"""
pyNLPQLP - A pyOptSparse wrapper for Schittkowski's NLPQLP
optimization algorithm.
"""
# Compiled module
try:
from . import nlpqlp # isort: skip
except ImportError:
nlpqlp = 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 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 nlpqlp is None:
if raiseError:
raise Error("There was an error importing the compiled nlpqlp module")
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"] = datetime.datetime.now().strftime("%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