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test_pyoptsparse.py
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test_pyoptsparse.py
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""" Testing pyoptsparse."""
import os
import unittest
from six.moves import cStringIO
import numpy as np
from openmdao.api import IndepVarComp, Group, Problem, ExecComp, Component
from openmdao.core.system import AnalysisError
from openmdao.test.paraboloid import Paraboloid
from openmdao.test.simple_comps import SimpleArrayComp, ArrayComp2D
from openmdao.test.util import assert_rel_error, ConcurrentTestCaseMixin, \
set_pyoptsparse_opt
# check that pyoptsparse is installed
# if it is, try to use SNOPT but fall back to SLSQP
OPT, OPTIMIZER = set_pyoptsparse_opt('SNOPT')
if OPTIMIZER:
from openmdao.drivers.pyoptsparse_driver import pyOptSparseDriver
class ParaboloidAE(Component):
""" Evaluates the equation f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3
This version raises an analysis error 50% of the time.
The AE in ParaboloidAE stands for AnalysisError, and is not a
reference to the Don Bluth film Tian A.E."""
def __init__(self):
super(ParaboloidAE, self).__init__()
self.add_param('x', val=0.0)
self.add_param('y', val=0.0)
self.add_output('f_xy', val=0.0)
self.eval_iter_count = 0
self.eval_fail_at = 3
self.grad_iter_count = 0
self.grad_fail_at = 100
self.fail_hard = False
def solve_nonlinear(self, params, unknowns, resids):
"""f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3
Optimal solution (minimum): x = 6.6667; y = -7.3333
"""
if self.eval_iter_count == self.eval_fail_at:
self.eval_iter_count = 0
if self.fail_hard:
raise RuntimeError('This should error.')
else:
raise AnalysisError('Try again.')
x = params['x']
y = params['y']
unknowns['f_xy'] = (x-3.0)**2 + x*y + (y+4.0)**2 - 3.0
self.eval_iter_count += 1
def linearize(self, params, unknowns, resids):
""" Jacobian for our paraboloid."""
if self.grad_iter_count == self.grad_fail_at:
self.grad_iter_count = 0
if self.fail_hard:
raise RuntimeError('This should error.')
else:
raise AnalysisError('Try again.')
x = params['x']
y = params['y']
J = {}
J['f_xy','x'] = 2.0*x - 6.0 + y
J['f_xy','y'] = 2.0*y + 8.0 + x
self.grad_iter_count += 1
return J
class TestPyoptSparse(unittest.TestCase, ConcurrentTestCaseMixin):
def setUp(self):
if OPT is None:
raise unittest.SkipTest("pyoptsparse is not installed")
if OPTIMIZER is None:
raise unittest.SkipTest("pyoptsparse is not providing SNOPT or SLSQP")
self.concurrent_setUp()
def tearDown(self):
self.concurrent_tearDown()
def test_simple_paraboloid_upper(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', IndepVarComp('x', 50.0), promotes=['*'])
root.add('p2', IndepVarComp('y', 50.0), promotes=['*'])
root.add('comp', Paraboloid(), promotes=['*'])
root.add('con', ExecComp('c = - x + y'), promotes=['*'])
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
if OPTIMIZER == 'SLSQP':
prob.driver.opt_settings['ACC'] = 1e-9
prob.driver.options['print_results'] = False
prob.driver.add_desvar('x', lower=-50.0, upper=50.0)
prob.driver.add_desvar('y', lower=-50.0, upper=50.0)
prob.driver.add_objective('f_xy')
prob.driver.add_constraint('c', upper=-15.0)
prob.setup(check=False)
prob.run()
# Minimum should be at (7.166667, -7.833334)
assert_rel_error(self, prob['x'], 7.16667, 1e-6)
assert_rel_error(self, prob['y'], -7.833334, 1e-6)
def test_simple_paraboloid_lower(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', IndepVarComp('x', 50.0), promotes=['*'])
root.add('p2', IndepVarComp('y', 50.0), promotes=['*'])
root.add('comp', Paraboloid(), promotes=['*'])
root.add('con', ExecComp('c = x - y'), promotes=['*'])
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
if OPTIMIZER == 'SLSQP':
prob.driver.opt_settings['ACC'] = 1e-9
prob.driver.options['print_results'] = False
prob.driver.add_desvar('x', lower=-50.0, upper=50.0)
prob.driver.add_desvar('y', lower=-50.0, upper=50.0)
