"""
OpenMDAO Wrapper for pyoptsparse.
pyoptsparse is based on pyOpt, which is an object-oriented framework for
formulating and solving nonlinear constrained optimization problems, with
additional MPI capability. Note: only SNOPT and SLSQP are currently supported.
"""
from __future__ import print_function
import traceback
from six import iterkeys, iteritems
from pyoptsparse import Optimization
from openmdao.core.driver import Driver
from openmdao.util.record_util import create_local_meta, update_local_meta
def _check_imports():
""" Dynamically remove optimizers we don't have
"""
optlist = ['ALPSO', 'CONMIN', 'FSQP', 'IPOPT', 'NLPQLP',
'NSGA2', 'PSQP', 'SLSQP', 'SNOPT', 'NLPY_AUGLAG', 'NOMAD']
for optimizer in optlist[:]:
try:
exec('from pyoptsparse import %s' % optimizer)
except ImportError:
optlist.remove(optimizer)
return optlist
[docs]class pyOptSparseDriver(Driver):
""" Driver wrapper for pyoptsparse. pyoptsparse is based on pyOpt, which
is an object-oriented framework for formulating and solving nonlinear
constrained optimization problems, with additional MPI capability.
pypptsparse has interfaces to the following optimizers:
ALPSO, CONMIN, FSQP, IPOPT, NLPQLP, NSGA2, PSQP, SLSQP,
SNOPT, NLPY_AUGLAG, NOMAD.
Note that some of these are not open source and therefore not included
in the pyoptsparse source code.
pyOptSparseDriver supports the following:
equality_constraints
inequality_constraints
two_sided_constraints
Options
-------
options['exit_flag'] : int(0)
0 for fail, 1 for ok
options['optimizer'] : str('SLSQP')
Name of optimizers to use
options['print_results'] : bool(True)
Print pyOpt results if True
options['pyopt_diff'] : bool(True)
Set to True to let pyOpt calculate the gradient
options['title'] : str('Optimization using pyOpt_sparse')
Title of this optimization run
"""
def __init__(self):
"""Initialize pyopt"""
super(pyOptSparseDriver, self).__init__()
# What we support
self.supports['inequality_constraints'] = True
self.supports['equality_constraints'] = True
self.supports['multiple_objectives'] = False
self.supports['two_sided_constraints'] = True
# TODO: Support these
self.supports['linear_constraints'] = False
self.supports['integer_design_vars'] = False
# User Options
self.options.add_option('optimizer', 'SLSQP', values=_check_imports(),
desc='Name of optimizers to use')
self.options.add_option('title', 'Optimization using pyOpt_sparse',
desc='Title of this optimization run')
self.options.add_option('print_results', True,
desc='Print pyOpt results if True')
self.options.add_option('pyopt_diff', False,
desc='Set to True to let pyOpt calculate the gradient')
# The user places optimizer-specific settings in here.
self.opt_settings = {}
# The user can set a file name here to store history
self.hist_file = None
self.pyopt_solution = None
self.lin_jacs = {}
self.quantities = []
self.metadata = None
self.exit_flag = 0
self._problem = None
self.sparsity = {}
[docs] def run(self, problem):
"""pyOpt execution. Note that pyOpt controls the execution, and the
individual optimizers (i.e., SNOPT) control the iteration.
Args
----
problem : `Problem`
Our parent `Problem`.
"""
self.pyopt_solution = None
rel = problem.root._relevance
# Metadata Setup
self.metadata = create_local_meta(None, self.options['optimizer'])
self.iter_count = 0
update_local_meta(self.metadata, (self.iter_count,))
# Initial Run
problem.root.solve_nonlinear(metadata=self.metadata)
opt_prob = Optimization(self.options['title'], self._objfunc)
# Add all parameters
param_meta = self.get_desvar_metadata()
self.indep_list = indep_list = list(iterkeys(param_meta))
param_vals = self.get_desvars()
for name, meta in iteritems(param_meta):
opt_prob.addVarGroup(name, meta['size'], type='c',
value=param_vals[name],
lower=meta['low'], upper=meta['high'])
opt_prob.finalizeDesignVariables()
# Add all objectives
objs = self.get_objectives()
self.quantities = list(iterkeys(objs))
self.sparsity = {}
for name in objs:
opt_prob.addObj(name)
