func : callable f(x, *args)
Function to minimise.
 x0 : ndarray
Initial guess.
 fprime : callable fprime(x, *args)
The gradient of func. If None, then func returns the function
value and the gradient (f, g = func(x, *args)), unless
approx_grad is True in which case func returns only f.
 args : tuple
Arguments to pass to func and fprime.
 approx_grad : bool
Whether to approximate the gradient numerically (in which case
func returns only the function value).
 bounds : list
(min, max) pairs for each element in x, defining
the bounds on that parameter. Use None for one of min or
max when there is no bound in that direction.
 m : int
The maximum number of variable metric corrections
used to define the limited memory matrix. (The limited memory BFGS
method does not store the full hessian but uses this many terms in an
approximation to it.)
 factr : float
The iteration stops when
(f^k  f^{k+1})/max{f^k,f^{k+1},1} <= factr * eps,
where eps is the machine precision, which is automatically
generated by the code. Typical values for factr are: 1e12 for
low accuracy; 1e7 for moderate accuracy; 10.0 for extremely
high accuracy.
 pgtol : float
The iteration will stop when
max{proj g_i  i = 1, ..., n} <= pgtol
where pg_i is the ith component of the projected gradient.
 epsilon : float
Step size used when approx_grad is True, for numerically
calculating the gradient
 iprint : int
Controls the frequency of output. iprint < 0 means no output.
 maxfun : int
Maximum number of function evaluations.