prob.driver.add_objective('f_xy')
prob.driver.add_constraint('c', lower=15.0)
prob.setup(check=False)
prob.run()
# Minimum should be at (7.166667, -7.833334)
assert_rel_error(self, prob['x'], 7.16667, 1e-6)
assert_rel_error(self, prob['y'], -7.833334, 1e-6)
def test_simple_paraboloid_lower_linear(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', IndepVarComp('x', 50.0), promotes=['*'])
root.add('p2', IndepVarComp('y', 50.0), promotes=['*'])
root.add('comp', Paraboloid(), promotes=['*'])
root.add('con', ExecComp('c = x - y'), promotes=['*'])
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
if OPTIMIZER == 'SLSQP':
prob.driver.opt_settings['ACC'] = 1e-9
prob.driver.options['print_results'] = False
prob.driver.add_desvar('x', lower=-50.0, upper=50.0)
prob.driver.add_desvar('y', lower=-50.0, upper=50.0)
prob.driver.add_objective('f_xy')
prob.driver.add_constraint('c', lower=15.0, linear=True)
if OPTIMIZER == 'SNOPT':
# there is currently a bug in SNOPT, it requires at least one
# nonlinear inequality constraint, so provide a 'fake' one
prob.driver.add_constraint('x', lower=-100.0)
prob.setup(check=False)
prob.run()
# Minimum should be at (7.166667, -7.833334)
assert_rel_error(self, prob['x'], 7.16667, 1e-6)
assert_rel_error(self, prob['y'], -7.833334, 1e-6)
def test_simple_paraboloid_equality(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', IndepVarComp('x', 50.0), promotes=['*'])
root.add('p2', IndepVarComp('y', 50.0), promotes=['*'])
root.add('comp', Paraboloid(), promotes=['*'])
root.add('con', ExecComp('c = - x + y'), promotes=['*'])
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
if OPTIMIZER == 'SLSQP':
prob.driver.opt_settings['ACC'] = 1e-9
prob.driver.options['print_results'] = False
prob.driver.add_desvar('x', lower=-50.0, upper=50.0)
prob.driver.add_desvar('y', lower=-50.0, upper=50.0)
prob.driver.add_objective('f_xy')
prob.driver.add_constraint('c', equals=-15.0)
prob.setup(check=False)
prob.run()
# Minimum should be at (7.166667, -7.833334)
assert_rel_error(self, prob['x'], 7.16667, 1e-6)
assert_rel_error(self, prob['y'], -7.833334, 1e-6)
def test_simple_paraboloid_equality_linear(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', IndepVarComp('x', 50.0), promotes=['*'])
root.add('p2', IndepVarComp('y', 50.0), promotes=['*'])
root.add('comp', Paraboloid(), promotes=['*'])
root.add('con', ExecComp('c = - x + y'), promotes=['*'])
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
if OPTIMIZER == 'SLSQP':
prob.driver.opt_settings['ACC'] = 1e-9
prob.driver.options['print_results'] = False
prob.driver.add_desvar('x', lower=-50.0, upper=50.0)
prob.driver.add_desvar('y', lower=-50.0, upper=50.0)
prob.driver.add_objective('f_xy')
prob.driver.add_constraint('c', equals=-15.0, linear=True)
if OPTIMIZER == 'SNOPT':
# there is currently a bug in SNOPT, it requires at least one
# nonlinear inequality constraint, so provide a 'fake' one
prob.driver.add_constraint('x', lower=-100.0)
prob.setup(check=False)
prob.run()
# Minimum should be at (7.166667, -7.833334)
assert_rel_error(self, prob['x'], 7.16667, 1e-6)
assert_rel_error(self, prob['y'], -7.833334, 1e-6)
def test_simple_paraboloid_double_sided_low(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', IndepVarComp('x', 50.0), promotes=['*'])
root.add('p2', IndepVarComp('y', 50.0), promotes=['*'])
root.add('comp', Paraboloid(), promotes=['*'])
root.add('con', ExecComp('c = - x + y'), promotes=['*'])
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
prob.driver.options['print_results'] = False
prob.driver.add_desvar('x', lower=-50.0, upper=50.0)
prob.driver.add_desvar('y', lower=-50.0, upper=50.0)
prob.driver.add_objective('f_xy')
prob.driver.add_constraint('c', lower=-11.0, upper=-10.0)
prob.setup(check=False)
prob.run()
# Minimum should be at (7.166667, -7.833334)
assert_rel_error(self, prob['y'] - prob['x'], -11.0, 1e-6)
def test_simple_paraboloid_double_sided_high(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', IndepVarComp('x', 50.0), promotes=['*'])
