Source code for pyoptsparse.pyIPOPT.pyIPOPT

pyIPOPT - A python wrapper to the core IPOPT compiled module.

# Standard Python modules
import copy
import datetime
import os
import time

# External modules
import numpy as np

# Local modules
from ..pyOpt_optimizer import Optimizer
from ..pyOpt_utils import (

# import the compiled module
THIS_DIR = os.path.dirname(os.path.abspath(__file__))
pyipoptcore = try_import_compiled_module_from_path("pyipoptcore", THIS_DIR)

[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(pyipoptcore, str) and raiseError: raise ImportError(pyipoptcore) 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, # snopt sort of chokes with 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 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.array(-INFINITY) buc = np.array(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_"), ) # Define the 4 call back functions that ipopt needs: def eval_f(x, user_data=None): fobj, fail = self._masterFunc(x, ["fobj"]) if fail == 2: self.userRequestedTermination = True return fobj def eval_g(x, user_data=None): fcon, fail = self._masterFunc(x, ["fcon"]) if fail == 2: self.userRequestedTermination = True return fcon.copy() def eval_grad_f(x, user_data=None): gobj, fail = self._masterFunc(x, ["gobj"]) if fail == 2: self.userRequestedTermination = True return gobj.copy() def eval_jac_g(x, flag, user_data=None): if flag: return copy.deepcopy(matStruct) else: gcon, fail = self._masterFunc(x, ["gcon"]) if fail == 2: self.userRequestedTermination = True return gcon.copy() # Define intermediate callback. If this method returns false, # Ipopt will terminate with the User_Requested_Stop status. def eval_intermediate_callback(*args, **kwargs): if self.userRequestedTermination is True: return False else: return True timeA = time.time() nnzj = len(matStruct[0]) nnzh = 0 nlp = pyipoptcore.create( len(xs), blx, bux, ncon, blc, buc, nnzj, nnzh, eval_f, eval_grad_f, eval_g, eval_jac_g, ) nlp.set_intermediate_callback(eval_intermediate_callback) self._set_ipopt_options(nlp) x, zl, zu, constraint_multipliers, obj, status = nlp.solve(xs) nlp.close() optTime = time.time() - timeA if self.storeHistory: self.metadata["endTime"] ="%Y-%m-%d %H:%M:%S") self.metadata["optTime"] = optTime self.hist.writeData("metadata", self.metadata) self.hist.close() # Store Results sol_inform = {} sol_inform["value"] = status sol_inform["text"] = self.informs[status] # Create the optimization solution sol = self._createSolution(optTime, sol_inform, obj, x) # 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(): if isinstance(value, str): nlp.str_option(name, value) elif isinstance(value, float): nlp.num_option(name, value) elif isinstance(value, int): nlp.int_option(name, value) else: print("invalid option type", type(value))