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我正在尝试在 OpenMDAO 上实现协作优化和其他多级架构。我在这里读到，这可以通过在问题子类中定义一个单独的 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()

上面代码的输出是 -

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
-----------------------------------

这意味着在任何时候都不会调用 Problem 子类中定义的solve_nonlinear。那么，我应该调用 Group 的 Subclass 中的学科优化器吗？

另外，我如何在两个优化问题（系统和学科）之间传递目标变量，特别是将各个学科的优化全局变量返回给系统优化器。

谢谢大家。

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1 回答 1

2

你是对的，永远不会调用solve_nonlinearon Problem，因为Problem它不是 OpenMDAO 组件并且没有solve_nonlinear方法。为了在另一个问题中运行子模型问题，您想要做的是将其封装在 Component 实例中。它看起来像这样：

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']

您可以将此组件放入您的主要问题中（与规则 2 中的一个一起，尽管 2 并不真正需要子优化，因为它没有局部设计变量）并围绕它优化全局设计变量。

一个警告：这不是我尝试过的（我也没有测试过上面不完整的代码片段），但它应该让你走上正轨。您可能会遇到错误，因为这并没有经过太多测试。当我有时间时，我会为 OpenMDAO 测试进行这样的 CO 测试，以确保我们的安全。

于 2016-02-09T15:18:55.820 回答