def configure(self):
""" Creates a new Assembly with this problem
Optimal Design at (1.9776, 0, 0)
Optimal Objective = 3.18339
"""
self.add('driver', FixedPointIterator())
# Inner Loop - Full Multidisciplinary Solve via fixed point iteration
C1 = self.add('C1', sellar.Discipline1())
C2 = self.add('C2', sellar.Discipline2())
self.driver.workflow.add(['C1','C2'])
#not relevant to the iteration. Just fixed constants
C1.z1 = C2.z1 = 1.9776
C1.z2 = C2.z2 = 0
C1.x1 = 0
# Solver settings
self.driver.max_iteration = 5
self.driver.tolerance = 1.e-15
self.driver.print_convergence = False
def __init__(self):
""" Creates a new Assembly with this problem
Optimal Design at (1.9776, 0, 0)
Optimal Objective = 3.18339"""
# pylint: disable-msg=E1101
super(SellarMDF, self).__init__()
# create Optimizer instance
self.add('driver', CONMINdriver())
# Outer Loop - Global Optimization
self.add('solver', BroydenSolver())
self.driver.workflow.add('solver')
# Inner Loop - Full Multidisciplinary Solve via fixed point iteration
self.add('dis1', sellar.Discipline1())
self.add('dis2', sellar.Discipline2())
self.solver.workflow.add(['dis1', 'dis2'])
# Make all connections
self.connect('dis1.y1','dis2.y1')
# Iteration loop
self.solver.add_parameter('dis1.y2', low=-9.e99, high=9.e99)
self.solver.add_constraint('dis2.y2 = dis1.y2')
# equivilent form
# self.solver.add_constraint('dis2.y2 - dis1.y2 = 0')
#Driver Settings
self.solver.itmax = 10
self.solver.alpha = .4
self.solver.tol = .0000001
self.solver.algorithm = "broyden2"
# Optimization parameters
self.driver.add_objective('(dis1.x1)**2 + dis1.z2 + dis1.y1 + math.exp(-dis2.y2)')
for param, low, high in zip([('dis1.z1','dis2.z1'), ('dis1.z2','dis2.z2'),
'dis1.x1'],
[-10.0, 0.0, 0.0],
[10.0, 10.0, 10.0]):
self.driver.add_parameter(param, low=low, high=high)
map(self.driver.add_constraint, ['3.16 < dis1.y1',
'dis2.y2 < 24.0' ])
self.driver.cons_is_linear = [1, 1]
self.driver.iprint = 0
self.driver.itmax = 30
self.driver.fdch = .001
self.driver.fdchm = .001
self.driver.delfun = .0001
self.driver.dabfun = .000001
self.driver.ctlmin = 0.0001
def __init__(self):
""" Creates a new Assembly with this problem
Optimal Design at (1.9776, 0, 0)
Optimal Objective = 3.18339"""
super(SellarMDF, self).__init__()
# create Optimizer instance
self.add('driver', CONMINdriver())
# Outer Loop - Global Optimization
self.add('solver', FixedPointIterator())
self.driver.workflow.add(['solver'])
# Inner Loop - Full Multidisciplinary Solve via fixed point iteration
self.add('dis1', sellar.Discipline1())
self.add('dis2', sellar.Discipline2())
self.solver.workflow.add(['dis1', 'dis2'])
# Add Parameters to optimizer
self.driver.add_parameter(('dis1.z1','dis2.z1'), low = -10.0, high = 10.0)
self.driver.add_parameter(('dis1.z2','dis2.z2'), low = 0.0, high = 10.0)
self.driver.add_parameter('dis1.x1', low = 0.0, high = 10.0)
# Make all connections
self.connect('dis1.y1','dis2.y1')
# Iteration loop
self.solver.add_parameter('dis1.y2', low=-9.e99, high=9.e99)
self.solver.add_constraint('dis2.y2 = dis1.y2')