root.add('p2', IndepVarComp('y', 50.0), promotes=['*'])
root.add('comp', Paraboloid(), promotes=['*'])
root.add('con', ExecComp('c = x - y'), promotes=['*'])
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
prob.driver.options['print_results'] = False
prob.driver.add_desvar('x', lower=-50.0, upper=50.0)
prob.driver.add_desvar('y', lower=-50.0, upper=50.0)
prob.driver.add_objective('f_xy')
prob.driver.add_constraint('c', lower=10.0, upper=11.0)
prob.setup(check=False)
prob.run()
# Minimum should be at (7.166667, -7.833334)
assert_rel_error(self, prob['x'] - prob['y'], 11.0, 1e-6)
def test_simple_paraboloid_scaled_desvars_fwd(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', IndepVarComp('x', 50.0), promotes=['*'])
root.add('p2', IndepVarComp('y', 50.0), promotes=['*'])
root.add('comp', Paraboloid(), promotes=['*'])
root.add('con', ExecComp('c = x - y'), promotes=['*'])
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
if OPTIMIZER == 'SNOPT':
prob.driver.opt_settings['Verify level'] = 3
prob.driver.options['print_results'] = False
prob.driver.add_desvar('x', lower=-50.0, upper=50.0, scaler=1/50.0)
prob.driver.add_desvar('y', lower=-50.0, upper=50.0, scaler=1/50.0)
prob.driver.add_objective('f_xy')
prob.driver.add_constraint('c', lower=10.0, upper=11.0)
root.ln_solver.options['mode'] = 'fwd'
prob.setup(check=False)
prob.run()
# Minimum should be at (7.166667, -7.833334)
assert_rel_error(self, prob['x'] - prob['y'], 11.0, 1e-6)
def test_simple_paraboloid_scaled_desvars_fd(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', IndepVarComp('x', 50.0), promotes=['*'])
root.add('p2', IndepVarComp('y', 50.0), promotes=['*'])
root.add('comp', Paraboloid(), promotes=['*'])
root.add('con', ExecComp('c = x - y'), promotes=['*'])
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
if OPTIMIZER == 'SNOPT':
prob.driver.opt_settings['Verify level'] = 3
prob.driver.options['print_results'] = False
prob.driver.add_desvar('x', lower=-50.0, upper=50.0, scaler=1/50.0)
prob.driver.add_desvar('y', lower=-50.0, upper=50.0, scaler=1/50.0)
prob.driver.add_objective('f_xy')
prob.driver.add_constraint('c', lower=10.0, upper=11.0)
root.deriv_options['type'] = 'fd'
prob.setup(check=False)
prob.run()
# Minimum should be at (7.166667, -7.833334)
assert_rel_error(self, prob['x'] - prob['y'], 11.0, 1e-6)
def test_simple_paraboloid_scaled_desvars_rev(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', IndepVarComp('x', 50.0), promotes=['*'])
root.add('p2', IndepVarComp('y', 50.0), promotes=['*'])
root.add('comp', Paraboloid(), promotes=['*'])
root.add('con', ExecComp('c = x - y'), promotes=['*'])
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
if OPTIMIZER == 'SNOPT':
prob.driver.opt_settings['Verify level'] = 3
prob.driver.options['print_results'] = False
prob.driver.add_desvar('x', lower=-50.0, upper=50.0, scaler=1/50.0)
prob.driver.add_desvar('y', lower=-50.0, upper=50.0, scaler=1/50.0)
prob.driver.add_objective('f_xy')
prob.driver.add_constraint('c', lower=10.0, upper=11.0)
root.ln_solver.options['mode'] = 'rev'
prob.setup(check=False)
prob.run()
# Minimum should be at (7.166667, -7.833334)
assert_rel_error(self, prob['x'] - prob['y'], 11.0, 1e-6)
def test_simple_paraboloid_scaled_constraint_fwd(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', IndepVarComp('x', 50.0), promotes=['*'])
root.add('p2', IndepVarComp('y', 50.0), promotes=['*'])
root.add('comp', Paraboloid(), promotes=['*'])
root.add('con', ExecComp('c = x - y'), promotes=['*'])
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
if OPTIMIZER == 'SNOPT':
prob.driver.opt_settings['Verify level'] = 3
prob.driver.options['print_results'] = False
prob.driver.add_desvar('x', lower=-50.0, upper=50.0)
prob.driver.add_desvar('y', lower=-50.0, upper=50.0)
prob.driver.add_objective('f_xy')
prob.driver.add_constraint('c', lower=10.0, upper=11.0, scaler=1/10.)
