# Standard Python modules
import datetime
import os
import time
# External modules
import numpy as np
# isort: off
# Attempt to import mpi4py.
# If PYOPTSPARSE_REQUIRE_MPI is set to a recognized positive value, attempt import
# and raise exception on failure. If set to anything else, no import is attempted.
if "PYOPTSPARSE_REQUIRE_MPI" in os.environ:
if os.environ["PYOPTSPARSE_REQUIRE_MPI"].lower() in ["always", "1", "true", "yes"]:
try:
from paropt import ParOpt as _ParOpt
from mpi4py import MPI
except ImportError:
_ParOpt = None
else:
_ParOpt = None
# If PYOPTSPARSE_REQUIRE_MPI is unset, attempt to import mpi4py.
# Since ParOpt requires mpi4py, if either _ParOpt or mpi4py is unavailable
# we disable the optimizer.
else:
try:
from paropt import ParOpt as _ParOpt
from mpi4py import MPI
except ImportError:
_ParOpt = None
# isort: on
# Local modules
from ..pyOpt_error import Error
from ..pyOpt_optimizer import Optimizer
from ..pyOpt_utils import INFINITY
[docs]
class ParOpt(Optimizer):
"""
ParOpt optimizer class
ParOpt has the capability to handle distributed design vectors.
This is not replicated here since pyOptSparse does not have the
capability to handle this type of design problem.
"""
def __init__(self, raiseError=True, options={}):
name = "ParOpt"
category = "Local Optimizer"
if _ParOpt is None:
if raiseError:
raise Error("There was an error importing ParOpt")
# Create and fill-in the dictionary of default option values
self.defOpts = {}
paropt_default_options = _ParOpt.getOptionsInfo()
# Manually override the options with missing default values
paropt_default_options["ip_checkpoint_file"].default = "default.out"
paropt_default_options["problem_name"].default = "problem"
for option_name in paropt_default_options:
# Get the type and default value of the named argument
_type = None
if paropt_default_options[option_name].option_type == "bool":
_type = bool
elif paropt_default_options[option_name].option_type == "int":
_type = int
elif paropt_default_options[option_name].option_type == "float":
_type = float
else:
_type = str
default_value = paropt_default_options[option_name].default
# Set the entry into the dictionary
self.defOpts[option_name] = [_type, default_value]
self.set_options = {}
self.informs = {}
super().__init__(name, category, defaultOptions=self.defOpts, informs=self.informs, options=options)
# ParOpt requires a dense Jacobian format
self.jacType = "dense2d"
return
[docs]
def __call__(
self, optProb, sens=None, sensStep=None, sensMode=None, storeHistory=None, hotStart=None, storeSens=True
):
"""
This is the main routine used to solve the optimization
problem.
Parameters
----------
optProb : Optimization or Solution class instance
This is the complete description of the optimization problem
to be solved by the optimizer
sens : str or python Function.
Specifiy method to compute sensitivities. To
explictly use pyOptSparse gradient class to do the
derivatives with finite differenes use \'FD\'. \'sens\'
may also be \'CS\' which will cause pyOptSpare to compute
the derivatives using the complex step method. Finally,
\'sens\' may be a python function handle which is expected
to compute the sensitivities directly. For expensive
function evaluations and/or problems with large numbers of
design variables this is the preferred method.
sensStep : float
Set the step size to use for design variables. Defaults to
1e-6 when sens is \'FD\' and 1e-40j when sens is \'CS\'.
sensMode : str
Use \'pgc\' for parallel gradient computations. Only
available with mpi4py and each objective evaluation is
otherwise serial
storeHistory : str
File name of the history file into which the history of
this optimization will be stored
hotStart : str
File name of the history file to "replay" for the
optimziation. The optimization problem used to generate
the history file specified in \'hotStart\' must be
**IDENTICAL** to the currently supplied \'optProb\'. By
identical we mean, **EVERY SINGLE PARAMETER MUST BE
IDENTICAL**. As soon as he requested evaluation point
from ParOpt does not match the history, function and
gradient evaluations revert back to normal evaluations.
storeSens : bool
Flag sepcifying if sensitivities are to be stored in hist.
This is necessay for hot-starting only.
