def test_opt(self):
# Optimization Object
optProb = Optimization("Paraboloid", objfunc)
# Design Variables
optProb.addVarGroup("x", 1, varType="c", lower=-50.0, upper=50.0, value=0.0)
optProb.addVarGroup("y", 1, varType="c", lower=-50.0, upper=50.0, value=0.0)
# Objective
optProb.addObj("obj")
# Equality Constraint
optProb.addConGroup("con", 1, lower=-15.0, upper=-15.0, wrt=["x", "y"], linear=True, jac=con_jac)
# Check optimization problem:
print(optProb)
test_name = "bugfix_SNOPT_test_opt"
optOptions = {
"Major feasibility tolerance": 1e-1,
"Print file": "{}.out".format(test_name),
"Summary file": "{}_summary.out".format(test_name),
}
# Optimizer
try:
opt = SNOPT(options=optOptions)
except Error:
raise unittest.SkipTest("Optimizer not available: SNOPT")
sol = opt(optProb, sens=sens)
# Check Solution 7.166667, -7.833334
tol = 1e-6
assert_allclose(sol.variables["x"][0].value, 7.166667, atol=tol, rtol=tol)
assert_allclose(sol.variables["y"][0].value, -7.833333, atol=tol, rtol=tol)
def test_opt_bug1(self):
# Due to a new feature, there is a TypeError when you optimize a model without a constraint.
optProb = Optimization("Paraboloid", objfunc_no_con)
# Design Variables
optProb.addVarGroup("x", 1, varType="c", lower=-50.0, upper=50.0, value=0.0)
optProb.addVarGroup("y", 1, varType="c", lower=-50.0, upper=50.0, value=0.0)
# Objective
optProb.addObj("obj")
test_name = "bugfix_SNOPT_bug1"
optOptions = {
"Major feasibility tolerance": 1e-1,
"Print file": f"{test_name}.out",
"Summary file": f"{test_name}_summary.out",
}
# Optimizer
try:
opt = SNOPT(options=optOptions)
except Error as e:
if "There was an error importing" in e.message:
raise unittest.SkipTest("Optimizer not available: SNOPT")
raise e
opt(optProb, sens=sens)
def test_opt(self):
# Optimization Object
optProb = Optimization('Paraboloid', objfunc)
# Design Variables
optProb.addVarGroup('x', 1, type='c', lower=-50.0, upper=50.0, value=0.0)
optProb.addVarGroup('y', 1, type='c', lower=-50.0, upper=50.0, value=0.0)
optProb.finalizeDesignVariables()
# Objective
optProb.addObj('obj')
# Equality Constraint
optProb.addConGroup('con', 1, lower=-15.0, upper=-15.0, wrt=['x', 'y'], linear=True, jac=con_jac)
# Check optimization problem:
print(optProb)
# Optimizer
try:
opt = SNOPT(optOptions = {'Major feasibility tolerance' : 1e-1})
except:
raise unittest.SkipTest('Optimizer not available: SNOPT')
sol = opt(optProb, sens=sens)
# Check Solution 7.166667, -7.833334
self.assertAlmostEqual(sol.variables['x'][0].value, 7.166667, places=6)
self.assertAlmostEqual(sol.variables['y'][0].value, -7.833333, places=6)
def test_sens(self):
termcomp = TerminateComp(max_sens=3)
optProb = Optimization("Paraboloid", termcomp.objfunc)
optProb.addVarGroup("x", 1, type="c", lower=-50.0, upper=50.0, value=0.0)
optProb.addVarGroup("y", 1, type="c", lower=-50.0, upper=50.0, value=0.0)
optProb.finalizeDesignVariables()
optProb.addObj("obj")
optProb.addConGroup("con", 1, lower=-15.0, upper=-15.0, wrt=["x", "y"], linear=True, jac=con_jac)
test_name = "SNOPT_user_termination_sens"
optOptions = {
"Print file": "{}.out".format(test_name),
"Summary file": "{}_summary.out".format(test_name),
}
try:
opt = SNOPT(options=optOptions)
except Error:
raise unittest.SkipTest("Optimizer not available: SNOPT")
sol = opt(optProb, sens=termcomp.sens)
self.assertEqual(termcomp.sens_count, 4)
