Source code for pyoptsparse.pySLSQP.pySLSQP

"""
pySLSQP - A variation of the pySLSQP wrapper specificially designed to
work with sparse optimization problems.
"""

# Standard Python modules
import datetime
import os
import time

# External modules
import numpy as np

# Local modules
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__))
slsqp = try_import_compiled_module_from_path("slsqp", THIS_DIR)


[docs] class SLSQP(Optimizer): """ SLSQP Optimizer Class - Inherited from Optimizer Abstract Class """ def __init__(self, raiseError=True, options={}): name = "SLSQP" category = "Local Optimizer" defOpts = self._getDefaultOptions() informs = self._getInforms() if isinstance(slsqp, str) and raiseError: raise ImportError(slsqp) self.set_options = [] super().__init__(name, category, defaultOptions=defOpts, informs=informs, options=options) # SLSQP needs Jacobians in dense format self.jacType = "dense2d" @staticmethod def _getDefaultOptions(): defOpts = { "ACC": [float, 1e-6], "MAXIT": [int, 500], "IPRINT": [int, 1], "IOUT": [int, 60], "IFILE": [str, "SLSQP.out"], } return defOpts @staticmethod def _getInforms(): informs = { -1: "Gradient evaluation required (g & a)", 0: "Optimization terminated successfully.", 1: "Function evaluation required (f & c)", 2: "More equality constraints than independent variables", 3: "More than 3*n iterations in LSQ subproblem", 4: "Inequality constraints incompatible", 5: "Singular matrix E in LSQ subproblem", 6: "Singular matrix C in LSQ subproblem", 7: "Rank-deficient equality constraint subproblem HFTI", 8: "Positive directional derivative for linesearch", 9: "Iteration limit exceeded", } return informs
[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 SLSQP 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 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() self._setSens(sens, sensStep, sensMode) blx, bux, xs = self._assembleContinuousVariables() xs = np.maximum(xs, blx) xs = np.minimum(xs, bux) n = len(xs) ff = 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: # ================================================================= # SLSQP - Objective/Constraint Values Function # ================================================================= def slfunc(m, me, la, n, f, g, x): fobj, fcon, fail = self._masterFunc(x, ["fobj", "fcon"]) f = fobj g[0:m] = -fcon slsqp.pyflush(self.getOption("IOUT")) return f, g # ================================================================= # SLSQP - Objective/Constraint Gradients Function # ================================================================= def slgrad(m, me, la, n, f, g, df, dg, x): gobj, gcon, fail = self._masterFunc(x, ["gobj", "gcon"]) df[0:n] = gobj.copy() dg[0:m, 0:n] = -gcon.copy() slsqp.pyflush(self.getOption("IOUT")) return df, dg # Setup argument list values la = max(m, 1) gg = np.zeros([la], float) df = np.zeros([n + 1], float) dg = np.zeros([la, n + 1], float) acc = np.array(self.getOption("ACC"), float) maxit = self.getOption("MAXIT") iprint = self.getOption("IPRINT") iout = self.getOption("IOUT") ifile = self.getOption("IFILE") if iprint >= 0: if os.path.isfile(ifile): os.remove(ifile) mode = np.array(0, int) mineq = m - meq + 2 * (n + 1) lsq = (n + 1) * ((n + 1) + 1) + meq * ((n + 1) + 1) + mineq * ((n + 1) + 1) lsi = ((n + 1) - meq + 1) * (mineq + 2) + 2 * mineq lsei = ((n + 1) + mineq) * ((n + 1) - meq) + 2 * meq + (n + 1) slsqpb = (n + 1) * (n / 2) + 2 * m + 3 * n + 3 * (n + 1) + 1 lwM = lsq + lsi + lsei + slsqpb + n + m lw = np.array(lwM, int) w = np.zeros(lw, float) ljwM = max(mineq, (n + 1) - meq) ljw = np.array(ljwM, int) jw = np.zeros(ljw, np.intc) nfunc = np.array(0, int) ngrad = np.array(0, int) # Run SLSQP t0 = time.time() # fmt: off slsqp.slsqp(m, meq, la, n, xs, blx, bux, ff, gg, df, dg, acc, maxit, iprint, iout, ifile, mode, w, lw, jw, ljw, nfunc, ngrad, slfunc, slgrad) # fmt: on optTime = time.time() - t0 # some entries of W include the lagrange multipliers # for each constraint, there are two entries (lower, upper). # if only one is active, look for the nonzero. If both are active, take the first one # FIXME: this does not currently work, so we do not save lambdaStar # to the solution object lambdaStar = [] idx = 0 for c_name in optProb.constraints: c = optProb.constraints[c_name] for _j in range(c.ncon): lambdaStar_lower = w[2 * idx] lambdaStar_upper = w[2 * idx + 1] if abs(lambdaStar_lower) > 1e-100: lambdaStar.append(lambdaStar_lower) else: lambdaStar.append(lambdaStar_upper) idx += 1 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() if iprint > 0: slsqp.closeunit(self.getOption("IOUT")) # Broadcast a -1 to indcate SLSQP has finished self.optProb.comm.bcast(-1, root=0) # Store Results inform = mode.item() sol_inform = {} sol_inform["value"] = inform sol_inform["text"] = self.informs[inform] # 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