# equivilent form
# self.solver.add_constraint('dis2.y2 - dis1.y2 = 0')
#Driver settings
self.solver.max_iteration = 1000
self.solver.tolerance = .0001
# Optimization parameters
self.driver.add_objective('(dis1.x1)**2 + dis1.z2 + dis1.y1 + math.exp(-dis2.y2)')
self.driver.add_constraint('3.16 < dis1.y1')
self.driver.add_constraint('dis2.y2 < 24.0')
self.driver.cons_is_linear = [1, 1]
self.driver.iprint = 0
self.driver.itmax = 30
self.driver.fdch = .001
self.driver.fdchm = .001
self.driver.delfun = .0001
self.driver.dabfun = .000001
self.driver.ctlmin = 0.0001
def configure(self):
""" Creates a new Assembly with this problem
Optimal Design at (1.9776, 0, 0)
Optimal Objective = 3.18339"""
# pylint: disable-msg=E1101
# create Optimizer instance
self.add('driver', SLSQPdriver())
#Outer Loop - Global Optimization
self.add('solver', BroydenSolver())
self.driver.workflow.add(['solver'])
# Inner Loop - Full Multidisciplinary Solve via fixed point iteration
self.add('dis1', sellar.Discipline1())
self.add('dis2', sellar.Discipline2())
self.solver.workflow.add(['dis1', 'dis2'])
# Add Parameters to optimizer
self.driver.add_parameter(('dis1.z1', 'dis2.z1'), low=-10.0, high=10.0)
self.driver.add_parameter(('dis1.z2', 'dis2.z2'), low=0.0, high=10.0)
self.driver.add_parameter('dis1.x1', low=0.0, high=10.0)
# Make all connections
self.connect('dis1.y1', 'dis2.y1')
# Solver Iteration loop
self.solver.add_parameter('dis1.y2')
self.solver.add_constraint('dis2.y2 = dis1.y2')
# equivilent form
# self.solver.add_constraint('dis2.y2 - dis1.y2 = 0')
#Driver Settings
self.solver.itmax = 10
self.solver.alpha = .4
self.solver.tol = .0000001
self.solver.algorithm = "broyden2"
# Optimization parameters
self.driver.add_objective(
'(dis1.x1)**2 + dis1.z2 + dis1.y1 + math.exp(-dis2.y2)')
self.driver.add_constraint('3.16 < dis1.y1')
self.driver.add_constraint('dis2.y2 < 24.0')
self.driver.iprint = 0
def configure(self):
""" Creates a new Assembly with this problem
Optimal Design at (1.9776, 0, 0)
Optimal Objective = 3.18339"""
# Disciplines
self.add('dis1', sellar.Discipline1())
self.add('dis2', sellar.Discipline2())
objective = '(dis1.x1)**2 + dis1.z2 + dis1.y1 + exp(-dis2.y2)'
constraint1 = 'dis1.y1 > 3.16'
constraint2 = 'dis2.y2 < 24.0'
# Top level is Fixed-Point Iteration
self.add('driver', FixedPointIterator())
def __init__(self):
""" Creates a new Assembly with this problem
Optimal Design at (1.9776, 0, 0)
Optimal Objective = 3.18339"""
super(SellarIDF, self).__init__()
# create Optimizer instance
self.add('driver', CONMINdriver())
# Disciplines
self.add('dis1', sellar.Discipline1())
self.add('dis2', sellar.Discipline2())
# Driver process definition
self.driver.workflow.add(['dis1', 'dis2'])
# Optimization parameters
self.driver.add_objective(
'(dis1.x1)**2 + dis1.z2 + dis1.y1 + math.exp(-dis2.y2)')
#Global Design Variables
self.driver.add_parameter(('dis1.z1', 'dis2.z1'), low=-10.0, high=10.0)
self.driver.add_parameter(('dis1.z2', 'dis2.z2'), low=0.0, high=10.0)
#Local Design Variables and Coupling Variables
self.driver.add_parameter('dis1.x1', low=0.0, high=10.0)