# Exit code for user requested termination.
self.assertEqual(sol.optInform["value"], 71)
def test_obj(self):
termcomp = TerminateComp(max_obj=2)
optProb = Optimization("Paraboloid", termcomp.objfunc)
optProb.addVarGroup("x",
1,
varType="c",
lower=-50.0,
upper=50.0,
value=0.0)
optProb.addVarGroup("y",
1,
varType="c",
lower=-50.0,
upper=50.0,
value=0.0)
optProb.addObj("obj")
optProb.addConGroup("con",
1,
lower=-15.0,
upper=-15.0,
wrt=["x", "y"],
linear=True,
jac=con_jac)
test_name = "SNOPT_user_termination_obj"
optOptions = {
"Print file": f"{test_name}.out",
"Summary file": f"{test_name}_summary.out",
}
try:
opt = SNOPT(options=optOptions)
except Error as e:
if "There was an error importing" in e.message:
raise unittest.SkipTest("Optimizer not available: SNOPT")
raise e
sol = opt(optProb, sens=termcomp.sens)
self.assertEqual(termcomp.obj_count, 3)
# Exit code for user requested termination.
self.assertEqual(sol.optInform["value"], 71)
def test_opt_bug_print_2con(self):
# Optimization Object
optProb = Optimization("Paraboloid", objfunc_2con)
# Design Variables
optProb.addVarGroup("x", 1, varType="c", lower=-50.0, upper=50.0, value=0.0)
optProb.addVarGroup("y", 1, varType="c", lower=-50.0, upper=50.0, value=0.0)
# Objective
optProb.addObj("obj")
con_jac2 = {}
con_jac2["x"] = -np.ones((2, 1))
con_jac2["y"] = np.ones((2, 1))
con_jac3 = {}
con_jac3["x"] = -np.ones((3, 1))
con_jac3["y"] = np.ones((3, 1))
# Equality Constraint
optProb.addConGroup("con", 2, lower=-15.0, upper=-15.0, wrt=["x", "y"], linear=True, jac=con_jac2)
optProb.addConGroup("con2", 3, lower=-15.0, upper=-15.0, wrt=["x", "y"], linear=True, jac=con_jac3)
# Check optimization problem:
print(optProb)
test_name = "bugfix_SNOPT_bug_print_2con"
optOptions = {
"Major feasibility tolerance": 1e-1,
"Print file": f"{test_name}.out",
"Summary file": f"{test_name}_summary.out",
}
# Optimizer
try:
opt = SNOPT(options=optOptions)
except Error as e:
if "There was an error importing" in e.message:
raise unittest.SkipTest("Optimizer not available: SNOPT")
raise e
sol = opt(optProb, sens=sens)
print(sol)
def test_opt_bug1(self):
# Due to a new feature, there is a TypeError when you optimize a model without a constraint.
optProb = Optimization('Paraboloid', objfunc_no_con)
# Design Variables
optProb.addVarGroup('x', 1, type='c', lower=-50.0, upper=50.0, value=0.0)
optProb.addVarGroup('y', 1, type='c', lower=-50.0, upper=50.0, value=0.0)
optProb.finalizeDesignVariables()
# Objective
optProb.addObj('obj')
# Optimizer
try:
opt = SNOPT(optOptions = {'Major feasibility tolerance' : 1e-1})
except:
raise unittest.SkipTest('Optimizer not available: SNOPT')
sol = opt(optProb, sens=sens)
def test_obj(self):
termcomp = TerminateComp(max_obj=2)
optProb = Optimization('Paraboloid', termcomp.objfunc)
optProb.addVarGroup('x',
1,
type='c',
lower=-50.0,
upper=50.0,
value=0.0)
optProb.addVarGroup('y',
1,
type='c',
lower=-50.0,
upper=50.0,
value=0.0)
optProb.finalizeDesignVariables()
optProb.addObj('obj')
optProb.addConGroup('con',
1,
lower=-15.0,
upper=-15.0,
wrt=['x', 'y'],
linear=True,
jac=con_jac)
try:
opt = SNOPT()
except:
raise unittest.SkipTest('Optimizer not available: SNOPT')
sol = opt(optProb, sens=termcomp.sens)
self.assertEqual(termcomp.obj_count, 3)
