy as np
import openmdao.api as om
from openmdao.test_suite.components.implicit_newton_linesearch import ImplCompTwoStatesArrays
top = om.Problem()
top.model.add_subsystem('px', om.IndepVarComp('x', np.ones((3, 1))))
top.model.add_subsystem('comp', ImplCompTwoStatesArrays())
top.model.connect('px.x', 'comp.x')
newt = top.model.nonlinear_solver = om.NewtonSolver(
solve_subsystems=False)
top.model.nonlinear_solver.options['maxiter'] = 2
top.model.linear_solver = om.ScipyKrylov()
ls = newt.linesearch = om.ArmijoGoldsteinLS()
ls.options['print_bound_enforce'] = True
top.set_solver_print(level=2)
top.setup()
# Test lower bounds: should go to the lower bound and stall
top['px.x'] = 2.0
top['comp.y'] = 0.
top['comp.z'] = 1.6
top.run_model()
for ind in range(3):
assert_near_equal(top['comp.z'][ind], [1.5], 1e-8)
def test_feature_print_bound_enforce(self):
top = om.Problem()
top.model.add_subsystem('comp',
ImplCompTwoStatesArrays(),
promotes_inputs=['x'])
newt = top.model.nonlinear_solver = om.NewtonSolver(
solve_subsystems=False)
top.model.nonlinear_solver.options['maxiter'] = 2
top.model.linear_solver = om.ScipyKrylov()
ls = newt.linesearch = om.BoundsEnforceLS(bound_enforcement='vector')
ls.options['print_bound_enforce'] = True
top.set_solver_print(level=2)
top.setup()
top.set_val('x', np.array([2., 2, 2]).reshape(3, 1))
# Test lower bounds: should go to the lower bound and stall
top.set_val('comp.y', 0.)
top.set_val('comp.z', 1.6)
top.run_model()
for ind in range(3):
assert_near_equal(top.get_val('comp.z', indices=ind), [1.5], 1e-8)
def test_feature_goldstein(self):
top = om.Problem()
top.model.add_subsystem('px', om.IndepVarComp('x', np.ones((3, 1))))
top.model.add_subsystem('comp', ImplCompTwoStatesArrays())
top.model.connect('px.x', 'comp.x')
top.model.nonlinear_solver = om.NewtonSolver(solve_subsystems=False)
top.model.nonlinear_solver.options['maxiter'] = 10
top.model.linear_solver = om.ScipyKrylov()
ls = top.model.nonlinear_solver.linesearch = om.ArmijoGoldsteinLS(
bound_enforcement='vector')
ls.options['method'] = 'Goldstein'
top.setup()
# Test lower bounds: should go to the lower bound and stall
top['px.x'] = 2.0
top['comp.y'] = 0.
top['comp.z'] = 1.6
top.run_model()
for ind in range(3):
assert_near_equal(top['comp.z'][ind], [1.5], 1e-8)
def setUp(self):
top = om.Problem()
top.model.add_subsystem('px', om.IndepVarComp('x', np.ones((3, 1))))
top.model.add_subsystem('comp', ImplCompTwoStatesArrays())
top.model.connect('px.x', 'comp.x')
top.model.nonlinear_solver = om.NewtonSolver()
top.model.nonlinear_solver.options['maxiter'] = 10
top.model.linear_solver = om.ScipyKrylov()
top.set_solver_print(level=0)
top.setup()
self.top = top
self.ub = np.array([2.6, 2.5, 2.65])
def test_feature_armijogoldsteinls_basic(self):
top = om.Problem()
top.model.add_subsystem('comp',
ImplCompTwoStatesArrays(),
promotes_inputs=['x'])
top.model.nonlinear_solver = om.NewtonSolver(solve_subsystems=False)
top.model.nonlinear_solver.options['maxiter'] = 10
top.model.linear_solver = om.ScipyKrylov()
top.model.nonlinear_solver.linesearch = om.ArmijoGoldsteinLS()
top.setup()
top.set_val('x', np.array([2., 2, 2]).reshape(3, 1))
# Test lower bounds: should go to the lower bound and stall
top.set_val('comp.y', 0.)
top.set_val('comp.z', 1.6)
top.run_model()
for ind in range(3):
assert_near_equal(top.get_val('comp.z', indices=ind), [1.5], 1e-8)
def test_feature_armijo_boundscheck_scalar(self):
top = om.Problem()
top.model.add_subsystem('comp',
ImplCompTwoStatesArrays(),
promotes_inputs=['x'])
top.model.nonlinear_solver = om.NewtonSolver(solve_subsystems=False)
top.model.nonlinear_solver.options['maxiter'] = 10
top.model.linear_solver = om.ScipyKrylov()
ls = top.model.nonlinear_solver.linesearch = om.ArmijoGoldsteinLS(
bound_enforcement='scalar')
top.setup()
top.set_val('x', np.array([2., 2, 2]).reshape(3, 1))
top.run_model()
# Test lower bounds: should stop just short of the lower bound
top.set_val('comp.y', 0.)
top.set_val('comp.z', 1.6)
top.run_model()