"""
pyIPOPT - A python wrapper to the core IPOPT compiled module.
"""
# Standard Python modules
import copy
import datetime
import time
# External modules
import numpy as np
# Local modules
from ..pyOpt_optimizer import Optimizer
from ..pyOpt_solution import SolutionInform
from ..pyOpt_utils import ICOL, INFINITY, IROW, convertToCOO, extractRows, import_module, scaleRows
cyipopt = import_module("cyipopt")
[docs]
class IPOPT(Optimizer):
"""
IPOPT Optimizer Class - Inherited from Optimizer Abstract Class
"""
def __init__(self, raiseError=True, options={}):
"""
IPOPT Optimizer Class Initialization
"""
name = "IPOPT"
category = "Local Optimizer"
defOpts = self._getDefaultOptions()
informs = self._getInforms()
if isinstance(cyipopt, Exception) and raiseError:
raise cyipopt
super().__init__(
name,
category,
defaultOptions=defOpts,
informs=informs,
options=options,
checkDefaultOptions=False,
)
# IPOPT needs Jacobians in coo format
self.jacType = "coo"
@staticmethod
def _getInforms():
informs = {
0: "Solve Succeeded",
1: "Solved To Acceptable Level",
2: "Infeasible Problem Detected",
3: "Search Direction Becomes Too Small",
4: "Diverging Iterates",
5: "User Requested Stop",
6: "Feasible Point Found",
-1: "Maximum Iterations Exceeded",
-2: "Restoration Failed",
-3: "Error In Step Computation",
-4: "Maximum CpuTime Exceeded",
-10: "Not Enough Degrees Of Freedom",
-11: "Invalid Problem Definition",
-12: "Invalid Option",
-13: "Invalid Number Detected",
-100: "Unrecoverable Exception",
-101: "NonIpopt Exception Thrown",
-102: "Insufficient Memory",
-199: "Internal Error",
}
return informs
@staticmethod
def _getDefaultOptions():
defOpts = {
"print_level": [int, 0],
"file_print_level": [int, 5],
"sb": [str, "yes"],
"print_user_options": [str, "yes"],
"output_file": [str, "IPOPT.out"],
"linear_solver": [str, "mumps"],
}
return defOpts
[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 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
self.userRequestedTermination = False
if len(optProb.constraints) == 0:
# If the user *actually* has an unconstrained problem,
# IPOPT sort of chokes with that....it has to have at
# least one constraint. So we will add one
# automatically here:
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()
blx, bux, xs = self._assembleContinuousVariables()
self._setSens(sens, sensStep, sensMode)
# Determine the sparsity structure of the full Jacobian
# -----------------------------------------------------
# Gather dummy data and process Jacobian:
gcon = {}
for iCon in self.optProb.constraints:
gcon[iCon] = self.optProb.constraints[iCon].jac
jac = self.optProb.processConstraintJacobian(gcon)
if self.optProb.nCon > 0:
# We need to reorder this full Jacobian...so get ordering:
indices, blc, buc, fact = self.optProb.getOrdering(["ne", "ni", "le", "li"], oneSided=False)
self.optProb.jacIndices = indices
self.optProb.fact = fact
self.optProb.offset = np.zeros(len(indices))
ncon = len(indices)
jac = extractRows(jac, indices) # Does reordering
scaleRows(jac, fact) # Perform logical scaling
else:
blc = np.atleast_1d(-INFINITY)
buc = np.atleast_1d(INFINITY)
ncon = 1
jac = convertToCOO(jac) # Conver to coo format for IPOPT
# We make a split here: If the rank is zero we setup the
# problem and run IPOPT, otherwise we go to the waiting loop:
if self.optProb.comm.rank == 0:
# Now what we need for IPOPT is precisely the .row and
# .col attributes of the fullJacobian array
matStruct = (
jac["coo"][IROW].copy().astype("int_"),
jac["coo"][ICOL].copy().astype("int_"),
)
class CyIPOPTProblem:
# Define the 4 call back functions that ipopt needs:
def objective(_, x):
fobj, fail = self._masterFunc(x, ["fobj"])
if fail == 1:
fobj = np.array(np.nan)
elif fail == 2:
self.userRequestedTermination = True
return fobj
def constraints(_, x):
fcon, fail = self._masterFunc(x, ["fcon"])
if fail == 1:
fcon = np.full_like(fcon, np.nan)
elif fail == 2:
self.userRequestedTermination = True
return fcon.copy()
def gradient(_, x):
gobj, fail = self._masterFunc(x, ["gobj"])
if fail == 1:
gobj = np.full_like(gobj, np.nan)
elif fail == 2:
self.userRequestedTermination = True
return gobj.copy()
def jacobian(_, x):
gcon, fail = self._masterFunc(x, ["gcon"])
if fail == 1:
gcon = np.full_like(gcon, np.nan)
elif fail == 2:
self.userRequestedTermination = True
return gcon.copy()
def jacobianstructure(_):
return copy.deepcopy(matStruct)
# Define intermediate callback. If this method returns false,
# Ipopt will terminate with the User_Requested_Stop status.
def intermediate(_, *args, **kwargs):
if self.userRequestedTermination is True:
return False
else:
return True
timeA = time.time()
nlp = cyipopt.Problem(
n=len(xs),
m=ncon,
problem_obj=CyIPOPTProblem(),
lb=blx,
ub=bux,
cl=blc,
cu=buc,
)
self._set_ipopt_options(nlp)
x, info = nlp.solve(xs)
nlp.close()
optTime = time.time() - timeA
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()
# Store optimizer exit condition and message
sol_inform = SolutionInform.from_informs(self.informs, info["status"])
# Create the optimization solution
sol = self._createSolution(optTime, sol_inform, info["obj_val"], x, multipliers=info["mult_g"])
# Indicate solution finished
self.optProb.comm.bcast(-1, root=0)
else:
self._waitLoop()
sol = None
# Communication solution and return
sol = self._communicateSolution(sol)
return sol
def _set_ipopt_options(self, nlp):
"""
Set all of the the options in self.options in the ipopt instance nlp
"""
# Set Options from the local options dictionary
# ---------------------------------------------
for name, value in self.options.items():
nlp.add_option(name, value)