root.ln_solver.options['mode'] = 'fwd'
prob.setup(check=False)
prob.run()
# Minimum should be at (7.166667, -7.833334)
assert_rel_error(self, prob['x'] - prob['y'], 11.0, 1e-6)
def test_simple_paraboloid_scaled_constraint_fd(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', IndepVarComp('x', 50.0), promotes=['*'])
root.add('p2', IndepVarComp('y', 50.0), promotes=['*'])
root.add('comp', Paraboloid(), promotes=['*'])
root.add('con', ExecComp('c = x - y'), promotes=['*'])
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
if OPTIMIZER == 'SNOPT':
prob.driver.opt_settings['Verify level'] = 3
prob.driver.options['print_results'] = False
prob.driver.add_desvar('x', lower=-50.0, upper=50.0)
prob.driver.add_desvar('y', lower=-50.0, upper=50.0)
prob.driver.add_objective('f_xy')
prob.driver.add_constraint('c', lower=10.0, upper=11.0, scaler=1/10.)
root.deriv_options['type'] = 'fd'
prob.setup(check=False)
prob.run()
# Minimum should be at (7.166667, -7.833334)
assert_rel_error(self, prob['x'] - prob['y'], 11.0, 1e-6)
def test_simple_paraboloid_scaled_constraint_rev(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', IndepVarComp('x', 50.0), promotes=['*'])
root.add('p2', IndepVarComp('y', 50.0), promotes=['*'])
root.add('comp', Paraboloid(), promotes=['*'])
root.add('con', ExecComp('c = x - y'), promotes=['*'])
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
if OPTIMIZER == 'SNOPT':
prob.driver.opt_settings['Verify level'] = 3
prob.driver.options['print_results'] = False
prob.driver.add_desvar('x', lower=-50.0, upper=50.0)
prob.driver.add_desvar('y', lower=-50.0, upper=50.0)
prob.driver.add_objective('f_xy')
prob.driver.add_constraint('c', lower=10.0, upper=11.0, scaler=1/10.)
root.ln_solver.options['mode'] = 'rev'
prob.setup(check=False)
prob.run()
# Minimum should be at (7.166667, -7.833334)
assert_rel_error(self, prob['x'] - prob['y'], 11.0, 1e-6)
def test_simple_paraboloid_scaled_objective_fwd(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', IndepVarComp('x', 50.0), promotes=['*'])
root.add('p2', IndepVarComp('y', 50.0), promotes=['*'])
root.add('comp', Paraboloid(), promotes=['*'])
root.add('con', ExecComp('c = x - y'), promotes=['*'])
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
if OPTIMIZER == 'SNOPT':
prob.driver.opt_settings['Verify level'] = 3
prob.driver.options['print_results'] = False
prob.driver.add_desvar('x', lower=-50.0, upper=50.0)
prob.driver.add_desvar('y', lower=-50.0, upper=50.0)
prob.driver.add_objective('f_xy', scaler=1/10.)
