def test_backtracking(self):
top = Problem()
top.model.add_subsystem('px', IndepVarComp('x', 1.0))
top.model.add_subsystem('comp', ImplCompTwoStates())
top.model.connect('px.x', 'comp.x')
top.model.nonlinear_solver = BroydenSolver()
top.model.nonlinear_solver.options['maxiter'] = 25
top.model.nonlinear_solver.options['diverge_limit'] = 0.5
top.model.nonlinear_solver.options['state_vars'] = ['comp.y', 'comp.z']
top.model.linear_solver = DirectSolver()
top.setup(check=False)
top.model.nonlinear_solver.linesearch = BoundsEnforceLS(
bound_enforcement='vector')
# Setup again because we assigned a new linesearch
top.setup(check=False)
top.set_solver_print(level=2)
# Test lower bound: should go to the lower bound and stall
top['px.x'] = 2.0
top['comp.y'] = 0.0
top['comp.z'] = 1.6
top.run_model()
assert_rel_error(self, top['comp.z'], 1.5, 1e-8)
# Test upper bound: should go to the upper bound and stall
top['px.x'] = 0.5
top['comp.y'] = 0.0
top['comp.z'] = 2.4
top.run_model()
assert_rel_error(self, top['comp.z'], 2.5, 1e-8)
def test_backtracking(self):
top = om.Problem()
top.model.add_subsystem('px', om.IndepVarComp('x', 1.0))
top.model.add_subsystem('comp', ImplCompTwoStates())
top.model.connect('px.x', 'comp.x')
top.model.nonlinear_solver = om.BroydenSolver()
top.model.nonlinear_solver.options['maxiter'] = 25
top.model.nonlinear_solver.options['diverge_limit'] = 0.5
top.model.nonlinear_solver.options['state_vars'] = ['comp.y', 'comp.z']
top.model.linear_solver = om.DirectSolver()
top.setup()
# Setup again because we assigned a new linesearch
top.setup()
top.set_solver_print(level=2)
# Test lower bound: should go to the lower bound and stall
top['px.x'] = 2.0
top['comp.y'] = 0.0
top['comp.z'] = 1.6
top.run_model()
assert_near_equal(top['comp.z'], 1.5, 1e-8)
# Test upper bound: should go to the upper bound and stall
top['px.x'] = 0.5
top['comp.y'] = 0.0
top['comp.z'] = 2.4
top.run_model()
assert_near_equal(top['comp.z'], 2.5, 1e-8)
def setUp(self):
top = om.Problem()
top.model.add_subsystem('px', om.IndepVarComp('x', 1.0))
top.model.add_subsystem('comp', ImplCompTwoStates())
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.setup()
self.top = top
def test_error_handling(self):
# Make sure the debug_print doen't bomb out.
class Bad(ExplicitComponent):
def setup(self):
self.add_input('x', val=0.0)
self.add_input('y', val=0.0)
self.add_output('f_xy', val=0.0, upper=1.0)
self.declare_partials(of='*', wrt='*')
self.count = 0
def compute(self, inputs, outputs):
if self.count < 1:
x = inputs['x']
y = inputs['y']
outputs['f_xy'] = (x - 3.0)**2 + x * y + (y + 4.0)**2 - 3.0
else:
outputs['f_xy'] = np.inf
self.count += 1
def compute_partials(self, inputs, partials):
x = inputs['x']
y = inputs['y']
partials['f_xy', 'x'] = 2.0 * x - 6.0 + y
partials['f_xy', 'y'] = 2.0 * y + 8.0 + x
top = Problem()
top.model = Group()
top.model.add_subsystem('px', IndepVarComp('x', 1.0))
top.model.add_subsystem('comp', ImplCompTwoStates())
top.model.add_subsystem('par', Bad())
top.model.connect('px.x', 'comp.x')
top.model.connect('comp.z', 'par.x')
top.model.nonlinear_solver = NewtonSolver()
top.model.nonlinear_solver.options['maxiter'] = 3
ls = top.model.nonlinear_solver.linesearch = BoundsEnforceLS(
bound_enforcement='vector')
ls.options['maxiter'] = 10
top.set_solver_print(level=0)
top.setup(check=False)