# Exit code for user requested termination.
self.assertEqual(sol.optInform['value'][0], 71)
def test_opt_bug_print_2con(self):
# Optimization Object
optProb = Optimization('Paraboloid', objfunc_2con)
# Design Variables
optProb.addVarGroup('x', 1, type='c', lower=-50.0, upper=50.0, value=0.0)
optProb.addVarGroup('y', 1, type='c', lower=-50.0, upper=50.0, value=0.0)
optProb.finalizeDesignVariables()
# Objective
optProb.addObj('obj')
con_jac2 = {}
con_jac2['x'] = -np.ones((2, 1))
con_jac2['y'] = np.ones((2, 1))
con_jac3 = {}
con_jac3['x'] = -np.ones((3, 1))
con_jac3['y'] = np.ones((3, 1))
# Equality Constraint
optProb.addConGroup('con', 2, lower=-15.0, upper=-15.0, wrt=['x', 'y'], linear=True, jac=con_jac2)
optProb.addConGroup('con2', 3, lower=-15.0, upper=-15.0, wrt=['x', 'y'], linear=True, jac=con_jac3)
# Check optimization problem:
print(optProb)
# Optimizer
try:
opt = SNOPT(optOptions = {'Major feasibility tolerance' : 1e-1})
except:
raise unittest.SkipTest('Optimizer not available: SNOPT')
sol = opt(optProb, sens=sens)
print(sol)
if optimize == True:
# optimization setup
optProb = Optimization('VAWT_Power', obj_func)
optProb.addObj('obj')
n = np.size(x0)
optProb.addVarGroup('xvars', n, 'c', lower=xlow, upper=xupp, value=x0)
optProb.addVarGroup('yvars', n, 'c', lower=ylow, upper=yupp, value=y0)
num_cons_sep = (n - 1) * n / 2
optProb.addConGroup('sep', num_cons_sep, lower=0, upper=None)
num_cons_obs = nobs * nwind
optProb.addConGroup('SPL', num_cons_obs, lower=0, upper=SPLlim / 10.)
opt = SNOPT()
opt.setOption('Scale option', 0)
if rotdir_spec == 'cn':
opt.setOption(
'Print file',
basepath + path.sep + 'optimization_results/SNOPT_print_SPL' +
str(SPLlim) + '_turb' + str(n) + '_counterrot.out')
opt.setOption(
'Summary file', basepath + path.sep +
'optimization_results/SNOPT_summary_SPL' + str(SPLlim) +
'_turb' + str(n) + '_counterrot.out')
elif rotdir_spec == 'co':
opt.setOption(
'Print file',
basepath + path.sep + 'optimization_results/SNOPT_print_SPL' +
str(SPLlim) + '_turb' + str(n) + '_corot.out')
jdist = cp.MvNormal(initial_seed, std_dev)
collocation = StochasticCollocation(3, "Normal")
collocation_grad = StochasticCollocation(3,
"Normal",
QoI_dimensions=rv_systemsize)
threshold_factor = 0.9
dominant_space = DimensionReduction(threshold_factor,
n_arnoldi_sample=3,
exact_Hessian=False)
dominant_space.getDominantDirections(QoI, jdist)
# Setup the problem
optProb = Optimization('Paraboloid', objfunc)
lower_bound = -20 * np.ones(rv_systemsize)
upper_bound = 20 * np.ones(rv_systemsize)
optProb.addVarGroup('xvars',
rv_systemsize,
'c',
lower=lower_bound,
upper=upper_bound,
value=10 * np.ones(rv_systemsize))
optProb.addObj('obj')
# Optimizer
opt = SNOPT(optOptions={'Major feasibility tolerance': 1e-6})
sol = opt(optProb, sens=sens)
# Check Solution
import inspect
print(sol.fStar)
print(sol.getDVs()['xvars'])
optProb.addConGroup("con", len(lower), lower=lower, upper=upper)
# And the 2 linear constriants
if USE_LINEAR:
jac = np.zeros((1, 9))
jac[0, 3] = 1.0
jac[0, 2] = -1.0
optProb.addConGroup("lin_con", 1, lower=-0.55, upper=0.55, wrt=["xvars"], jac={"xvars": jac}, linear=True)
# Objective
optProb.addObj("obj")
# Check optimization problem:
print(optProb)
optProb.printSparsity()
# Global Optimizer: ALPSO
opt1 = ALPSO()
# Get first Solution
sol1 = opt1(optProb)
print(sol1)
# Now run the previous solution with SNOPT
opt2 = SNOPT()
sol2 = opt2(sol1)
# Check Solution
print(sol2)
omega_r = 1500 # rpm
R_rotor = 0.705 # m
fuel_cap = 1.0 # kg
theta_hover = 20 # deg
