Source code for pyoptsparse.pyParOpt.ParOpt

# 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