# Make sure we don't raise an error when we reach the final debug print.
top.run_model()
def setUp(self):
top = Problem()
top.model = Group()
top.model.add_subsystem('px', IndepVarComp('x', 1.0))
top.model.add_subsystem('comp', ImplCompTwoStates())
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.setup(check=False)
self.top = top
def setUp(self):
top = om.Problem()
top.model.add_subsystem('px', om.IndepVarComp('x', 1.0))
top.model.add_subsystem('comp', ImplCompTwoStates())
top.model.add_subsystem('par', ParaboloidAE())
top.model.connect('px.x', 'comp.x')
top.model.connect('comp.z', 'par.x')
top.model.nonlinear_solver = om.NewtonSolver()
top.model.nonlinear_solver.options['maxiter'] = 1
top.model.linear_solver = om.ScipyKrylov()
ls = top.model.nonlinear_solver.linesearch = om.ArmijoGoldsteinLS(bound_enforcement='vector')
ls.options['maxiter'] = 10
ls.options['alpha'] = 1.0
top.set_solver_print(level=0)
self.top = top
self.ls = ls
def test_line_search_deprecated(self):
top = Problem()
top.model = Group()
top.model.add_subsystem('px', IndepVarComp('x', 1.0))
top.model.add_subsystem('comp', ImplCompTwoStates())
top.model.connect('px.x', 'comp.x')
top.model.nonlinear_solver = NewtonSolver()
top.model.nonlinear_solver.options['maxiter'] = 10
top.model.linear_solver = ScipyIterativeSolver()
msg = "The 'line_search' attribute provides backwards compatibility with OpenMDAO 1.x ; use 'linesearch' instead."
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
top.model.nonlinear_solver.line_search = ArmijoGoldsteinLS(
bound_enforcement='vector')
self.assertEqual(str(w[-1].message), msg)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
ls = top.model.nonlinear_solver.line_search
self.assertEqual(str(w[-1].message), msg)
ls.options['maxiter'] = 10
ls.options['alpha'] = 1.0
top.setup(check=False)
# Test lower bound: should go to the lower bound and stall
top['px.x'] = 2.0
top['comp.y'] = 0.0
top['comp.z'] = 1.6
top.run_model()
assert_rel_error(self, top['comp.z'], 1.5, 1e-8)
# Test upper bound: should go to the upper bound and stall
top['px.x'] = 0.5
top['comp.y'] = 0.0
top['comp.z'] = 2.4
top.run_model()
assert_rel_error(self, top['comp.z'], 2.5, 1e-8)
def test_feature_specification(self):
top = Problem()
top.model = Group()
top.model.add_subsystem('px', IndepVarComp('x', 1.0))
top.model.add_subsystem('comp', ImplCompTwoStates())
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 = ArmijoGoldsteinLS()
ls.options['maxiter'] = 10
top.setup(check=False)
top['px.x'] = 2.0
top['comp.y'] = 0.0
top['comp.z'] = 1.6
top.run_model()
assert_rel_error(self, top['comp.z'], 1.5, 1e-8)
def test_line_search_deprecated(self):
top = Problem()
top.model.add_subsystem('px', IndepVarComp('x', 1.0))
top.model.add_subsystem('comp', ImplCompTwoStates())
top.model.connect('px.x', 'comp.x')
top.model.nonlinear_solver = NewtonSolver()
top.model.nonlinear_solver.options['maxiter'] = 10
top.model.linear_solver = ScipyKrylov()
msg = "The 'line_search' attribute provides backwards compatibility with OpenMDAO 1.x ; " \
"use 'linesearch' instead."
