我正在尝试在 OpenMDAO 上实现协作优化和其他多级架构。我读here https://stackoverflow.com/questions/32612456/how-to-setup-multi-level-mdo-formulation-with-openmdao-1-x/32632997#32632997这可以通过在问题子类中定义单独的solve_nonlinear 方法来完成。
问题是，在运行问题实例时，未调用定义的solve_linear。
这是代码 -
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()
上述代码的输出是 -
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Problem子类中定义的solve_nonlinear在任何时候都不会被调用。那么，我应该在 Group 的子类中调用纪律优化器吗？
另外，如何在两个优化问题（系统和学科）之间传递目标变量，特别是将优化的全局变量从各个学科返回到系统优化器。
谢谢大家。
