class Sub(Problem):
def __init__(self):
super(Sub, self).__init__()
self.root = Group()
# Add the 'Paraboloid Component' to sub's root Group. Alternatively, I could have placed the 'Paraboloid' Component within a group
self.root.add('P', Paraboloid())
# Initialize x and y values in seperate IndepVarComps and add them to sub's root group
self.root.add('p1', IndepVarComp('x', 13.0))
self.root.add('p2', IndepVarComp('y', -14.0))
# Define a constraint equation and add it to top's root Group
self.root.add('con', ExecComp('c = x - y'))
# Connect 'p1.x' and 'p2.y' to 'con.x' and 'con.y' respectively
self.root.connect('p1.x', 'con.x')
self.root.connect('p2.y', 'con.y')
# Connect the IndepVarComps 'p1.x' and 'p2.y' to 'T.Paraboloid.x' and 'T.Paraboloid.y' respectively
self.root.connect('p1.x', 'P.x')
self.root.connect('p2.y', 'P.y')
# Instantiate sub's optimization driver
self.driver = ScipyOptimizer()
# Modify the optimization driver's settings
self.driver.options[
'optimizer'] = 'COBYLA' # Type of Optimizer. 'COBYLA' does not require derivatives
self.driver.options[
'tol'] = 1.0e-4 # Tolerance for termination. Not sure exactly what it represents. Default: 1.0e-6
self.driver.options[
'maxiter'] = 200 # Maximum iterations. Default: 200
self.driver.opt_settings[
'rhobeg'] = 1.0 # Initial step size. Default: 1.0
#sub.driver.opt_settings['catol'] = 0.1 # Absolute tolerance for constraint violations
# Add design variables, objective, and constraints to the optimization driver
self.driver.add_desvar('p1.x', lower=-50, upper=50)
self.driver.add_objective('P.f_xy')
self.driver.add_constraint('con.c', lower=15.0)
self.driver.add_constraint(
'p1.x', lower=-50.0, upper=50.0
) # Note adding this while this variable constraint reduces the degree to which
class BruteForceSellarProblem(Problem):
""" Performs optimization on the Sellar problem.
Applies a normal distribution to the design vars and runs all of the
samples, then collects the values of all of the outputs, calculates
the mean of those and stuffs that back into the unknowns vector.
This is the brute force version that just stamps out N separate
sellar models in a parallel group and sets the input of each
one to be one of these random design vars.
Args
----
n : number of randomized points to generate for each input value
derivs : if True, use user-defined derivatives, else use Finite Difference
"""
def __init__(self, n=10, derivs=False):
super(BruteForceSellarProblem, self).__init__(impl=impl)
root = self.root = Group()
if not derivs:
root.deriv_options['type'] = 'fd'
sellars = root.add('sellars', ParallelGroup())
for i in range(n):
name = 'sellar%i' % i
sellars.add(name, SellarDerivatives())
root.connect('dist_x', 'sellars.' + name + '.x', src_indices=[i])
root.connect('dist_z',
'sellars.' + name + '.z',
src_indices=[i * 2, i * 2 + 1])
root.connect('sellars.' + name + '.obj', 'collect.obj_%i' % i)
root.connect('sellars.' + name + '.con1', 'collect.con1_%i' % i)
root.connect('sellars.' + name + '.con2', 'collect.con2_%i' % i)
root.add('indep',
IndepVarComp([('x', 1.0), ('z', np.array([5.0, 2.0]))]),
promotes=['x', 'z'])
root.add(
'random',
Randomize(
n=n,
params=[
# name, value, std dev
('x', 1.0, 1e-2),
('z', np.array([5.0, 2.0]), 1e-2)
]),
promotes=['x', 'z', 'dist_x', 'dist_z'])
root.add('collect',
Collector(n=n, names=['obj', 'con1', 'con2']),
promotes=['obj', 'con1', 'con2'])
# top level driver setup
self.driver = ScipyOptimizer()
self.driver.options['optimizer'] = 'SLSQP'
self.driver.options['tol'] = 1.0e-8
self.driver.options['maxiter'] = 50
self.driver.options['disp'] = False
self.driver.add_desvar('z',
lower=np.array([-10.0, 0.0]),
upper=np.array([10.0, 10.0]))
self.driver.add_desvar('x', lower=0.0, upper=10.0)
self.driver.add_objective('obj')
self.driver.add_constraint('con1', upper=0.0)
self.driver.add_constraint('con2', upper=0.0)
class BruteForceSellarProblem(Problem):
""" Performs optimization on the Sellar problem.