self.sparsity[name] = self.indep_list
# Calculate and save gradient for any linear constraints.
lcons = self.get_constraints(lintype='linear').values()
if len(lcons) > 0:
self.lin_jacs = problem.calc_gradient(indep_list, lcons,
return_format='dict')
#print("Linear Gradient")
#print(self.lin_jacs)
# Add all equality constraints
econs = self.get_constraints(ctype='eq', lintype='nonlinear')
con_meta = self.get_constraint_metadata()
self.quantities += list(iterkeys(econs))
for name in econs:
size = con_meta[name]['size']
lower = upper = con_meta[name]['equals']
# Sparsify Jacobian via relevance
wrt = rel.relevant[name].intersection(indep_list)
self.sparsity[name] = wrt
if con_meta[name]['linear'] is True:
opt_prob.addConGroup(name, size, lower=lower, upper=upper,
linear=True, wrt=wrt,
jac=self.lin_jacs[name])
else:
opt_prob.addConGroup(name, size, lower=lower, upper=upper,
wrt=wrt)
# Add all inequality constraints
incons = self.get_constraints(ctype='ineq', lintype='nonlinear')
self.quantities += list(iterkeys(incons))
for name in incons:
size = con_meta[name]['size']
# Bounds - double sided is supported
lower = con_meta[name]['lower']
upper = con_meta[name]['upper']
# Sparsify Jacobian via relevance
wrt = rel.relevant[name].intersection(indep_list)
self.sparsity[name] = wrt
if con_meta[name]['linear'] is True:
opt_prob.addConGroup(name, size, upper=upper, lower=lower,
linear=True, wrt=wrt,
jac=self.lin_jacs[name])
else:
opt_prob.addConGroup(name, size, upper=upper, lower=lower,
wrt=wrt)
# Instantiate the requested optimizer
optimizer = self.options['optimizer']
try:
exec('from pyoptsparse import %s' % optimizer)
except ImportError:
msg = "Optimizer %s is not available in this installation." % \
optimizer
raise ImportError(msg)
optname = vars()[optimizer]
opt = optname()
#Set optimization options
for option, value in self.opt_settings.items():
opt.setOption(option, value)
self._problem = problem
# Execute the optimization problem
if self.options['pyopt_diff'] is True:
# Use pyOpt's internal finite difference
fd_step = problem.root.fd_options['step_size']
sol = opt(opt_prob, sens='FD', sensStep=fd_step, storeHistory=self.hist_file)
else:
# Use OpenMDAO's differentiator for the gradient
sol = opt(opt_prob, sens=self._gradfunc, storeHistory=self.hist_file)
self._problem = None
# Print results
if self.options['print_results'] is True:
print(sol)
# Pull optimal parameters back into framework and re-run, so that
# framework is left in the right final state
dv_dict = sol.getDVs()
for name in indep_list:
val = dv_dict[name]
self.set_desvar(name, val)
self.root.solve_nonlinear(metadata=self.metadata)
# Save the most recent solution.
self.pyopt_solution = sol
try:
exit_status = sol.optInform['value']
self.exit_flag = 1
if exit_status > 2: # bad
self.exit_flag = 0
except KeyError: #nothing is here, so something bad happened!
self.exit_flag = 0
def _objfunc(self, dv_dict):
""" Function that evaluates and returns the objective function and
constraints. This function is passed to pyOpt's Optimization object
and is called from its optimizers.
Args
----
dv_dict : dict
Dictionary of design variable values.
Returns
-------
func_dict : dict
Dictionary of all functional variables evaluated at design point.
fail : int
0 for successful function evaluation
1 for unsuccessful function evaluation
"""
fail = 1
func_dict = {}
metadata = self.metadata
system = self.root
try:
for name in self.indep_list:
self.set_desvar(name, dv_dict[name])
# Execute the model
#print("Setting DV")
#print(dv_dict)
self.iter_count += 1
update_local_meta(metadata, (self.iter_count,))
system.solve_nonlinear(metadata=metadata)
# Get the objective function evaluations
for name, obj in iteritems(self.get_objectives()):
func_dict[name] = obj
# Get the constraint evaluations
for name, con in iteritems(self.get_constraints()):
func_dict[name] = con
# Record after getting obj and constraint to assure they have
# been gathered in MPI.
self.recorders.record_iteration(system, metadata)
# Get the double-sided constraint evaluations
#for key, con in iteritems(self.get_2sided_constraints()):
# func_dict[name] = np.array(con.evaluate(self.parent))
fail = 0
except Exception as msg:
tb = traceback.format_exc()
# Exceptions seem to be swallowed by the C code, so this
# should give the user more info than the dreaded "segfault"
print("Exception: %s" % str(msg))
print(70*"=",tb,70*"=")
fail = 1
#print("Functions calculated")
#print(func_dict)
return func_dict, fail
def _gradfunc(self, dv_dict, func_dict):
""" Function that evaluates and returns the gradient of the objective
function and constraints. This function is passed to pyOpt's
Optimization object and is called from its optimizers.
Args
----
dv_dict : dict
Dictionary of design variable values.
func_dict : dict
Dictionary of all functional variables evaluated at design point.
Returns
-------
sens_dict : dict
Dictionary of dictionaries for gradient of each dv/func pair
fail : int
0 for successful function evaluation
1 for unsuccessful function evaluation
"""
fail = 1
sens_dict = {}
try:
sens_dict = self.calc_gradient(dv_dict.keys(), self.quantities,
return_format='dict',
sparsity=self.sparsity)
#for key, value in iteritems(self.lin_jacs):
# sens_dict[key] = value
fail = 0
except Exception as msg:
tb = traceback.format_exc()
# Exceptions seem to be swallowed by the C code, so this
# should give the user more info than the dreaded "segfault"
print("Exception: %s" % str(msg))
print(70*"=",tb,70*"=")
#print("Derivatives calculated")
#print(dv_dict)
#print(sens_dict)
return sens_dict, fail
