def preprocess(self):
"""
Prepares the optimization problem, including preprocessing variables,
and setting up the lists of constraints and variable bounds that SciPy
needs. Must be called after all functions are added to the driver.
"""
ConstrainedOptimizationDriver.preprocess(self)
class _fun:
def __init__(self,fun,idx):
self._f = fun
self._i = idx
def __call__(self,x):
return self._f(x,self._i)
#end
# setup the constraint list, the callbacks are the same for all
# constraints, an index argument (i) is used to distinguish them.
self._constraints = []
for i in range(self._nCon):
self._constraints.append({'type' : ('ineq','eq')[i<len(self._constraintsEQ)],
'fun' : _fun(self._eval_g,i),
'jac' : _fun(self._eval_jac_g,i)})
#end
# variable bounds
self._bounds = np.array((self.getLowerBound(),self.getUpperBound()),float).transpose()
# size the gradient and constraint jacobian
self._grad_f = np.zeros((self._nVar,))
self._old_grad_f = np.zeros((self._nVar,))
self._jac_g = np.zeros((self._nVar,self._nCon))
self._old_jac_g = np.zeros((self._nVar,self._nCon))
def getNLP(self):
"""
Prepares and returns the optimization problem for Ipopt (an instance of ipyopt.Problem).
For convenience also does other preprocessing, must be called after all functions are set.
Do not destroy the driver after obtaining the problem.
"""
ConstrainedOptimizationDriver.preprocess(self)
conLowerBound = np.zeros([
self._nCon,
])
conUpperBound = np.zeros([
self._nCon,
])
i = len(self._constraintsEQ)
conUpperBound[i:(i + len(self._constraintsGT))] = 1e20
# assume row major storage for gradient sparsity
rg = range(self._nVar * self._nCon)
self._sparseIndices = (np.array([i // self._nVar for i in rg],
dtype=int),
np.array([i % self._nVar for i in rg],
dtype=int))
# create the optimization problem
self._nlp = opt.Problem(self._nVar, self.getLowerBound(),
self.getUpperBound(), self._nCon,
conLowerBound, conUpperBound,
self._sparseIndices, 0, self._eval_f,
self._eval_grad_f, self._eval_g,
self._eval_jac_g)
return self._nlp