Source code for pyoptsparse.pyCONMIN.pyCONMIN

pyCONMIN - A variation of the pyCONMIN wrapper specificially designed to
work with sparse optimization problems.
# Compiled module
    from . import conmin  # isort: skip
except ImportError:
    conmin = None
# Standard Python modules
import datetime
import os
import time

# External modules
import numpy as np

# Local modules
from ..pyOpt_error import Error
from ..pyOpt_optimizer import Optimizer

[docs] class CONMIN(Optimizer): """ CONMIN Optimizer Class - Inherited from Optimizer Abstract Class """ def __init__(self, raiseError=True, options={}): name = "CONMIN" category = "Local Optimizer" defOpts = self._getDefaultOptions() informs = self._getInforms() if conmin is None: if raiseError: raise Error("There was an error importing the compiled conmin module") self.set_options = [] super().__init__(name, category, defaultOptions=defOpts, informs=informs, options=options) # CONMIN needs Jacobians in dense format self.jacType = "dense2d" @staticmethod def _getInforms(): informs = {} return informs @staticmethod def _getDefaultOptions(): defOpts = { "ITMAX": [int, int(1e4)], "DELFUN": [float, 1e-6], "DABFUN": [float, 1e-6], "ITRM": [int, 5], "NFEASCT": [int, 20], "IPRINT": [int, 4], "IOUT": [int, 6], "IFILE": [str, "CONMIN.out"], } 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 from CONMIN 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 user *actually* has an unconstrained problem, # slsqp 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/coldstart 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) ff = self._assembleObjective() oneSided = True noEquality = True if self.unconstrained: m = 0 else: indices, blc, buc, fact = self.optProb.getOrdering( ["ne", "le", "ni", "li"], oneSided=oneSided, noEquality=noEquality ) m = len(indices) self.optProb.jacIndices = indices self.optProb.fact = fact self.optProb.offset = buc if self.optProb.comm.rank == 0: # ================================================================= # CONMIN - Objective/Constraint Values Function # ================================================================= def cnmnfun(n1, n2, x, f, g): fobj, fcon, fail = self._masterFunc(x[0:ndv], ["fobj", "fcon"]) f = fobj g[0:ncn] = fcon return f, g # ================================================================= # CONMIN - Objective/Constraint Gradients Function # ================================================================= def cnmngrad(n1, n2, x, f, g, ct, df, a, ic, nac): gobj, gcon, fail = self._masterFunc(x[0:ndv], ["gobj", "gcon"]) df[0:ndv] = gobj.copy() # Only assign the gradients for constraints that are # actually active: nac = 0 for j in range(ncn): if g[j] >= ct: a[0:ndv, nac] = gcon[j, :] ic[nac] = j + 1 nac += 1 return df, a, ic, nac # Setup argument list values ndv = len(xs) ncn = m nn1 = ndv + 2 nn2 = ncn + 2 * ndv nn3 = max(nn2, ndv) nn4 = max(nn2, ndv) nn5 = 2 * nn4 if ncn > 0: gg = np.zeros(ncn, float) else: gg = np.array([0], float) if self.getOption("IPRINT") >= 0 and self.getOption("IPRINT") <= 4: iprint = self.getOption("IPRINT") else: raise Error("IPRINT option must be >= 0 and <= 4") iout = self.getOption("IOUT") ifile = self.getOption("IFILE") # Check if file exists and remove if necessary if iprint > 0: if os.path.isfile(ifile): os.remove(ifile) itmax = self.getOption("ITMAX") delfun = self.getOption("DELFUN") # finit, ginit = cnmnfun([],[],xx,ff,gg) dabfun = self.getOption("DABFUN") itrm = self.getOption("ITRM") nfeasct = self.getOption("ITRM") nfdg = 1 # User will supply all gradients # Counters for functions and gradients nfun = 0 ngrd = 0 # Run CONMIN t0 = time.time() # fmt: off conmin.conmin(ndv, ncn, xs, blx, bux, ff, gg, nn1, nn2, nn3, nn4, nn5, iprint, iout, ifile, itmax, delfun, dabfun, itrm, nfeasct, nfdg, nfun, ngrd, cnmnfun, cnmngrad) # fmt: on optTime = time.time() - t0 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() if iprint > 0: conmin.closeunit(self.getOption("IOUT")) # Broadcast a -1 to indcate SLSQP has finished self.optProb.comm.bcast(-1, root=0) # Store Results sol_inform = {} # sol_inform['value'] = inform # sol_inform['text'] = self.informs[inform[0]] # Create the optimization solution sol = self._createSolution(optTime, sol_inform, ff, xs) 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