Source code for pyoptsparse.pyNLPQLP.pyNLPQLP

pyNLPQLP - A pyOptSparse wrapper for Schittkowski's NLPQLP
optimization algorithm.
# 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
from ..pyOpt_utils import try_import_compiled_module_from_path

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

[docs] class NLPQLP(Optimizer): """ NLPQL Optimizer Class - Inherited from Optimizer Abstract Class """ def __init__(self, raiseError=True, options={}): name = "NLPQLP" category = "Local Optimizer" defOpts = self._getDefaultOptions() informs = self._getInforms() if isinstance(nlpqlp, str) and raiseError: raise ImportError(nlpqlp) super().__init__(name, category, defaultOptions=defOpts, informs=informs, options=options) # NLPQLP needs Jacobians in dense format self.jacType = "dense2d" @staticmethod def _getInforms(): informs = { -2: ( "Compute gradient values w.r.t. the variables stored in" + " first column of X, and store them in DF and DG." + " Only derivatives for active constraints ACTIVE(J)=.TRUE. need to be computed." ), -1: ( "Compute objective fn and all constraint values subject" + "the variables found in the first L columns of X, and store them in F and G." ), 0: "The optimality conditions are satisfied.", 1: "The algorithm has been stopped after MAXIT iterations.", 2: "The algorithm computed an uphill search direction.", 3: "Underflow occurred when determining a new approximation matrix for the Hessian of the Lagrangian.", 4: "The line search could not be terminated successfully.", 5: "Length of a working array is too short. More detailed error information is obtained with IPRINT>0", 6: "There are false dimensions, for example M>MMAX, N>=NMAX, or MNN2<>M+N+N+2.", 7: "The search direction is close to zero, but the current iterate is still infeasible.", 8: "The starting point violates a lower or upper bound.", 9: "Wrong input parameter, i.e., MODE, LDL decomposition in D and C (in case of MODE=1), IPRINT, IOUT", 10: "Internal inconsistency of the quadratic subproblem, division by zero.", 11: "More than MAXFUN successive non-evaluable function calls.", 100: ( "The solution of the quadratic programming subproblem has been" + " terminated with an error message and IFAIL is set to IFQL+100," + " where IFQL denotes the index of an inconsistent constraint." ), } return informs @staticmethod def _getDefaultOptions(): defOpts = { "accuracy": [float, 1e-6], "accuracyQP": [float, 1e-14], "stepMin": [float, 1e-6], "maxFun": [int, 20], "maxIt": [int, 500], "maxNM": [int, 1], "rho": [float, 1.0], "iPrint": [int, [2, 0, 1, 3, 4]], "mode": [int, 0], "iOut": [int, 6], "lMerit": [bool, True], "lQl": [bool, False], "iFile": [str, "NLPQLP.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. Specify method to compute sensitivities. To explicitly use pyOptSparse gradient class to do the derivatives with finite differences 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 optimization. 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 NLPQL does not match the history, function and gradient evaluations revert back to normal evaluations. storeSens : bool Flag specifying if sensitivities are to be stored in hist. This is necessary for hot-starting only. """ self.startTime = time.time() self.callCounter = 0 self.storeSens = storeSens if len(optProb.constraints) == 0: 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) nvar = len(xs) f = self._assembleObjective() oneSided = True # Set the number of nonlinear constraints snopt *thinks* we have: if self.unconstrained: m = 0 meq = 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 # Also figure out the number of equality: tmp0, __, __, __ = self.optProb.getOrdering(["ne", "le"], oneSided=oneSided) meq = len(tmp0) if self.optProb.comm.rank == 0: # ================================================================= # NLPQL - Objective/Constraint Values Function (Real Valued) # ================================================================= def nlfunc(m, me, mmax, n, f, g, x, active, fail): fobj, fcon, fail = self._masterFunc(x, ["fobj", "fcon"]) f = fobj g[0:m] = -fcon return f, g, fail # ================================================================= # NLPQL - Objective/Constraint Gradients Function # ================================================================= def nlgrad(m, me, mmax, n, f, g, df, dg, x, active, wa): gobj, gcon, fail = self._masterFunc(x, ["gobj", "gcon"]) df[0:n] = gobj.copy() dg[0:m, 0:n] = -gcon.copy() return df, dg # setup argument list values num_procs = 1 # We only allow a single "processor" ie we are # actually running NLPQL (no P) # Set som basic sizes m = m me = meq mmax = max(1, m) n = nvar nmax = max(2, n + 2) mnn2 = m + n + n + 2 # xs, ff, and gg have to have an extra dimension # associated with them for the NP. We will do this # correctly even though num_procs is hard-coded to 1. xs = np.array(xs).T f = np.array(f) g = np.zeros((mmax, num_procs)) df = np.zeros(nmax) dg = np.zeros((mmax, nmax)) u = np.zeros(mnn2) c = np.zeros((nmax, nmax)) d = np.zeros(nmax) go = self.getOption if go("iPrint") < 0 or go("iPrint") > 4: raise Error("Incorrect iPrint option. Must be >=0 and <= 4") if not (go("mode") >= 0 and go("mode") <= 18): raise Error("Incorrect mode option. Must be >= 0 and <= 18.") if os.path.isfile(go("iFile")): os.remove(go("iFile")) ifail = np.array(0, dtype=int) # Run NLPQL t0 = time.time() # fmt: off nlpqlp.wrapper(num_procs, m, me, mmax, n, nmax, mnn2, xs, f, g, df, dg, u, blx, bux, c, d, go('accuracy'), go('accuracyQP'), go('stepMin'), go('maxFun'), go('maxIt'), go('maxNM'), go('rho'), go('mode'), go('iPrint'), go('iOut'), go('iFile'), ifail, go('lMerit'), go('lQl'), nlfunc, nlgrad) # fmt: on optTime = time.time() - t0 # Broadcast a -1 to indcate NLPQL has finished self.optProb.comm.bcast(-1, root=0) 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 inform = ifail.item() sol_inform = {} sol_inform["value"] = inform sol_inform["text"] = self.informs[inform] # Create the optimization solution sol = self._createSolution(optTime, sol_inform, f, 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