def test_clear_dir_dot_sens(self):
m = simple_model_1()
sens = SensitivityInterface(m, clone_model=False)
k_aug = K_augInterface()
opt_ipopt.solve(m, tee=True)
m.ptb = pyo.Param(mutable=True, initialize=1.5)
cwd = os.getcwd()
dir_contents = os.listdir(cwd)
sens_param = [m.p]
sens.setup_sensitivity(sens_param)
# Call k_aug
k_aug.k_aug(m, tee=True)
self.assertIsInstance(k_aug.data["dsdp_in_.in"], str)
sens.perturb_parameters([m.ptb])
# Call dot_sens. In the process, we re-write dsdp_in_.in
k_aug.dot_sens(m, tee=True)
# Make sure we get the values we expect. This problem is easy enough
# to solve by hand:
# x = [1, 1, -2] = [v1, v2, dual]
# Sensitivity system:
# | 2 -2 1 |
# |-2 2 1 | dx/dp = -[dL/dxdp, dc/dp]^T = -[0, 0, -1]^T
# | 1 1 0 |
# => dx/dp = [0.5, 0.5, 0]^T
# Here, dp = [0.5]
# => dx = [0.25, 0.25, 0]^T
# => x_new = [1.25, 1.25, -2]
self.assertAlmostEqual(m.v1.value, 1.25, 7)
self.assertAlmostEqual(m.v2.value, 1.25, 7)
# We are back in our working directory
self.assertEqual(cwd, os.getcwd())
# The contents of this directory have not changed
self.assertEqual(dir_contents, os.listdir(cwd))
self.assertFalse(os.path.exists("dsdp_in_.in"))
self.assertFalse(os.path.exists("delta_p.out"))
self.assertFalse(os.path.exists("dot_out.out"))
self.assertFalse(os.path.exists("timings_dot_driver_dsdp.txt"))
# And we have saved strings of the file contents.
self.assertIsInstance(k_aug.data["dsdp_in_.in"], str)
self.assertIsInstance(k_aug.data["delta_p.out"], str)
self.assertIsInstance(k_aug.data["dot_out.out"], str)
self.assertIsInstance(k_aug.data["timings_dot_driver_dsdp.txt"], str)
def sensitivity_calculation(method,
instance,
paramList,
perturbList,
cloneModel=True,
tee=False,
keepfiles=False,
solver_options=None):
"""This function accepts a Pyomo ConcreteModel, a list of parameters, and
their corresponding perturbation list. The model is then augmented with
dummy constraints required to call sipopt or k_aug to get an approximation
of the perturbed solution.
Parameters
----------
method: string
'sipopt' or 'k_aug'
instance: Block
pyomo block or model object
paramSubList: list
list of mutable parameters or fixed variables
perturbList: list
list of perturbed parameter values
cloneModel: bool, optional
indicator to clone the model. If set to False, the original
model will be altered
tee: bool, optional
indicator to stream solver log
keepfiles: bool, optional
preserve solver interface files
solver_options: dict, optional
Provides options to the solver (also the name of an attribute)
Returns
-------
The model that was manipulated by the sensitivity interface
"""
sens = SensitivityInterface(instance, clone_model=cloneModel)
sens.setup_sensitivity(paramList)
m = sens.model_instance
if method not in {"k_aug", "sipopt"}:
raise ValueError("Only methods 'k_aug' and 'sipopt' are supported'")
if method == 'k_aug':
k_aug = SolverFactory('k_aug', solver_io='nl')
dot_sens = SolverFactory('dot_sens', solver_io='nl')
ipopt = SolverFactory('ipopt', solver_io='nl')
k_aug_interface = K_augInterface(k_aug=k_aug, dot_sens=dot_sens)
ipopt.solve(m, tee=tee)
m.ipopt_zL_in.update(m.ipopt_zL_out) #: important!
m.ipopt_zU_in.update(m.ipopt_zU_out) #: important!
k_aug.options['dsdp_mode'] = "" #: sensitivity mode!
k_aug_interface.k_aug(m, tee=tee)
sens.perturb_parameters(perturbList)
if method == 'sipopt':
ipopt_sens = SolverFactory('ipopt_sens', solver_io='nl')
ipopt_sens.options['run_sens'] = 'yes'
# Send the model to ipopt_sens and collect the solution
results = ipopt_sens.solve(m, keepfiles=keepfiles, tee=tee)
elif method == 'k_aug':
dot_sens.options["dsdp_mode"] = ""
k_aug_interface.dot_sens(m, tee=tee)
return m