self.driver.add_parameter('dis2.y1', low=3.16, high=10.0)
self.driver.add_parameter('dis1.y2', low=-10.0, high=24.0)
self.driver.add_constraint('(dis2.y1-dis1.y1)**3 <= 0')
self.driver.add_constraint('(dis1.y1-dis2.y1)**3 <= 0')
self.driver.add_constraint('(dis2.y2-dis1.y2)**3 <= 0')
self.driver.add_constraint('(dis1.y2-dis2.y2)**3 <= 0')
self.driver.iprint = 0
self.driver.itmax = 100
self.driver.fdch = .003
self.driver.fdchm = .003
self.driver.delfun = .0001
self.driver.dabfun = .00001
self.driver.ct = -.01
self.driver.ctlmin = 0.001
def configure(self):
self.add("driver", DOEdriver())
self.recorders = [ListCaseRecorder()]
self.driver.DOEgenerator = FullFactorial()
# configure the specific DOE options
self.driver.DOEgenerator.num_levels = 3
self.add("dis1", sellar.Discipline1())
self.add("dis2", sellar.Discipline2())
# setting some variables to fixed values
self.dis1.y2 = 3.15
self.dis2.y1 = 3.78
# adding three parameters to the DOEDriver
self.driver.add_parameter(("dis1.z1", "dis2.z1"), low=-10.0, high=10.0)
self.driver.add_parameter(("dis1.z2", "dis2.z2"), low=0.0, high=10.0)
self.driver.add_parameter("dis1.x1", low=0.0, high=10.0)
def configure(self):
""" Creates a new Assembly with this problem
Optimal Design at (1.9776, 0, 0)
Optimal Objective = 3.18339"""
# create Optimizer instance
self.add('driver', SLSQPdriver())
# Disciplines
self.add('dis1', sellar.Discipline1())
self.add('dis2', sellar.Discipline2())
# Driver process definition
self.driver.workflow.add(['dis1', 'dis2'])
# Optimization parameters
self.driver.add_objective(
'(dis1.x1)**2 + dis1.z2 + dis1.y1 + math.exp(-dis2.y2)')
#Global Design Variables
self.driver.add_parameter(('dis1.z1', 'dis2.z1'), low=-10.0, high=10.0)
self.driver.add_parameter(('dis1.z2', 'dis2.z2'), low=0.0, high=10.0)
#Local Design Variables and Coupling Variables
self.driver.add_parameter('dis1.x1', low=0.0, high=10.0)
self.driver.add_parameter('dis2.y1', low=-1e99, high=1e99)
self.driver.add_parameter('dis1.y2', low=-1e99, high=1e99)
self.driver.add_constraint('3.16 < dis1.y1')
self.driver.add_constraint('dis2.y2 < 24.0')
self.driver.add_constraint('(dis2.y1-dis1.y1)**2 <= 0')
self.driver.add_constraint('(dis2.y2-dis1.y2)**2 <= 0')
self.driver.iprint = 0
def configure(self):
""" Creates a new Assembly with this problem
Optimal Design at (1.9776, 0, 0)
Optimal Objective = 3.18339"""
# Sub assembly
sub = self.add('sub', Assembly())
# Inner Loop - Full Multidisciplinary Solve via fixed point iteration
sub.add('driver', FixedPointIterator())
sub.add('dis1', sellar.Discipline1())
sub.add('dis2', sellar.Discipline2())
sub.driver.workflow.add(['dis1', 'dis2'])
# Make all connections
sub.connect('dis1.y1', 'dis2.y1')
sub.connect('dis1.z1', 'dis2.z1')
# Iteration loop
sub.driver.add_parameter('dis1.y2')
sub.driver.add_constraint('dis2.y2 = dis1.y2')
# Solver settings
sub.driver.max_iteration = 100
sub.driver.tolerance = .00001
sub.driver.print_convergence = False
# Subassy boundaries
sub.add('globals', VarTree(Globals(), iotype='in'))
sub.add('states', VarTree(States(), iotype='out'))
sub.connect('globals.z1', 'dis1.z1')
