"""
pyPSQP - the pyPSQP wrapper
"""
# Compiled module
try:
from . import psqp # isort: skip
except ImportError:
psqp = 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 PSQP(Optimizer):
"""
PSQP Optimizer Class - Inherited from Optimizer Abstract Class
"""
def __init__(self, raiseError=True, options={}):
name = "PSQP"
category = "Local Optimizer"
defOpts = self._getDefaultOptions()
informs = self._getInforms()
if psqp is None:
if raiseError:
raise Error("There was an error importing the compiled psqp module")
super().__init__(name, category, defaultOptions=defOpts, informs=informs, options=options)
# PSQP needs Jacobians in dense format
self.jacType = "dense2d"
@staticmethod
def _getDefaultOptions():
defOpts = {
"XMAX": [float, 1e16],
"TOLX": [float, 1e-16],
"TOLC": [float, 1e-6],
"TOLG": [float, 1e-6],
"RPF": [float, 1e-4],
"MIT": [int, 1000],
"MFV": [int, 2000],
"MET": [int, 2],
"MEC": [int, 2],
"IPRINT": [int, 2],
"IOUT": [int, 6],
"IFILE": [str, "PSQP.out"],
}
return defOpts
@staticmethod
def _getInforms():
informs = {
1: "Change in design variable was less than or equal to tolerance",
2: "Change in objective function was less than or equal to tolerance",
3: "Objective function less than or equal to tolerance",
4: "Maximum constraint value is less than or equal to tolerance",
11: "Maximum number of iterations exceeded",
12: "Maximum number of function evaluations exceeded",
13: "Maximum number of gradient evaluations exceeded",
-6: "Termination criterion not satisfied, but obtained point is acceptable",
-7: "Positive directional derivative in line search",
-8: "Interpolation error in line search",
-10: "Optimization failed",
}
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 PSQP 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:
self.unconstrained = True
optProb.dummyConstraint = False
# Set optProb and finalize
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()
xi = 3 * np.ones(len(xs), "int")
xs = np.maximum(xs, blx)
xs = np.minimum(xs, bux)
nvar = len(xs)
ff = self._assembleObjective()
# pSQP CAN handle two sided constraints, but need to know
# which ones are which. That is a little tricky to determine,
# so we will split them into one-sided <= zero constraints and
# equality=0 constraints such that it is simple to determine
# the type of constraints. Put the equality constraints frist.
oneSided = True
# Set the number of nonlinear constraints snopt *thinks* we have:
if self.unconstrained:
ncon = 0
cf = np.zeros(1)
cl = np.zeros(1)
cu = np.zeros(1)
ic = np.zeros(1)
# as done for SLSQP, we need this dummy constraint to make the code work
optProb.dummyConstraint = True
else:
indices, blc, buc, fact = self.optProb.getOrdering(["ne", "le", "ni", "li"], oneSided=oneSided)
ncon = len(indices)
cl = np.zeros(ncon) # -self.getOption('XMAX')*np.ones(ncon)
cu = np.zeros(ncon)
cf = np.zeros(ncon + 1)
ic = 2 * np.ones(ncon, "intc")
self.optProb.jacIndices = indices
self.optProb.fact = fact
self.optProb.offset = buc
# Also figure out the number of equality and set the type
# of constraint to 5
tmp0, __, __, __ = self.optProb.getOrdering(["ne", "le"], oneSided=oneSided)
ic[0 : len(tmp0)] = 5
if self.optProb.comm.rank == 0:
# ======================================================================
# PSQP - Objective Values Function
# ======================================================================
def pobj(n, x, f):
if self._checkEval(x):
self._internalEval(x)
f = self.storedData["fobj"]
psqp.pyflush(self.getOption("IOUT"))
return f
# ======================================================================
# PSQP - Constraint Values Function
# ======================================================================
def pcon(n, k, x, g):
if self._checkEval(x):
self._internalEval(x)
g = self.storedData["fcon"][k - 1]
psqp.pyflush(self.getOption("IOUT"))
return g
# ======================================================================
# PSQP - Objective Gradients Function
# ======================================================================
def pdobj(n, x, df):
if self._checkEval(x):
self._internalEval(x)
df = self.storedData["gobj"]
psqp.pyflush(self.getOption("IOUT"))
return df
# ======================================================================
# PSQP - Constraint Gradients Function
# ======================================================================
def pdcon(n, k, x, dg):
if self._checkEval(x):
self._internalEval(x)
dg = self.storedData["gcon"][k - 1]
psqp.pyflush(self.getOption("IOUT"))
return dg
# Setup argument list values
iterm = np.array(0, int)
gmax = 0.0
cmax = 0.0
opt = self.getOption
if not opt("IPRINT") <= 2:
raise Error("Incorrect output level setting (IPRINT) option. Must be <= 2")
if opt("IPRINT") != 0:
if os.path.isfile(opt("IFILE")):
os.remove(opt("IFILE"))
# Run PSQP
t0 = time.time()
# fmt: off
psqp.psqp_wrap(nvar, ncon, xs, xi, blx, bux, cf, ic, cl, cu,
opt('MIT'), opt('MFV'), opt('MET'), opt('MEC'),
opt('XMAX'), opt('TOLX'), opt('TOLC'), opt('TOLG'),
opt('RPF'), ff, gmax, cmax, opt('IPRINT'),
opt('IOUT'), opt('IFILE'), iterm, pobj, pdobj,
pcon, pdcon)
# fmt: on
optTime = time.time() - t0
if opt("IPRINT") > 0:
psqp.closeunit(opt("IPRINT"))
# Broadcast a -1 to indcate SLSQP has finished
self.optProb.comm.bcast(-1, root=0)
# Store Results
inform = iterm.item()
if inform < 0 and inform not in self.informs:
inform = -10
sol_inform = {}
sol_inform["value"] = inform
sol_inform["text"] = self.informs[inform]
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()
# 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