def __init__(self, wrt, f, fprime, min_grad=1e-6, args=None):
"""Create a NonlinearConjugateGradient object.
Parameters
----------
wrt : array_like
Array of parameters of the problem.
f : callable
Objective function.
fprime : callable
First derivative of the objective function.
min_grad : float
If all components of the gradient fall below this value, stop
minimization.
args : iterable, optional
Iterable of arguments passed on to the objective function and its
derivatives.
"""
super(NonlinearConjugateGradient, self).__init__(wrt, args=args)
self.f = f
self.fprime = fprime
self.line_search = WolfeLineSearch(wrt, self.f, self.fprime, c2=0.2)
self.min_grad = min_grad
def __init__(self,
wrt,
f,
fprime,
initial_hessian_diag=1,
n_factors=10,
line_search=None,
args=None):
"""
Create an Lbfgs object.
Attributes
----------
wrt : array_like
Current solution to the problem. Can be given as a first argument to \
``.f`` and ``.fprime``.
f : Callable
The object function.
fprime : Callable
First derivative of the objective function. Returns an array of the \
same shape as ``.wrt``.
initial_hessian_diag : array_like
The initial estimate of the diagonal of the Hessian.
n_factors : int
The number of factors that should be used to implicitly represent the inverse Hessian.
line_search : LineSearch object.
Line search object to perform line searches with.
args : iterable
Iterator over arguments which ``fprime`` will be called with.
"""
super(Lbfgs, self).__init__(wrt, args=args)
self.f = f
self.fprime = fprime
self.initial_hessian_diag = initial_hessian_diag
self.n_factors = n_factors
if line_search is not None:
self.line_search = line_search
else:
self.line_search = WolfeLineSearch(wrt, self.f, self.fprime)
def __init__(self,
wrt,
f,
fprime,
initial_inv_hessian=None,
line_search=None,
args=None):
"""Create a BFGS object.
Parameters
----------
wrt : array_like
Array that represents the solution. Will be operated upon in
place. ``f`` and ``fprime`` should accept this array as a first argument.
f : callable
The objective function.
fprime : callable
Callable that given a solution vector as first parameter and *args
and **kwargs drawn from the iterations ``args`` returns a
search direction, such as a gradient.
initial_inv_hessian : array_like
The initial estimate of the approximiate Hessian.
line_search : LineSearch object.
Line search object to perform line searches with.
args : iterable
Iterator over arguments which ``fprime`` will be called with.
"""
super(Bfgs, self).__init__(wrt, args=args)
self.f = f
self.fprime = fprime
self.inv_hessian = initial_inv_hessian
if line_search is not None:
self.line_search = line_search
else:
self.line_search = WolfeLineSearch(wrt, self.f, self.fprime)