# Note, dis1.z2 is connected by input-input conn
sub.connect('globals.z2', 'dis1.z2')
sub.connect('globals.z2', 'dis2.z2')
sub.create_passthrough('dis1.x1')
sub.connect('dis1.y1', 'states.y[0]')
sub.connect('dis2.y2', 'states.y[1]')
# Global Optimization
self.add('driver', SLSQPdriver())
self.driver.gradient_options.force_fd = True
#self.driver.iprint = 3
# Extra comp
self.add('half', Half())
self.connect('half.z2b', 'sub.globals.z2')
self.driver.workflow.add(['half', 'sub'])
# Add Parameters to optimizer
self.driver.add_parameter('sub.globals.z1', low=-10.0, high=10.0)
self.driver.add_parameter('half.z2a', low=0.0, high=10.0)
self.driver.add_parameter('sub.x1', low=0.0, high=10.0)
# Optimization parameters
self.driver.add_objective(
'(sub.x1)**2 + sub.globals.z2 + sub.states.y[0] + math.exp(-sub.states.y[1])'
)
self.driver.add_constraint('3.16 < sub.states.y[0]')
self.driver.add_constraint('sub.states.y[1] < 24.0')
self.sub.globals.z1 = 5.0
self.half.z2a = 2.0
self.sub.x1 = 1.0
def configure(self):
""" Creates a new Assembly with this problem
Optimal Design at (1.9776, 0, 0)
Optimal Objective = 3.18339"""
# Global Optimization
self.add('driver', SLSQPdriver())
self.add('localopt1', SLSQPdriver())
self.add('localopt2', SLSQPdriver())
self.driver.workflow.add(['localopt1', 'localopt2'])
# Local Optimization 1
self.add('dis1', sellar.Discipline1())
self.localopt1.workflow.add('dis1')
# Local Optimization 2
self.add('dis2a', SellarDiscipline2a())
self.add('dis2b', SellarDiscipline2b())
self.add('dis2c', SellarDiscipline2c())
self.connect('dis2a.temp1', 'dis2b.temp1')
self.connect('dis2b.temp2', 'dis2c.temp2')
self.localopt2.workflow.add(['dis2a', 'dis2b', 'dis2c'])
#Parameters - Global Optimization
# using group parameters to 'broadcast' same data
self.driver.add_objective('x1**2 + z2 + y1 + math.exp(-y2)')
for param, low, high in zip(['z1', 'z2', 'x1', 'y1', 'y2'],
[-10.0, 0.0, 0.0, 3.16, -10.0],
[10.0, 10.0, 10.0, 10, 24.0]):
self.driver.add_parameter(param, low=low, high=high)
map(self.driver.add_constraint, [
'(z1-dis1.z1)**2 + (z2-dis1.z2)**2 + (x1-dis1.x1)**2 + '
'(y1-dis1.y1)**2 + (y2-dis1.y2)**2 < 0',
'(z1-dis2b.z1)**2 + (z2-dis2c.z2)**2 + (y1-dis2a.y1)**2 + '
'(y2-dis2c.y2)**2 < 0'])
self.driver.iprint = 0
self.driver.itmax = 100
self.driver.fdch = .003
self.driver.fdchm = .003
self.driver.delfun = .0001
self.driver.dabfun = .00001
self.driver.ct = -.0008
self.driver.ctlmin = 0.0008
#Parameters - Local Optimization 1
self.localopt1.add_objective('(z1-dis1.z1)**2 + '
'(z2-dis1.z2)**2 + '
'(x1-dis1.x1)**2 + '
'(y1-dis1.y1)**2 + '
'(y2-dis1.y2)**2')
for param, low, high in zip(['dis1.z1', 'dis1.z2', 'dis1.x1', 'dis1.y2'],
[-10.0, 0.0, 0.0, -10.0],
[10.0, 10.0, 10.0, 24.0]):
self.localopt1.add_parameter(param, low=low, high=high)
self.localopt1.iprint = 0
self.localopt1.itmax = 100
self.localopt1.fdch = .003
self.localopt1.fdchm = .003
self.localopt1.delfun = .0001
self.localopt1.dabfun = .000001
#Parameters - Local Optimization 2
self.localopt2.add_objective('(z1-dis2b.z1)**2 + '
'(z2-dis2c.z2)**2 + '
'(y1-dis2a.y1)**2 + '