x0 = scale(
[eng_displace, fuel_rate, omega_r, R_rotor, fuel_cap, theta_hover])
# x0 = [ 4.3441622, 0.16620041, 0.10885692, 2.78460696, 6.76453586, 0.85782237]
x0 = [4.61280557, 0.17376677, 0.08200489, 1.5, 7.32432873, 0.86800068]
lb = [0, 0, 0, 0, 0, 0]
ub = scale([1200.0, 1.0, 7500.0, 3.0, 12.0, 30.0])
setPayload(5.0)
# print obj_func_print(x0)
optimizer = SNOPT()
# optimizer = NSGA2()
# optimizer.setOption('maxGen', 200)
xopt, fopt, info = optimize(obj_func, x0, lb, ub, optimizer)
print 'SNOPT:', xopt, fopt, info
out = obj_func_print(xopt)
# Create the pareto frunt
steps = 20
payloads = np.linspace(0.0, 10.0, num=steps)
flight_times = np.zeros(steps)
engine_size = np.zeros(steps)
for i in range(steps):
def optimize(self,
sparse=True,
tol=None,
optOptions={},
storeHistory=False):
# set N
if sparse:
self.N = 50000
else:
self.N = 500
# Optimization Object
optProb = Optimization("large and sparse", self.objfunc)
# Design Variables
optProb.addVar("x", lower=-100, upper=150, value=0)
optProb.addVarGroup("y",
self.N,
lower=-10 - np.arange(self.N),
upper=np.arange(self.N),
value=0)
optProb.addVarGroup("z",
2 * self.N,
upper=np.arange(2 * self.N),
lower=-100 - np.arange(2 * self.N),
value=0)
# Constraints
optProb.addCon("con1", upper=100, wrt=["x"])
optProb.addCon("con2", upper=100)
optProb.addCon("con3", lower=4, wrt=["x", "z"])
xJac = np.ones((self.N, 1))
if sparse:
yJac = scipy.sparse.spdiags(np.ones(self.N), 0, self.N, self.N)
else:
yJac = np.eye(self.N)
optProb.addConGroup(
"lincon",
self.N,
lower=2 - 3 * np.arange(self.N),
linear=True,
wrt=["x", "y"],
jac={
"x": xJac,
"y": yJac
},
)
optProb.addObj("obj")
# Optimizer
try:
opt = SNOPT(options=optOptions)
except Error:
raise unittest.SkipTest("Optimizer not available: SNOPT")
sol = opt(optProb, sens=self.sens)
# Check Solution
if tol is not None:
if opt.version != "7.7.7":
assert_allclose(sol.objectives["obj"].value,
10.0,
atol=tol,
rtol=tol)
else:
assert_allclose(sol.fStar, 10.0, atol=tol, rtol=tol)
assert_allclose(sol.variables["x"][0].value,
2.0,
atol=tol,
rtol=tol)
return sol
# Optimization Object
optProb = Optimization("Paraboloid", objfunc)
# Design Variables
optProb.addVarGroup("x", 1, type="c", lower=-50.0, upper=50.0, value=0.0)
optProb.addVarGroup("y", 1, type="c", lower=-50.0, upper=50.0, value=0.0)
optProb.finalizeDesignVariables()
# Objective
optProb.addObj("obj")
# Equality Constraint
optProb.addConGroup("con",
1,
lower=-15.0,
upper=-15.0,
wrt=["x", "y"],
linear=True,
jac=con_jac)
# Check optimization problem:
print(optProb)
# Optimizer
opt = SNOPT(optOptions={"Major feasibility tolerance": 1e-1})
sol = opt(optProb, sens=sens)
# Check Solution
print(sol)
print("Solution shoud be (x, y) = (7.166667, -7.833334)\n")
optProb.addVarGroup('me',
2,
lower=np.array([-1.0, 0.0]),
upper=np.array([0.0, 0.9]),
value=np.array([-0.5, 0.3])) # , scalar=1E-1)
optProb.addVarGroup('MU',
2,
lower=np.array([0.0, 1.5]),
upper=np.array([1.0, 20.0]),
value=np.array([0.5, 5.5])) # , scalar=1E-1)
optProb.addVarGroup('aU', 1, lower=0.0, upper=20.0,
value=5.0) # , scalar=1E-1)
optProb.addVarGroup('bU', 1, lower=0.0, upper=5.0,
value=1.66) # , scalar=1E-1)
optProb.addVarGroup('cos_spread', 1, lower=0.0, upper=10.0,
value=2.0) # , scalar=1E-1)
# add objective
optProb.addObj('obj', scale=1E0)
# initialize optimizer
snopt = SNOPT(options={'Print file': 'SNOPT_print_tune_all.out'})
# run optimizer
sol = snopt(optProb, sens=None)
# print solution
print sol
# plot fit
tuning_obj_function(xdict=sol.xStar, plot=True)
