如何在OpenMDAO 1.x中使用嵌套问题?
我正试图在OpenMDAO上实现协作优化&其他多级体系结构。我读here,这可以通过在问题的子类中定义单独的solve_nonlinear方法来完成。如何在OpenMDAO 1.x中使用嵌套问题?
问题是,在运行问题实例时,定义的solve_linear未被调用。 下面是代码 -
from __future__ import print_function, division
import numpy as np
import time
from openmdao.api import Component,Group, IndepVarComp, ExecComp,\
Problem, ScipyOptimizer, NLGaussSeidel, ScipyGMRES
class SellarDis1(Component):
"""Component containing Discipline 1."""
def __init__(self):
super(SellarDis1, self).__init__()
self.add_param('z', val=np.zeros(2))
self.add_param('x', val=0.0)
self.add_param('y2', val=1.0)
self.add_output('y1', val=1.0)
def solve_nonlinear(self, params, unknowns, resids):
y1 = z1**2 + z2 + x1 - 0.2*y2"""
z1 = params['z'][0]
z2 = params['z'][1]
x1 = params['x']
y2 = params['y2']
unknowns['y1'] = z1**2 + z2 + x1 - 0.2*y2
def linearize(self, params, unknowns, resids):
J = {}
J['y1','y2'] = -0.2
J['y1','z'] = np.array([[2*params['z'][0], 1.0]])
J['y1','x'] = 1.0
return J
class SellarDis2(Component):
def __init__(self):
super(SellarDis2, self).__init__()
self.add_param('z', val=np.zeros(2))
self.add_param('y1', val=1.0)
self.add_output('y2', val=1.0)
def solve_nonlinear(self, params, unknowns, resids):
z1 = params['z'][0]
z2 = params['z'][1]
y1 = params['y1']
y1 = abs(y1)
unknowns['y2'] = y1**.5 + z1 + z2
def linearize(self, params, unknowns, resids):
J = {}
J['y2', 'y1'] = 0.5*params['y1']**-0.5
J['y2', 'z'] = np.array([[1.0, 1.0]])
return J
class Sellar(Group):
def __init__(self):
super(Sellar, self).__init__()
self.add('px', IndepVarComp('x', 1.0), promotes=['*'])
self.add('pz', IndepVarComp('z', np.array([5.0,2.0])), promotes=['*'])
self.add('d1', SellarDis1(), promotes=['*'])
self.add('d2', SellarDis2(), promotes=['*'])
self.add('obj_cmp', ExecComp('obj = x**2 + z[1] + y1 + exp(-y2)',
z=np.array([0.0, 0.0]), x=0.0, y1=0.0, y2=0.0),
promotes=['*'])
self.add('con_cmp1', ExecComp('con1 = 3.16 - y1'), promotes=['*'])
self.add('con_cmp2', ExecComp('con2 = y2 - 24.0'), promotes=['*'])
self.nl_solver = NLGaussSeidel()
self.nl_solver.options['atol'] = 1.0e-12
self.ln_solver = ScipyGMRES()
def solve_nonlinear(self, params=None, unknowns=None, resids=None, metadata=None):
print("Group's solve_nonlinear was called!!")
# Discipline Optimizer would be called here?
super(Sellar, self).solve_nonlinear(params, unknowns, resids)
class ModifiedProblem(Problem):
def solve_nonlinear(self, params, unknowns, resids):
print("Problem's solve_nonlinear was called!!")
# or here ?
super(ModifiedProblem, self).solve_nonlinear()
top = ModifiedProblem()
top.root = Sellar()
top.driver = ScipyOptimizer()
top.driver.options['optimizer'] = 'SLSQP'
top.driver.add_desvar('z', lower=np.array([-10.0, 0.0]),
upper=np.array([10.0, 10.0]))
top.driver.add_desvar('x', lower=0., upper=10.0)
top.driver.add_objective('obj')
top.driver.add_constraint('con1', upper=0.0)
top.driver.add_constraint('con2', upper=0.0)
top.setup(check=False)
top.run()
的上面的代码的输出是 -
Group's solve_nonlinear was called!!
Group's solve_nonlinear was called!!
Group's solve_nonlinear was called!!
Group's solve_nonlinear was called!!
Group's solve_nonlinear was called!!
Group's solve_nonlinear was called!!
Group's solve_nonlinear was called!!
Optimization terminated successfully. (Exit mode 0)
Current function value: [ 3.18339395]
Iterations: 6
Function evaluations: 6
Gradient evaluations: 6
Optimization Complete
-----------------------------------
这意味着在问题的亚类中定义的solve_nonlinear未在任何时候调用。那么,我应该在Group的子类中调用学科优化器吗?
另外,如何在两个优化问题(系统&学科)之间传递目标变量,特别是将各个学科的优化全局变量返回给系统优化程序。
感谢所有。
您是对的solve_nonlinear
Problem
永远不会被调用,因为Problem
不是OpenMDAO组件,也没有solve_nonlinear
方法。为了在另一个问题中运行子模型问题,你想要做的是将其封装在组件实例中。这将是这个样子:
class SubOptimization(Component)
def __init__(self):
super(SubOptimization, self).__init__()
# Inputs to this subprob
self.add_param('z', val=np.zeros(2))
self.add_param('x', val=0.0)
self.add_param('y2', val=1.0)
# Unknowns for this sub prob
self.add_output('y1', val=1.0)
self.problem = prob = Problem()
prob.root = Group()
prob.add('px', IndepVarComp('x', 1.0), promotes=['*'])
prob.add('d1', SellarDis1(), promotes=['*'])
# TODO - add cons/objs for sub prob
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.add_desvar('x', lower=0., upper=10.0)
prob.driver.add_objective('obj')
prob.driver.add_constraint('con1', upper=0.0)
prob.driver.add_constraint('con2', upper=0.0)
prob.setup()
# Must finite difference across optimizer
self.fd_options['force_fd'] = True
def solve_nonlinear(self, params, unknowns, resids):
prob = self.problem
# Pass values into our problem
prob['x'] = params['x']
prob['z'] = params['z']
prob['y2'] = params['y2']
# Run problem
prob.run()
# Pull values from problem
unknowns['y1'] = prob['y1']
你可以把这个组件到您的主要问题(连同一个学科2个,虽然2并不真的需要一个子优化,因为它没有本地设计变中)并围绕它优化全局设计变量。
一个警告:这不是我已经尝试过的事情(我也没有测试过上述不完整的代码片断),但它应该让你走上正确的轨道。这可能会遇到一个错误,因为这并没有经过太多的测试。当我得到一些时间时,我将为OpenMDAO测试组织一个像这样的CO测试,以确保我们的安全。
谢谢!它的工作完美。 你能解释为什么子优化对于学科2来说不是必需的吗?即使它没有局部设计变量,局部约束仍然需要照顾。 –
你说得对。我很长一段时间没有做过协作优化,并且认为它只是优化'x',但看起来像当地人优化全球'z'设计变量和耦合变量,而外部优化器驱动目标。 –
查看openmdao 2.0的相关答案:https://*.com/questions/42611927/openmdao-co-collaborative-optimization-on-sellar-test-case/48393272#48393272 –