'(y2-dis2c.y2)**2')
for param, low, high in zip(['dis2b.z1', 'dis2c.z2', 'dis2a.y1'],
[-10.0, 0.0, 3.16],
[10.0, 10.0, 10]):
self.localopt2.add_parameter(param, low=low, high=high)
self.localopt2.iprint = 0
self.localopt2.itmax = 100
self.localopt2.fdch = .003
self.localopt2.fdchm = .003
self.localopt2.delfun = .001
self.localopt2.dabfun = .00001
def configure(self):
""" Creates a new Assembly with this problem
Optimal Design at (1.9776, 0, 0)
Optimal Objective = 3.18339"""
# Disciplines
self.add('dis1', sellar.Discipline1())
self.add('dis2', sellar.Discipline2())
objective = '(dis1.x1)**2 + dis1.z2 + dis1.y1 + exp(-dis2.y2)'
constraint1 = 'dis1.y1 > 3.16'
constraint2 = 'dis2.y2 < 24.0'
# Top level is Fixed-Point Iteration
self.add('driver', FixedPointIterator())
self.driver.add_parameter('dis1.x1', low=0.0, high=10.0, start=1.0)
self.driver.add_parameter(['dis1.z1', 'dis2.z1'],
low=-10.0,
high=10.0,
start=5.0)
self.driver.add_parameter(['dis1.z2', 'dis2.z2'],
low=0.0,
high=10.0,
start=2.0)
self.driver.add_constraint('x1_store = dis1.x1')
self.driver.add_constraint('z_store[0] = dis1.z1')
self.driver.add_constraint('z_store[1] = dis1.z2')
self.driver.max_iteration = 50
self.driver.tolerance = .001
# Multidisciplinary Analysis
self.add('mda', BroydenSolver())
self.mda.add_parameter('dis1.y2', low=-9.e99, high=9.e99, start=0.0)
self.mda.add_constraint('dis2.y2 = dis1.y2')
self.mda.add_parameter('dis2.y1', low=-9.e99, high=9.e99, start=3.16)
self.mda.add_constraint('dis2.y1 = dis1.y1')
# Discipline 1 Sensitivity Analysis
self.add('sa_dis1', SensitivityDriver())
self.sa_dis1.workflow.add(['dis1'])
self.sa_dis1.add_parameter('dis1.x1', low=0.0, high=10.0, fd_step=.001)
self.sa_dis1.add_constraint(constraint1)
self.sa_dis1.add_constraint(constraint2)
self.sa_dis1.add_objective(objective, name='obj')
# Discipline 2 Sensitivity Analysis
# dis2 has no local parameter, so there is no need to treat it as
# a subsystem.
# System Level Sensitivity Analysis
# Note, we cheat here and run an MDA instead of solving the
# GSE equations. Have to put this on the TODO list.
self.add('ssa', SensitivityDriver())
self.ssa.workflow.add(['mda'])
self.ssa.add_parameter(['dis1.z1', 'dis2.z1'], low=-10.0, high=10.0)
self.ssa.add_parameter(['dis1.z2', 'dis2.z2'], low=0.0, high=10.0)
self.ssa.add_constraint(constraint1)
self.ssa.add_constraint(constraint2)
self.ssa.add_objective(objective, name='obj')
# Discipline Optimization
# (Only discipline1 has an optimization input)
self.add('bbopt1', CONMINdriver())
self.bbopt1.add_parameter('x1_store', low=0.0, high=10.0, start=1.0)
self.bbopt1.add_objective(
'sa_dis1.F[0] + sa_dis1.dF[0][0]*(x1_store-dis1.x1)')
self.bbopt1.add_constraint(
'sa_dis1.G[0] + sa_dis1.dG[0][0]*(x1_store-dis1.x1) < 0')
#this one is technically unncessary
self.bbopt1.add_constraint(
'sa_dis1.G[1] + sa_dis1.dG[1][0]*(x1_store-dis1.x1) < 0')
self.bbopt1.add_constraint('(x1_store-dis1.x1)<.5')
self.bbopt1.add_constraint('(x1_store-dis1.x1)>-.5')
self.bbopt1.iprint = 0
self.bbopt1.linobj = True
# Global Optimization
self.add('sysopt', CONMINdriver())
