def __init__(self, n=0, m=0, name='Algopy-Generic', **kwargs):
NLPModel.__init__(self, n, m, name, **kwargs)
try:
self._trace_obj(self.x0)
except:
pass
try:
self._trace_cons(self.x0)
except:
pass
def __init__(self, n=0, m=0, name="Algopy-Generic", **kwargs):
NLPModel.__init__(self, n, m, name, **kwargs)
try:
self._trace_obj(self.x0)
except:
pass
try:
self._trace_cons(self.x0)
except:
pass
def __init__(self, n=0, m=0, name="Adolc-Generic", **kwargs):
NLPModel.__init__(self, n, m, name, **kwargs)
self.__class__.__NUM_INSTANCES[0] += 1
# Trace objective and constraint functions.
self._obj_trace_id = None
self._trace_obj(self.x0)
self._con_trace_id = None
if self.m > 0:
self._trace_con(self.x0)
self.first_sparse_hess_eval = True
self.first_sparse_jac_eval = True
if i == 1:
grad[0] = 0.
grad[1] = 2.
elif i == 2:
grad[0] = 3.
grad[1] = 0.
return grad
Infinity = numpy.inf
# Create an instance of an NLPModel
nlp = NLPModel(n=2, m=2, name='Rosenbrock',
Lvar=numpy.array([-1000,-Infinity]), Uvar=numpy.array([Infinity,1000]),
Lcon=numpy.array([-2000,-1000]), Ucon=numpy.array([2000,1000]),
x0 = numpy.array([-1.2, 1]))
# Assign an objective function and its gradient
nlp.obj = rosenbrock
nlp.grad = gradient_rosenbrock
# Assign constraints and its gradients
nlp.cons = constraint
nlp.igrad = gradient_constraint
# Translate this NLPy problem in a pyOpt problem
opt_prob = PyOpt_From_NLPModel(nlp)
def __init__(self, n=0, m=0, name='CppAD-Generic', **kwargs):
NLPModel.__init__(self, n, m, name, **kwargs)
# Trace objective and constraint functions.
self._trace_obj(self.x0)
if self.m > 0: self._trace_cons(self.x0)
def __init__(self, pyopt_model, sens_type, **kwargs):
"""
:parameters:
:nlp: nonlinear problem pyOpt
:sens_type: sensitivity type
'FD' : estimation of gradients using finite differences
'CS' : estimation of gradients using complex step
grad_func : user provided gradients
"""
self.pyopt_model = pyopt_model
# Problem dimensions.
nbVar = len(pyopt_model._variables)
nbCons = len(pyopt_model._constraints)
# Bounds on variables.
LVar = -numpy.inf * numpy.ones(nbVar)
UVar = numpy.inf * numpy.ones(nbVar)
X0 = numpy.zeros(nbVar)
for i in range(nbVar):
var = pyopt_model.getVar(i)
LVar[i] = var.lower
UVar[i] = var.upper
X0[i] = var.value
# Constraints left and right-hand side.
LCon = -numpy.inf * numpy.ones(nbCons)
UCon = numpy.inf * numpy.ones(nbCons)
for j in range(nbCons):
cons = pyopt_model.getCon(j)
if cons.type=='i':
LCon[j] = cons.lower
UCon[j] = cons.upper
elif cons.type=='e':
LCon[j] = cons.equal
UCon[j] = cons.equal
# Differentiation method.
self.sens_type = sens_type
if sens_type == 'FD':
self.sens_step = 1e-6
else:
self.sens_step = 1e-20
self.gradient_method = Gradient(pyopt_model, sens_type, '',
self.sens_step)
# Saved values (private).
self._last_x = None
self._last_obj = None
self._last_grad_obj = None
self._last_cons = None
self._last_grad_con = None
# Initialize model.
NLPModel.__init__(self, name=pyopt_model.name, n=nbVar, m=nbCons, Lvar=LVar, Uvar=UVar,
Lcon=LCon, Ucon=UCon, x0=X0, **kwargs)
def __init__(self, n=0, m=0, name='CppAD-Generic', **kwargs):
NLPModel.__init__(self, n, m, name, **kwargs)
# Trace objective and constraint functions.
self._trace_obj(self.x0)
if self.m > 0: self._trace_cons(self.x0)