prob.driver.add_constraint('c', lower=10.0, upper=11.0)
root.ln_solver.options['mode'] = 'fwd'
prob.setup(check=False)
prob.run()
# Minimum should be at (7.166667, -7.833334)
assert_rel_error(self, prob['x'] - prob['y'], 11.0, 1e-6)
def test_simple_paraboloid_scaled_objective_rev(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', IndepVarComp('x', 50.0), promotes=['*'])
root.add('p2', IndepVarComp('y', 50.0), promotes=['*'])
root.add('comp', Paraboloid(), promotes=['*'])
root.add('con', ExecComp('c = x - y'), promotes=['*'])
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
if OPTIMIZER == 'SNOPT':
prob.driver.opt_settings['Verify level'] = 3
prob.driver.options['print_results'] = False
prob.driver.add_desvar('x', lower=-50.0, upper=50.0)
prob.driver.add_desvar('y', lower=-50.0, upper=50.0)
prob.driver.add_objective('f_xy', scaler=1/10.)
prob.driver.add_constraint('c', lower=10.0, upper=11.0)
root.ln_solver.options['mode'] = 'rev'
prob.setup(check=False)
prob.run()
# Minimum should be at (7.166667, -7.833334)
assert_rel_error(self, prob['x'] - prob['y'], 11.0, 1e-6)
def test_simple_array_comp(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', IndepVarComp('x', np.zeros([2])), promotes=['*'])
root.add('comp', SimpleArrayComp(), promotes=['*'])
root.add('con', ExecComp('c = y - 20.0', c=np.array([0.0, 0.0]), y=np.array([0.0, 0.0])), promotes=['*'])
root.add('obj', ExecComp('o = y[0]', y=np.array([0.0, 0.0])), promotes=['*'])
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
prob.driver.options['print_results'] = False
prob.driver.add_desvar('x', lower=-50.0, upper=50.0)
prob.driver.add_objective('o')
prob.driver.add_constraint('c', equals=0.0)
prob.setup(check=False)
prob.run()
obj = prob['o']
assert_rel_error(self, obj, 20.0, 1e-6)
def test_simple_array_comp2D(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', IndepVarComp('x', np.zeros((2, 2))), promotes=['*'])
root.add('comp', ArrayComp2D(), promotes=['*'])
root.add('con', ExecComp('c = y - 20.0', c=np.zeros((2, 2)), y=np.zeros((2, 2))), promotes=['*'])
root.add('obj', ExecComp('o = y[0, 0]', y=np.zeros((2, 2))), promotes=['*'])
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
prob.driver.options['print_results'] = False
prob.driver.add_desvar('x', lower=-50.0, upper=50.0)
prob.driver.add_objective('o')
prob.driver.add_constraint('c', equals=0.0)
prob.setup(check=False)
prob.run()
obj = prob['o']
assert_rel_error(self, obj, 20.0, 1e-6)
def test_simple_array_comp2D_array_lo_hi(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', IndepVarComp('x', np.zeros((2, 2))), promotes=['*'])
root.add('comp', ArrayComp2D(), promotes=['*'])
root.add('con', ExecComp('c = y - 20.0', c=np.zeros((2, 2)), y=np.zeros((2, 2))), promotes=['*'])
root.add('obj', ExecComp('o = y[0, 0]', y=np.zeros((2, 2))), promotes=['*'])
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
prob.driver.options['print_results'] = False
prob.driver.add_desvar('x', lower=-50.0*np.ones((2, 2)), upper=50.0*np.ones((2, 2)))
prob.driver.add_objective('o')
prob.driver.add_constraint('c', equals=0.0)
prob.setup(check=False)
prob.run()
obj = prob['o']
assert_rel_error(self, obj, 20.0, 1e-6)
def test_fan_out(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', IndepVarComp('x', 1.0))
root.add('p2', IndepVarComp('x', 1.0))
root.add('comp1', ExecComp('y = 3.0*x'))
root.add('comp2', ExecComp('y = 5.0*x'))
root.add('obj', ExecComp('o = i1 + i2'))
root.add('con1', ExecComp('c = 15.0 - x'))
root.add('con2', ExecComp('c = 15.0 - x'))
# hook up explicitly
root.connect('p1.x', 'comp1.x')
