"""
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_error import pyOptSparseWarning
from ..pyOpt_optimizer import Optimizer
from ..pyOpt_solution import SolutionInform
from ..pyOpt_utils import import_module
# import the compiled module
THIS_DIR = os.path.dirname(os.path.abspath(__file__))
slsqp = import_module("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, Exception) and raiseError:
raise 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):
if (x < blx).any() or (x > bux).any():
pyOptSparseWarning("Values in x were outside bounds during a minimize step, clipping to bounds")
fobj, fcon, fail = self._masterFunc(np.clip(x, blx, bux), ["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(np.clip(x, blx, bux), ["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
# Clip final result to user bounds (this occurs during the optimization as well
# so this just makes the output consistent with what the optimizer sees)
xs = np.clip(xs, blx, bux)
# 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 optimizer exit condition and message
inform = mode.item()
sol_inform = SolutionInform.from_informs(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