self.sysopt.add_parameter('z_store[0]',
low=-10.0,
high=10.0,
start=5.0)
self.sysopt.add_parameter('z_store[1]', low=0.0, high=10.0, start=2.0)
self.sysopt.add_objective(
'ssa.F[0]+ ssa.dF[0][0]*(z_store[0]-dis1.z1) + ssa.dF[0][1]*(z_store[1]-dis1.z2)'
)
self.sysopt.add_constraint(
'ssa.G[0] + ssa.dG[0][0]*(z_store[0]-dis1.z1) + ssa.dG[0][1]*(z_store[1]-dis1.z2) < 0'
)
self.sysopt.add_constraint(
'ssa.G[1] + ssa.dG[1][0]*(z_store[0]-dis1.z1) + ssa.dG[1][1]*(z_store[1]-dis1.z2) < 0'
)
self.sysopt.add_constraint('z_store[0]-dis1.z1<.5')
self.sysopt.add_constraint('z_store[0]-dis1.z1>-.5')
self.sysopt.add_constraint('z_store[1]-dis1.z2<.5')
self.sysopt.add_constraint('z_store[1]-dis1.z2>-.5')
self.sysopt.iprint = 0
self.sysopt.linobj = True
self.driver.workflow.add(['ssa', 'sa_dis1', 'bbopt1', 'sysopt'])
def configure(self):
""" Creates a new Assembly with this problem
Optimal Design at (1.9776, 0, 0)
Optimal Objective = 3.18339"""
# Global Optimization
self.add('driver', SLSQPdriver())
self.add('localopt1', SLSQPdriver())
self.add('localopt2', SLSQPdriver())
self.driver.workflow.add(['localopt2', 'localopt1'])
# Local Optimization 1
self.add('dis1', sellar.Discipline1())
# Local Optimization 2
self.add('dis2', sellar.Discipline2())
#Parameters - Global Optimization
self.driver.add_objective('(local_des_var_targets[0])**2 + '
'global_des_var_targets[1] + '
'coupling_var_targets[0] + '
'math.exp(-coupling_var_targets[1])')
self.driver.add_parameter('global_des_var_targets[0]',
low=-10.0,
high=10.0)
self.driver.add_parameter('global_des_var_targets[1]',
low=0.0,
high=10.0)
self.driver.add_parameter('local_des_var_targets[0]',
low=0.0,
high=10.0)
self.driver.add_parameter('coupling_var_targets[0]',
low=3.16,
high=10.0)
self.driver.add_parameter('coupling_var_targets[1]',
low=-10.0,
high=24.0)
con1 = '(global_des_var_targets[0] - dis1.z1)**2 + ' \
'(global_des_var_targets[1] - dis1.z2)**2 + ' \
'(local_des_var_targets[0] - dis1.x1)**2 + ' \
'(coupling_var_targets[0] - dis1.y1)**2 + ' \
'(coupling_var_targets[1] - dis1.y2)**2 <= 0'
con2 = '(global_des_var_targets[0] - dis2.z1)**2 + ' \
'(global_des_var_targets[1] - dis2.z2)**2 + ' \
'(coupling_var_targets[0] - dis2.y1)**2 + ' \
'(coupling_var_targets[1] - dis2.y2)**2 <= 0'
self.driver.add_constraint(con1)
self.driver.add_constraint(con2)
self.driver.iprint = 0
#Parameters - Local Optimization 1
self.localopt1.add_objective(
'(global_des_var_targets[0] - dis1.z1)**2 + '
'(global_des_var_targets[1] - dis1.z2)**2 + '
'(local_des_var_targets[0] - dis1.x1)**2 + '
'(coupling_var_targets[0] - dis1.y1)**2 + '
'(coupling_var_targets[1] - dis1.y2)**2')
self.localopt1.add_parameter('dis1.x1', low=0.0, high=10.0)
self.localopt1.add_parameter('dis1.z1', low=-10.0, high=10.0)
self.localopt1.add_parameter('dis1.z2', low=0.0, high=10.0)
self.localopt1.add_parameter('dis1.y2', low=-1e99, high=1e99)
self.localopt1.add_constraint('dis1.y1 > 3.16')
self.localopt1.iprint = 0
self.localopt1.accuracy = 1e-12
#Parameters - Local Optimization 2
self.localopt2.add_objective('(global_des_var_targets[0] - dis2.z1)**2 + ' \
