def configure(self):
self.add('d1', Discipline(prob_size=2))
self.add('d2', Discipline(prob_size=2))
self.connect('d1.y_out', 'd2.y_in')
self.connect('d2.y_out', 'd1.y_in')
self.add('driver', NewtonSolver())
self.driver.workflow.add(['d1', 'd2'])
self.driver.newton = True
def configure(self):
self.add('d1', Discipline(prob_size=2))
self.add('d2', Discipline(prob_size=2))
self.connect('d1.y_out', 'd2.y_in')
#self.connect('d2.y_out', 'd1.y_in')
self.add('driver', NewtonSolver())
self.driver.workflow.add(['d1', 'd2'])
self.driver.add_parameter('d1.y_in', low=-1e99, high=1e99)
self.driver.add_constraint('d2.y_out = d1.y_in')
def configure(self):
self.add('SysX', SysX())
self.add('SysY', SysY())
self.add('SysZ', SysZ())
self.connect('SysX.x', 'SysY.x')
self.connect('SysX.x', 'SysZ.x')
self.connect('SysY.y', 'SysZ.y')
self.connect('SysZ.z', 'SysX.z')
self.connect('SysZ.z', 'SysY.z')
self.add('driver', NewtonSolver())
self.driver.workflow.add(['SysX', 'SysY', 'SysZ'])
def test_newton_nested(self):
# Make sure derivatives across the newton-solved system are correct.
top = set_as_top(Assembly())
top.add('driver', SimpleDriver())
top.add('d1', Discipline1_WithDerivatives())
top.d1.x1 = 1.0
top.d1.y1 = 1.0
top.d1.y2 = 1.0
top.d1.z1 = 5.0
top.d1.z2 = 2.0
top.add('d2', Discipline2_WithDerivatives())
top.d2.y1 = 1.0
top.d2.y2 = 1.0
top.d2.z1 = 5.0
top.d2.z2 = 2.0
top.connect('d1.y1', 'd2.y1')
top.add('solver', NewtonSolver())
top.solver.atol = 1e-9
top.solver.workflow.add(['d1', 'd2'])
top.solver.add_parameter('d1.y2', low=-1e99, high=1e99)
top.solver.add_constraint('d1.y2 = d2.y2')
top.driver.workflow.add(['solver'])
top.driver.add_parameter('d1.z1', low=-100, high=100)
top.driver.add_objective('d1.y1 + d1.y2')
top.run()
J = top.driver.calc_gradient(mode='forward')
print J
assert_rel_error(self, J[0][0], 10.77542099, 1e-5)
J = top.driver.calc_gradient(mode='adjoint')
print J
assert_rel_error(self, J[0][0], 10.77542099, 1e-5)
top.driver.gradient_options.fd_step = 1e-7
top.driver.gradient_options.fd_form = 'central'
J = top.driver.calc_gradient(mode='fd')
print J
assert_rel_error(self, J[0][0], 10.77542099, 1e-5)
def configure(self):
self.add('d1', Discipline1_WithDerivatives())
self.d1.x1 = 1.0
self.d1.y1 = 1.0
self.d1.y2 = 1.0
self.d1.z1 = 5.0
self.d1.z2 = 2.0
self.add('d2', Discipline2_WithDerivatives())
self.d2.y1 = 1.0
self.d2.y2 = 1.0
self.d2.z1 = 5.0
self.d2.z2 = 2.0
self.connect('d1.y1', 'd2.y1')
self.connect('d2.y2', 'd1.y2')
self.add('driver', NewtonSolver())
self.driver.workflow.add(['d1', 'd2'])
def test_equation(self):
top = set_as_top(Assembly())
top.add('precomp', ExecCompWithDerivatives(['y=x'], ['dy_dx = 1']))
top.precomp.x = 1.0
expr = ['y = 3.0*x*x -4.0*x']
deriv = ['dy_dx = 6.0*x -4.0']
top.add('comp', ExecCompWithDerivatives(expr, deriv))
top.driver.workflow.add(['comp'])
top.add('driver', NewtonSolver())
top.driver.add_parameter('comp.x')
top.driver.add_constraint('precomp.y - comp.y = 1.0 - 2.0')
top.run()
print top.comp.x, top.comp.y
assert_rel_error(self, top.comp.x, -0.38742588, 1e-4)
def configure(self):
self.add('d1', Discipline1())
self.d1.x1 = 1.0
self.d1.y1 = 1.0
self.d1.y2 = 1.0
self.d1.z1 = 5.0
self.d1.z2 = 2.0
self.add('d2', Discipline2_WithDerivatives())
self.d2.y1 = 1.0
self.d2.y2 = 1.0
self.d2.z1 = 5.0
self.d2.z2 = 2.0
self.connect('d1.y1', 'd2.y1')
#self.connect('d2.y2', 'd1.y2')
self.add('driver', NewtonSolver())
self.driver.workflow.add(['d1', 'd2'])
self.driver.add_parameter('d1.y2', low=-1e99, high=1e99)
self.driver.add_constraint('d1.y2 = d2.y2')
def test_general_solver(self):
a = set_as_top(Assembly())
comp = a.add('comp', ExecComp(exprs=["f=a * x**n + b * x - c"]))
comp.n = 77.0 / 27.0
comp.a = 1.0
comp.b = 1.0
comp.c = 10.0
comp.x = 0.0
driver = a.add('driver', NewtonSolver())
driver.workflow.add('comp')
driver.add_parameter('comp.x', 0, 100)
driver.add_constraint('comp.f=0')
self.top.driver.gradient_options.fd_step = 0.01
self.top.driver.gradient_options.fd_step_type = 'relative'
a.run()
assert_rel_error(self, a.comp.x, 2.06720359226, .0001)
assert_rel_error(self, a.comp.f, 0, .0001)