root.connect('p2.x', 'comp2.x')
root.connect('comp1.y', 'obj.i1')
root.connect('comp2.y', 'obj.i2')
root.connect('comp1.y', 'con1.x')
root.connect('comp2.y', 'con2.x')
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
prob.driver.options['print_results'] = False
prob.driver.add_desvar('p1.x', lower=-50.0, upper=50.0)
prob.driver.add_desvar('p2.x', lower=-50.0, upper=50.0)
prob.driver.add_objective('obj.o')
prob.driver.add_constraint('con1.c', equals=0.0)
prob.driver.add_constraint('con2.c', equals=0.0)
prob.setup(check=False)
prob.run()
obj = prob['obj.o']
assert_rel_error(self, obj, 30.0, 1e-6)
# Verify that pyOpt has the correct wrt names
con1 = prob.driver.pyopt_solution.constraints['con1.c']
self.assertEqual(con1.wrt, ['p1.x'])
con2 = prob.driver.pyopt_solution.constraints['con2.c']
self.assertEqual(con2.wrt, ['p2.x'])
def test_sparsity_fwd(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', IndepVarComp('x', 1.0))
root.add('p2', IndepVarComp('x', 1.0))
root.add('comp1', ExecComp('y = 3.0*x'))
root.add('comp2', ExecComp('y = 5.0*x'))
root.add('obj', ExecComp('o = i1 + i2'))
root.add('con1', ExecComp('c = 15.0 - x'))
root.add('con2', ExecComp('c = 15.0 - x'))
# hook up explicitly
root.connect('p1.x', 'comp1.x')
root.connect('p2.x', 'comp2.x')
root.connect('comp1.y', 'obj.i1')
root.connect('comp2.y', 'obj.i2')
root.connect('comp1.y', 'con1.x')
root.connect('comp2.y', 'con2.x')
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
prob.driver.options['print_results'] = False
prob.driver.add_desvar('p1.x', lower=-50.0, upper=50.0)
prob.driver.add_desvar('p2.x', lower=-50.0, upper=50.0)
prob.driver.add_objective('obj.o')
prob.driver.add_constraint('con1.c', equals=0.0)
prob.driver.add_constraint('con2.c', equals=0.0)
prob.setup(check=False)
prob.run()
# Verify that the appropriate sparsity pattern is applied
dv_dict = {'p1.x': 1.0, 'p2.x': 1.0}
prob.driver._problem = prob
sens_dict, fail = prob.driver._gradfunc(dv_dict, {})
self.assertTrue('p2.x' not in sens_dict['con1.c'])
self.assertTrue('p1.x' in sens_dict['con1.c'])
self.assertTrue('p2.x' in sens_dict['con2.c'])
self.assertTrue('p1.x' not in sens_dict['con2.c'])
self.assertTrue('p1.x' in sens_dict['obj.o'])
self.assertTrue('p2.x' in sens_dict['obj.o'])
def test_sparsity_rev(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', IndepVarComp('x', 1.0))
root.add('p2', IndepVarComp('x', 1.0))
root.add('comp1', ExecComp('y = 3.0*x'))
root.add('comp2', ExecComp('y = 5.0*x'))
root.add('obj', ExecComp('o = i1 + i2'))
root.add('con1', ExecComp('c = 15.0 - x'))
root.add('con2', ExecComp('c = 15.0 - x'))
# hook up explicitly
root.connect('p1.x', 'comp1.x')
root.connect('p2.x', 'comp2.x')
root.connect('comp1.y', 'obj.i1')
root.connect('comp2.y', 'obj.i2')
root.connect('comp1.y', 'con1.x')
root.connect('comp2.y', 'con2.x')
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
prob.driver.options['print_results'] = False
prob.driver.add_desvar('p1.x', lower=-50.0, upper=50.0)
prob.driver.add_desvar('p2.x', lower=-50.0, upper=50.0)
prob.driver.add_objective('obj.o')
prob.driver.add_constraint('con1.c', equals=0.0)
prob.driver.add_constraint('con2.c', equals=0.0)
prob.root.ln_solver.options['mode'] = 'rev'
prob.setup(check=False)
prob.run()
# Verify that the appropriate sparsity pattern is applied
dv_dict = {'p1.x': 1.0, 'p2.x': 1.0}
prob.driver._problem = prob
sens_dict, fail = prob.driver._gradfunc(dv_dict, {})
self.assertTrue('p2.x' not in sens_dict['con1.c'])
self.assertTrue('p1.x' in sens_dict['con1.c'])
self.assertTrue('p2.x' in sens_dict['con2.c'])