"""
self.startTime = time.time()
self.callCounter = 0
self.storeSens = storeSens
if len(optProb.constraints) == 0:
# If the problem is unconstrained, add a dummy constraint.
self.unconstrained = True
optProb.dummyConstraint = True
# Save the optimization problem and finalize constraint
# Jacobian, in general can only do on root proc
self.optProb = optProb
self.optProb.finalize()
# Set history/hotstart
self._setHistory(storeHistory, hotStart)
self._setInitialCacheValues()
self._setSens(sens, sensStep, sensMode)
blx, bux, xs = self._assembleContinuousVariables()
xs = np.maximum(xs, blx)
xs = np.minimum(xs, bux)
# The number of design variables
n = len(xs)
oneSided = True
if self.unconstrained:
m = 0
else:
indices, blc, buc, fact = self.optProb.getOrdering(["ne", "le", "ni", "li"], oneSided=oneSided)
m = len(indices)
self.optProb.jacIndices = indices
self.optProb.fact = fact
self.optProb.offset = buc
if self.optProb.comm.rank == 0:
class Problem(_ParOpt.Problem):
def __init__(self, ptr, n, m, xs, blx, bux):
super().__init__(MPI.COMM_SELF, n, m)
self.ptr = ptr
self.n = n
self.m = m
self.xs = xs
self.blx = blx
self.bux = bux
self.fobj = 0.0
return
def getVarsAndBounds(self, x, lb, ub):
"""Get the variable values and bounds"""
# Find the average distance between lower and upper bound
bound_sum = 0.0
for i in range(len(x)):
if self.blx[i] <= -INFINITY or self.bux[i] >= INFINITY:
bound_sum += 1.0
else:
bound_sum += self.bux[i] - self.blx[i]
bound_sum = bound_sum / len(x)
for i in range(len(x)):
x[i] = self.xs[i]
lb[i] = self.blx[i]
ub[i] = self.bux[i]
if self.xs[i] <= self.blx[i]:
x[i] = self.blx[i] + 0.5 * np.min((bound_sum, self.bux[i] - self.blx[i]))
elif self.xs[i] >= self.bux[i]:
x[i] = self.bux[i] - 0.5 * np.min((bound_sum, self.bux[i] - self.blx[i]))
return
def evalObjCon(self, x):
"""Evaluate the objective and constraint values"""
fobj, fcon, fail = self.ptr._masterFunc(x[:], ["fobj", "fcon"])
self.fobj = fobj
return fail, fobj, -fcon
def evalObjConGradient(self, x, g, A):
"""Evaluate the objective and constraint gradients"""
gobj, gcon, fail = self.ptr._masterFunc(x[:], ["gobj", "gcon"])
g[:] = gobj[:]
for i in range(self.m):
A[i][:] = -gcon[i][:]
return fail
optTime = MPI.Wtime()
# Optimize the problem
problem = Problem(self, n, m, xs, blx, bux)
optimizer = _ParOpt.Optimizer(problem, self.set_options)
optimizer.optimize()
x, z, zw, zl, zu = optimizer.getOptimizedPoint()
# Set the total opt time
optTime = MPI.Wtime() - optTime
# Get the obective function value
fobj = problem.fobj
if self.storeHistory:
self.metadata["endTime"] = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
self.metadata["optTime"] = optTime
self.hist.writeData("metadata", self.metadata)
self.hist.close()
# Create the optimization solution. Note that the signs on the multipliers
# are switch since ParOpt uses a formulation with c(x) >= 0, while pyOpt
# uses g(x) = -c(x) <= 0. Therefore the multipliers are reversed.
sol_inform = {}
# If number of constraints is zero, ParOpt returns z as None.
# Thus if there is no constraints, should pass an empty list
# to multipliers instead of z.
if z is not None:
sol = self._createSolution(optTime, sol_inform, fobj, x[:], multipliers=-z)
else:
sol = self._createSolution(optTime, sol_inform, fobj, x[:], multipliers=[])
# Indicate solution finished
self.optProb.comm.bcast(-1, root=0)
else: # We are not on the root process so go into waiting loop:
self._waitLoop()
sol = None
# Communication solution and return
sol = self._communicateSolution(sol)
return sol
def _on_setOption(self, name, value):
"""
Add the value to the set_options dictionary.
"""
self.set_options[name] = value