Applies a normal distribution to the design vars and runs all of the
samples, then collects the values of all of the outputs, calculates
the mean of those and stuffs that back into the unknowns vector.
This is the brute force version that just stamps out N separate
sellar models in a parallel group and sets the input of each
one to be one of these random design vars.
Args
----
n : number of randomized points to generate for each input value
derivs : if True, use user-defined derivatives, else use Finite Difference
"""
def __init__(self, n=10, derivs=False):
super(BruteForceSellarProblem, self).__init__(impl=impl)
root = self.root = Group()
if not derivs:
root.deriv_options["type"] = "fd"
sellars = root.add("sellars", ParallelGroup())
for i in range(n):
name = "sellar%i" % i
sellars.add(name, SellarDerivatives())
root.connect("dist_x", "sellars." + name + ".x", src_indices=[i])
root.connect("dist_z", "sellars." + name + ".z", src_indices=[i * 2, i * 2 + 1])
root.connect("sellars." + name + ".obj", "collect.obj_%i" % i)
root.connect("sellars." + name + ".con1", "collect.con1_%i" % i)
root.connect("sellars." + name + ".con2", "collect.con2_%i" % i)
root.add("indep", IndepVarComp([("x", 1.0), ("z", np.array([5.0, 2.0]))]), promotes=["x", "z"])
root.add(
"random",
Randomize(
n=n,
params=[
# name, value, std dev
("x", 1.0, 1e-2),
("z", np.array([5.0, 2.0]), 1e-2),
],
),
promotes=["x", "z", "dist_x", "dist_z"],
)
root.add("collect", Collector(n=n, names=["obj", "con1", "con2"]), promotes=["obj", "con1", "con2"])
# top level driver setup
self.driver = ScipyOptimizer()
self.driver.options["optimizer"] = "SLSQP"
self.driver.options["tol"] = 1.0e-8
self.driver.options["maxiter"] = 50
self.driver.options["disp"] = False
self.driver.add_desvar("z", lower=np.array([-10.0, 0.0]), upper=np.array([10.0, 10.0]))
self.driver.add_desvar("x", lower=0.0, upper=10.0)
self.driver.add_objective("obj")
self.driver.add_constraint("con1", upper=0.0)
self.driver.add_constraint("con2", upper=0.0)
class BruteForceSellarProblem(Problem):
""" Performs optimization on the Sellar problem.
Applies a normal distribution to the design vars and runs all of the
samples, then collects the values of all of the outputs, calculates
the mean of those and stuffs that back into the unknowns vector.
This is the brute force version that just stamps out N separate
sellar models in a parallel group and sets the input of each
one to be one of these random design vars.
Args
----
n : number of randomized points to generate for each input value
derivs : if True, use user-defined derivatives, else use Finite Difference
"""
def __init__(self, n=10, derivs=False):
super(BruteForceSellarProblem, self).__init__(impl=impl)
root = self.root = Group()
if not derivs:
root.deriv_options['type'] = 'fd'
sellars = root.add('sellars', ParallelGroup())
for i in range(n):
name = 'sellar%i' % i
sellars.add(name, SellarDerivatives())
root.connect('dist_x', 'sellars.'+name+'.x', src_indices=[i])
root.connect('dist_z', 'sellars.'+name+'.z', src_indices=[i*2, i*2+1])
root.connect('sellars.'+name+'.obj', 'collect.obj_%i' % i)
root.connect('sellars.'+name+'.con1', 'collect.con1_%i' % i)
root.connect('sellars.'+name+'.con2', 'collect.con2_%i' % i)
root.add('indep', IndepVarComp([
('x', 1.0),
('z', np.array([5.0, 2.0]))
]),
promotes=['x', 'z'])
root.add('random', Randomize(n=n, params=[
# name, value, std dev
('x', 1.0, 1e-2),
('z', np.array([5.0, 2.0]), 1e-2)
]),
promotes=['x', 'z', 'dist_x', 'dist_z'])
root.add('collect', Collector(n=n, names=['obj', 'con1', 'con2']),
promotes=['obj', 'con1', 'con2'])
# top level driver setup
self.driver = ScipyOptimizer()
self.driver.options['optimizer'] = 'SLSQP'
self.driver.options['tol'] = 1.0e-8
self.driver.options['maxiter'] = 50
self.driver.options['disp'] = False
self.driver.add_desvar('z', lower=np.array([-10.0, 0.0]),
upper=np.array([ 10.0, 10.0]))
self.driver.add_desvar('x', lower=0.0, upper=10.0)
self.driver.add_objective('obj')
self.driver.add_constraint('con1', upper=0.0)
self.driver.add_constraint('con2', upper=0.0)