self.assertTrue('p1.x' not in sens_dict['con2.c'])
self.assertTrue('p1.x' in sens_dict['obj.o'])
self.assertTrue('p2.x' in sens_dict['obj.o'])
def test_sparsity_fd(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', IndepVarComp('x', 1.0))
root.add('p2', IndepVarComp('x', 1.0))
root.add('comp1', ExecComp('y = 3.0*x'))
root.add('comp2', ExecComp('y = 5.0*x'))
root.add('obj', ExecComp('o = i1 + i2'))
root.add('con1', ExecComp('c = 15.0 - x'))
root.add('con2', ExecComp('c = 15.0 - x'))
# hook up explicitly
root.connect('p1.x', 'comp1.x')
root.connect('p2.x', 'comp2.x')
root.connect('comp1.y', 'obj.i1')
root.connect('comp2.y', 'obj.i2')
root.connect('comp1.y', 'con1.x')
root.connect('comp2.y', 'con2.x')
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
prob.driver.options['print_results'] = False
prob.driver.add_desvar('p1.x', lower=-50.0, upper=50.0)
prob.driver.add_desvar('p2.x', lower=-50.0, upper=50.0)
prob.driver.add_objective('obj.o')
prob.driver.add_constraint('con1.c', equals=0.0)
prob.driver.add_constraint('con2.c', equals=0.0)
prob.root.deriv_options['type'] = 'fd'
prob.setup(check=False)
prob.run()
# Verify that the appropriate sparsity pattern is applied
dv_dict = {'p1.x': 1.0, 'p2.x': 1.0}
prob.driver._problem = prob
sens_dict, fail = prob.driver._gradfunc(dv_dict, {})
self.assertTrue('p2.x' not in sens_dict['con1.c'])
self.assertTrue('p1.x' in sens_dict['con1.c'])
self.assertTrue('p2.x' in sens_dict['con2.c'])
self.assertTrue('p1.x' not in sens_dict['con2.c'])
self.assertTrue('p1.x' in sens_dict['obj.o'])
self.assertTrue('p2.x' in sens_dict['obj.o'])
def test_inf_as_desvar_bounds(self):
# User may use np.inf as a bound. It is unneccessary, but the user
# may do it anyway, so make sure SLSQP doesn't blow up with it (bug
# reported by rfalck)
prob = Problem()
root = prob.root = Group()
root.add('p1', IndepVarComp('x', 50.0), promotes=['*'])
root.add('p2', IndepVarComp('y', 50.0), promotes=['*'])
root.add('comp', Paraboloid(), promotes=['*'])
root.add('con', ExecComp('c = - x + y'), promotes=['*'])
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.opt_settings['ACC'] = 1e-9
prob.driver.options['print_results'] = False
prob.driver.add_desvar('x', lower=-np.inf, upper=np.inf)
prob.driver.add_desvar('y', lower=-50.0, upper=50.0)
prob.driver.add_objective('f_xy')
prob.driver.add_constraint('c', upper=-15.0)
prob.setup(check=False)
prob.run()
# Minimum should be at (7.166667, -7.833334)
assert_rel_error(self, prob['x'], 7.16667, 1e-6)
assert_rel_error(self, prob['y'], -7.833334, 1e-6)
def test_sub_sparsity(self):
class SegmentComp(Component):
"""
Each segment accepts y-values of the points within that segments, and computes
the distance from each point to the origin, and the total area under the segment.
"""
def __init__(self, M, x0, x1, index):
super(SegmentComp, self).__init__()
self.index = index
self.M = M
self.x0 = x0
self.x1 = x1
self.deriv_options['type'] = 'fd'
self.x_i = np.linspace(self.x0, self.x1, M)
self.add_param(name='y_i', shape=(M, ),
desc='y-values of each point in the segment')
self.add_output(name='r_i', shape=(M, ),
desc='distance from each point in the segment to (pi,0)')
self.add_output(name='area', val=0.0,
desc='area under the curve approximated by the trapezodal rule')
def solve_nonlinear(self, params, unknowns, resids):
x_i = self.x_i
y_i = params['y_i']
unknowns['r_i'] = np.sqrt( x_i**2 + y_i**2 )
from scipy.integrate import simps
unknowns['area'] = simps(y_i, x_i)
class SumAreaComp(Component):
"""
SumAreaComp takes the area under each segment and sums them to
compute the total area.