'(global_des_var_targets[1] - dis2.z2)**2 + ' \
'(coupling_var_targets[0] - dis2.y1)**2 + ' \
'(coupling_var_targets[1] - dis2.y2)**2')
self.localopt2.add_parameter('dis2.z1', low=-10.0, high=10.0)
self.localopt2.add_parameter('dis2.z2', low=0.0, high=10.0)
self.localopt2.add_parameter('dis2.y1', low=-1e99, high=1e99)
self.localopt2.add_constraint('dis2.y2 < 24.0')
self.localopt2.iprint = 0
self.localopt2.accuracy = 1e-12
def configure(self):
# Global Optimization
self.add('driver', SLSQPdriver())
self.add('localopt1', SLSQPdriver())
self.add('localopt2', SLSQPdriver())
self.driver.workflow.add(['localopt1', 'localopt2'])
# Local Optimization 1
self.add('dis1', sellar.Discipline1())
self.localopt1.workflow.add(['dis1'])
# Local Optimization 2
self.add('dis2', sellar.Discipline2())
self.localopt2.workflow.add(['dis2'])
# Parameters - Global Optimization
self.driver.add_objective(
'(local_des_var_targets[0])**2 + global_des_var_targets[1] + coupling_var_targets[0] + math.exp(-coupling_var_targets[1])'
)
self.driver.add_parameter('global_des_var_targets[0]',
low=-10.0,
high=10.0)
self.driver.add_parameter('global_des_var_targets[1]',
low=0.0,
high=10.0)
self.driver.add_parameter('coupling_var_targets[0]',
low=-1e99,
high=1e99)
self.driver.add_parameter('coupling_var_targets[1]',
low=-1e99,
high=1e99)
self.driver.add_parameter('local_des_var_targets[0]',
low=0.0,
high=10.0)
con1 = '(local_des_var_targets[0]-dis1.x1)**2+' + \
'(global_des_var_targets[0]-dis1.z1)**2+' + \
'(global_des_var_targets[1]-dis1.z2)**2+' + \
'(coupling_var_targets[1]-dis1.y2)**2+' + \
'(coupling_var_targets[0]-dis1.y1)**2<=.001'
con2 = '(global_des_var_targets[0]-dis2.z1)**2 +' + \
'(global_des_var_targets[1]-dis2.z2)**2 +' + \
'(coupling_var_targets[0]-dis2.y1)**2 +' + \
'(coupling_var_targets[1]-dis2.y2)**2 <= .001'
self.driver.add_constraint(con1)
self.driver.add_constraint(con2)
self.driver.printvars = ['dis1.y1', 'dis2.y2']
self.driver.iprint = 1
# Parameters - Local Optimization 1
self.localopt1.add_objective('(local_des_var_targets[0]-dis1.x1)**2+'
'(global_des_var_targets[0]-dis1.z1)**2+'
'(global_des_var_targets[1]-dis1.z2)**2+'
'(coupling_var_targets[1]-dis1.y2)**2+'
'(coupling_var_targets[0]-dis1.y1)**2')
self.localopt1.add_parameter('dis1.x1', low=0.0, high=10.0)
self.localopt1.add_parameter('dis1.z1', low=-10.0, high=10.0)
self.localopt1.add_parameter('dis1.z2', low=0.0, high=10.0)
self.localopt1.add_parameter('dis1.y2', low=-1e99, high=1e99)
self.localopt1.add_constraint('3.16 < dis1.y1')
self.localopt1.iprint = 1
# Parameters - Local Optimization 2
self.localopt2.add_objective(
'(global_des_var_targets[0]-dis2.z1)**2 + ' +
'(global_des_var_targets[1]-dis2.z2)**2 + ' +
'(coupling_var_targets[0]-dis2.y1)**2 + ' +
'(coupling_var_targets[1]-dis2.y2)**2')
self.localopt2.add_parameter('dis2.z1', low=-10.0, high=10.0)
self.localopt2.add_parameter('dis2.z2', low=0.0, high=10.0)
self.localopt2.add_parameter('dis2.y1', low=-1e99, high=1e99)
self.localopt2.add_constraint('dis2.y2 < 24.0')
self.localopt2.iprint = 1
