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optimization.py
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"""
Framework interface to pyOptSparse
John Hwang, March 2014
"""
from __future__ import division
from pyoptsparse import Optimization as OptProblem
from pyoptsparse import OPT as Optimizer
import numpy
class Optimization(object):
""" Automatically sets up and runs an optimization """
def __init__(self, system):
""" Takes system containing all DVs and outputs """
self._system = system
self._variables = {'dv': {}, 'func': {}}
self.sens_callback = None
def _get_name(self, var_id):
""" Returns unique string for the variable """
return var_id[0] + '_' + str(var_id[1])
def _add_var(self, typ, var, value=0.0,
lower=None, upper=None,
get_jacs=None, linear=False):
""" Wrapped by next three methods """
var_id = self._system.get_id(var)
var_name = self._get_name(var_id)
self._variables[typ][var_name] = {'ID': var_id,
'value': value,
'lower': lower,
'upper': upper,
'get_jacs': get_jacs,
'linear': linear}
def add_design_variable(self, var, value=0.0,
lower=None, upper=None):
""" Self-explanatory; part of API """
self._add_var('dv', var, value=value,
lower=lower, upper=upper)
def add_objective(self, var):
""" Self-explanatory; part of API """
self._add_var('func', var)
def add_constraint(self, var, lower=None, upper=None,
get_jacs=None, linear=False):
""" Self-explanatory; part of API """
self._add_var('func', var, lower=lower, upper=upper,
get_jacs=get_jacs, linear=linear)
def add_sens_callback(self, callback):
self.sens_callback = callback
def obj_func(self, dv_dict):
""" Objective function passed to pyOptSparse """
system = self._system
variables = self._variables
for dv_name in variables['dv'].keys():
dv_id = variables['dv'][dv_name]['ID']
system(dv_id).value = dv_dict[dv_name]
print '********************'
print 'Evaluating functions'
print '********************'
print
temp, success = system.compute(True)
fail = not success
print 'DVs:'
print dv_dict
print 'Failure:', fail
print
print '-------------------------'
print 'Done evaluating functions'
print '-------------------------'
print
func_dict = {}
for func_name in variables['func'].keys():
func_id = variables['func'][func_name]['ID']
func_dict[func_name] = system.vec['u'][func_id]
if fail:
system.vec['u'].array[:] = 1.0
system.vec['du'].array[:] = 0.0
for var in system.variables:
system.vec['u'][var][:] = \
system.variables[var]['u'] /\
system.variables[var]['u0']
return func_dict, fail
def sens_func(self, dv_dict, func_dict):
""" Derivatives function passed to pyOptSparse """
system = self._system
variables = self._variables
print '**********************'
print 'Evaluating derivatives'
print '**********************'
print
fail = False
sens_dict = {}
for func_name in variables['func'].keys():
func = variables['func'][func_name]
func_id = func['ID']
get_jacs = func['get_jacs']
nfunc = system.vec['u'][func_id].shape[0]
sens_dict[func_name] = {}
if func['get_jacs'] is None:
for dv_name in variables['dv'].keys():
dv_id = variables['dv'][dv_name]['ID']
ndv = system.vec['u'][dv_id].shape[0]
sens_dict[func_name][dv_name] \
= numpy.zeros((nfunc, ndv))
for ind in xrange(nfunc):
temp, success = system.compute_derivatives('rev', func_id, ind, True)
fail = fail or not success
for dv_name in variables['dv'].keys():
dv_id = variables['dv'][dv_name]['ID']
sens_dict[func_name][dv_name][ind, :] \
= system.vec['df'][dv_id]
else:
jacs = get_jacs()
for dv_var in jacs:
dv_id = self._system.get_id(dv_var)
dv_name = self._get_name(dv_id)
sens_dict[func_name][dv_name] \
= jacs[dv_var]
#print 'DVs:'
#print dv_dict
#print 'Functions:'
#print func_dict
#print 'Derivatives:'
#print sens_dict
print 'Failure:', fail
print
print '---------------------------'
print 'Done evaluating derivatives'
print '---------------------------'
print
if fail:
system.vec['du'].array[:] = 0.0
if self.sens_callback is not None:
self.sens_callback()
return sens_dict, fail
def __call__(self, optimizer, options=None):
""" Run optimization """
system = self._system
variables = self._variables
opt_prob = OptProblem('Optimization', self.obj_func)
for dv_name in variables['dv'].keys():
dv = variables['dv'][dv_name]
dv_id = dv['ID']
value = dv['value']
lower = dv['lower']
upper = dv['upper']
size = system.vec['u'](dv_id).shape[0]
opt_prob.addVarGroup(dv_name, size, value=value,
lower=lower, upper=upper)
opt_prob.finalizeDesignVariables()
for func_name in variables['func'].keys():
func = variables['func'][func_name]
func_id = func['ID']
lower = func['lower']
upper = func['upper']
linear = func['linear']
get_jacs = func['get_jacs']
size = system.vec['u'](func_id).shape[0]
if lower is None and upper is None:
opt_prob.addObj(func_name)
else:
if func['get_jacs'] is None:
opt_prob.addConGroup(func_name, size,
lower=lower, upper=upper)
else:
jacs_var = get_jacs()
dv_names = []
jacs = {}
for dv_var in jacs_var:
dv_id = self._system.get_id(dv_var)
dv_name = self._get_name(dv_id)
dv_names.append(dv_name)
jacs[dv_name] = jacs_var[dv_var]
opt_prob.addConGroup(func_name, size,
wrt=dv_names,
jac=jacs, linear=linear,
lower=lower, upper=upper)
if options is None:
options = {}
opt = Optimizer(optimizer, options=options)
opt.setOption('Iterations limit', int(1e6))
#opt.setOption('Verify level', 3)
sol = opt(opt_prob, sens=self.sens_func, storeHistory='hist.hst')
print sol