"""
def __init__(self, N):
super(SumAreaComp, self).__init__()
self.N = N
self.deriv_options['type'] = 'fd'
for i in range(N):
self.add_param(name='area{0}'.format(i), val=0.0)
self.add_output(name='area', val=0.0, desc='total area')
def solve_nonlinear(self, params, unknowns, resids):
unknowns['area'] = 0.0
for i in range(self.N):
unknowns['area'] += params['area{0}'.format(i)]
N = M = 10
prob = Problem(root=Group())
root = prob.root
b = np.linspace(-np.pi, np.pi, N+1) # segment boundaries
num_unique_points = N*M-(N-1)
root.add(name='ivc',
system=IndepVarComp(name='y_i', val=np.zeros((num_unique_points, ))),
promotes=['y_i'])
root.add(name='sum_area', system=SumAreaComp(N))
yi_index = 0
segments = []
for i in range(N):
seg_name = 'seg{0}'.format(i)
segments.append(SegmentComp(M,b[i], b[i+1], i))
root.add(name=seg_name, system=segments[i])
root.connect('{0}.area'.format(seg_name), 'sum_area.area{0}'.format(i))
yindices = np.arange(yi_index, yi_index+M, dtype=int)
root.connect('y_i', '{0}.y_i'.format(seg_name), src_indices=yindices)
yi_index = yi_index+M-1
prob.driver = driver = pyOptSparseDriver()
driver.options['optimizer'] = OPTIMIZER
driver.options['print_results'] = False
driver.add_desvar(name="y_i",
lower=-100.0,
upper=100.0,
scaler=1.0,
adder=0.0)
for i in range(N):
driver.add_constraint(name='seg{0:d}.r_i'.format(i),
lower=0,
upper=np.pi,
scaler=1.0,
adder=0.0)
driver.add_objective(name='sum_area.area',scaler=-1.0)
prob.setup(check=False)
prob['y_i'] = np.linspace(0, 2*np.pi,num_unique_points)
prob.run()
assert_rel_error(self, 15.4914, prob['sum_area.area'], 1e-4)
sub_sparsity = prob.driver.sub_sparsity
self.assertEquals(len(sub_sparsity['seg0.r_i']['y_i']), 10)
def test_sub_sparsity_sparse_and_full_in_parallel(self):
class SegmentComp(Component):
"""
Each segment accepts y-values of the points within that segments, and computes
the distance from each point to the origin, and the total area under the segment.
"""
def __init__(self, M, x0, x1, index):
super(SegmentComp, self).__init__()
self.index = index
self.M = M
self.x0 = x0
self.x1 = x1
self.deriv_options['type'] = 'fd'
self.x_i = np.linspace(self.x0, self.x1, M)
self.add_param(name='y_i', shape=(M, ),
desc='y-values of each point in the segment')
self.add_output(name='r_i', shape=(M, ),
desc='distance from each point in the segment to (pi,0)')
self.add_output(name='area', val=0.0,
desc='area under the curve approximated by the trapezodal rule')
def solve_nonlinear(self, params, unknowns, resids):
x_i = self.x_i
y_i = params['y_i']
unknowns['r_i'] = np.sqrt( x_i**2 + y_i**2 )
from scipy.integrate import simps
unknowns['area'] = simps(y_i, x_i)
class SumAreaComp(Component):
"""
SumAreaComp takes the area under each segment and sums them to
compute the total area.
"""
def __init__(self, N, NNN):
super(SumAreaComp, self).__init__()
self.N = N
self.deriv_options['type'] = 'fd'
for i in range(N):
self.add_param(name='area{0}'.format(i), val=0.0)
self.add_output(name='area', val=0.0, desc='total area')
self.add_output(name='fake', val=0.0, desc='total area')
self.add_param('bypass', shape=(NNN, ), desc='Param bypass')
def solve_nonlinear(self, params, unknowns, resids):
unknowns['area'] = 0.0
for i in range(self.N):
unknowns['area'] += params['area{0}'.format(i)]
unknowns['area'] += np.sum(params['bypass'])
N = M = 5
prob = Problem(root=Group())
root = prob.root
b = np.linspace(-np.pi, np.pi, N+1) # segment boundaries
num_unique_points = N*M-(N-1)