Arrays 添加一个<=X<=常数a、b和数组X的b约束
我想约束(不绑定)设计变量Arrays 添加一个<=X<=常数a、b和数组X的b约束,arrays,openmdao,Arrays,Openmdao,我想约束(不绑定)设计变量z,这样每个条目都被约束为小于或等于5,大于或等于-5:-5基本问题是,除非您另有说明,否则ExecComp假设所有内容都是标量。因此,如果你给它一些大小信息,你可以使你的代码工作 self.add('con_cmp1',ExecComp('con1=z',inits={'z':np.zeros(2),'con1':np.zeros(2)}),promoted=['z',con1']) 然而,实际上您根本不需要ExecComp。可以像这样直接约束设计变量: top.dr
z
,这样每个条目都被约束为小于或等于5
,大于或等于-5
:-5基本问题是,除非您另有说明,否则ExecComp假设所有内容都是标量。因此,如果你给它一些大小信息,你可以使你的代码工作
self.add('con_cmp1',ExecComp('con1=z',inits={'z':np.zeros(2),'con1':np.zeros(2)}),promoted=['z',con1'])
然而,实际上您根本不需要ExecComp。可以像这样直接约束设计变量:
top.driver.add_约束('z',lower=np.array([-5.,-5.]),upper=np.array([5,5.])
基本问题是,除非您另有说明,否则ExecComp假定所有内容都是标量。因此,如果你给它一些大小信息,你可以使你的代码工作
self.add('con_cmp1',ExecComp('con1=z',inits={'z':np.zeros(2),'con1':np.zeros(2)}),promoted=['z',con1'])
然而,实际上您根本不需要ExecComp。可以像这样直接约束设计变量:
top.driver.add_约束('z',lower=np.array([-5.,-5.]),upper=np.array([5,5.])
from __future__ import print_function
from openmdao.api import ExecComp, IndepVarComp, Group, NLGaussSeidel, \
ScipyGMRES, Problem, ScipyOptimizer
import numpy as np
from openmdao.api import Component
class SellarDis1(Component):
"""Component containing Discipline 1."""
def __init__(self):
super(SellarDis1, self).__init__()
# Global Design Variable
self.add_param('z', val=np.zeros(2))
# Local Design Variable
self.add_param('x', val=0.)
# Coupling parameter
self.add_param('y2', val=1.0)
# Coupling output
self.add_output('y1', val=1.0)
def solve_nonlinear(self, params, unknowns, resids):
"""Evaluates the equation
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):
""" Jacobian for Sellar discipline 1."""
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):
"""Component containing Discipline 2."""
def __init__(self):
super(SellarDis2, self).__init__()
# Global Design Variable
self.add_param('z', val=np.zeros(2))
# Coupling parameter
self.add_param('y1', val=1.0)
# Coupling output
self.add_output('y2', val=1.0)
def solve_nonlinear(self, params, unknowns, resids):
"""Evaluates the equation
y2 = y1**(.5) + z1 + z2"""
z1 = params['z'][0]
z2 = params['z'][1]
y1 = params['y1']
# Note: this may cause some issues. However, y1 is constrained to be
# above 3.16, so lets just let it converge, and the optimizer will
# throw it out
y1 = abs(y1)
unknowns['y2'] = y1**.5 + z1 + z2
def linearize(self, params, unknowns, resids):
""" Jacobian for Sellar discipline 2."""
J = {}
J['y2', 'y1'] = .5*params['y1']**-.5
#Extra set of brackets below ensure we have a 2D array instead of a 1D array
# for the Jacobian; Note that Jacobian is 2D (num outputs x num inputs).
J['y2', 'z'] = np.array([[1.0, 1.0]])
return J
class SellarDerivatives(Group):
""" Group containing the Sellar MDA. This version uses the disciplines
with derivatives."""
def __init__(self):
super(SellarDerivatives, self).__init__()
self.add('px', IndepVarComp('x', 1.0), promotes=['x'])
self.add('pz', IndepVarComp('z', np.array([5.0, 2.0])), promotes=['z'])
self.add('d1', SellarDis1(), promotes=['z', 'x', 'y1', 'y2'])
self.add('d2', SellarDis2(), promotes=['z', 'y1', 'y2'])
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=['obj', 'z', 'x', 'y1', 'y2'])
self.add('con_cmp1', ExecComp('con1 = z'), promotes=['z', 'con1'])
self.nl_solver = NLGaussSeidel()
self.nl_solver.options['atol'] = 1.0e-12
self.ln_solver = ScipyGMRES()
top = Problem()
top.root = SellarDerivatives()
top.driver = ScipyOptimizer()
top.driver.options['optimizer'] = 'SLSQP'
top.driver.options['tol'] = 1.0e-8
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=0.0, upper=10.0)
top.driver.add_objective('obj')
top.driver.add_constraint('con1', lower=np.array([-5., -5.]), upper=np.array([5.,5.]))
top.setup()
# Setting initial values for design variables
top['x'] = 1.0
top['z'] = np.array([5.0, 2.0])
top.run()
print("\n")
print( "Minimum found at (%f, %f, %f)" % (top['z'][0], \
top['z'][1], \
top['x']))
print("Coupling vars: %f, %f" % (top['y1'], top['y2']))
print("Minimum objective: ", top['obj'])