with assert_warning(DeprecationWarning, msg):
top.model.nonlinear_solver.line_search = ArmijoGoldsteinLS(
bound_enforcement='vector')
with assert_warning(DeprecationWarning, msg):
ls = top.model.nonlinear_solver.line_search
ls.options['maxiter'] = 10
ls.options['alpha'] = 1.0
top.setup(check=False)
# Test lower bound: should go to the lower bound and stall
top['px.x'] = 2.0
top['comp.y'] = 0.0
top['comp.z'] = 1.6
top.run_model()
assert_rel_error(self, top['comp.z'], 1.5, 1e-8)
# Test upper bound: should go to the upper bound and stall
top['px.x'] = 0.5
top['comp.y'] = 0.0
top['comp.z'] = 2.4
top.run_model()
assert_rel_error(self, top['comp.z'], 2.5, 1e-8)
def test_feature_specification(self):
from openmdao.api import Problem, Group, IndepVarComp, NewtonSolver, ScipyKrylov, ArmijoGoldsteinLS
from openmdao.test_suite.components.implicit_newton_linesearch import ImplCompTwoStates
top = Problem()
top.model = Group()
top.model.add_subsystem('px', IndepVarComp('x', 1.0))
top.model.add_subsystem('comp', ImplCompTwoStates())
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()
ls.options['maxiter'] = 10
top.setup(check=False)
top['px.x'] = 2.0
top['comp.y'] = 0.0
top['comp.z'] = 1.6
top.run_model()
assert_rel_error(self, top['comp.z'], 1.5, 1e-8)
def test_analysis_error(self):
class ParaboloidAE(ExplicitComponent):
""" Evaluates the equation f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3
This version raises an analysis error if x < 2.0
The AE in ParaboloidAE stands for AnalysisError."""
def __init__(self):
super(ParaboloidAE, self).__init__()
self.fail_hard = False
def setup(self):
self.add_input('x', val=0.0)
self.add_input('y', val=0.0)
self.add_output('f_xy', val=0.0)
self.declare_partials(of='*', wrt='*')
def compute(self, inputs, outputs):
"""f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3
Optimal solution (minimum): x = 6.6667; y = -7.3333
"""
x = inputs['x']
y = inputs['y']
if x < 1.75:
raise AnalysisError('Try Again.')
outputs['f_xy'] = (x - 3.0)**2 + x * y + (y + 4.0)**2 - 3.0
def compute_partials(self, inputs, partials):
""" Jacobian for our paraboloid."""
x = inputs['x']
y = inputs['y']
partials['f_xy', 'x'] = 2.0 * x - 6.0 + y
partials['f_xy', 'y'] = 2.0 * y + 8.0 + x
top = Problem()
top.model = Group()
top.model.add_subsystem('px', IndepVarComp('x', 1.0))
top.model.add_subsystem('comp', ImplCompTwoStates())
top.model.add_subsystem('par', ParaboloidAE())
top.model.connect('px.x', 'comp.x')
top.model.connect('comp.z', 'par.x')
top.model.nonlinear_solver = NewtonSolver()
top.model.nonlinear_solver.options['maxiter'] = 1
top.model.linear_solver = ScipyKrylov()
ls = top.model.nonlinear_solver.linesearch = ArmijoGoldsteinLS(
bound_enforcement='vector')
ls.options['maxiter'] = 10
ls.options['alpha'] = 1.0
top.set_solver_print(level=0)
top.setup(check=False)
# Test lower bound: should go as far as it can without going past 1.75 and triggering an
# AnalysisError. It doesn't do a great job, so ends up at 1.8 instead of 1.75
top['px.x'] = 2.0
top['comp.y'] = 0.0
top['comp.z'] = 2.1
top.run_model()
assert_rel_error(self, top['comp.z'], 1.8, 1e-8)
# Test the behavior with the switch turned off.
top = Problem()
top.model = Group()
top.model.add_subsystem('px', IndepVarComp('x', 1.0))
top.model.add_subsystem('comp', ImplCompTwoStates())
top.model.add_subsystem('par', ParaboloidAE())
top.model.connect('px.x', 'comp.x')
top.model.connect('comp.z', 'par.x')
top.model.nonlinear_solver = NewtonSolver()
top.model.nonlinear_solver.options['maxiter'] = 1
top.model.linear_solver = ScipyKrylov()
ls = top.model.nonlinear_solver.linesearch = ArmijoGoldsteinLS(
bound_enforcement='vector')
ls.options['maxiter'] = 10
ls.options['alpha'] = 1.0
ls.options['retry_on_analysis_error'] = False
top.set_solver_print(level=0)
top.setup(check=False)
top['px.x'] = 2.0
top['comp.y'] = 0.0
top['comp.z'] = 2.1
with self.assertRaises(AnalysisError) as context:
top.run_model()
self.assertEqual(str(context.exception), 'Try Again.')
