minimize(method=’LBFGSB’)¶

scipy.optimize.
minimize
(fun, x0, args=(), method='LBFGSB', jac=None, bounds=None, tol=None, callback=None, options={'disp': None, 'maxcor': 10, 'ftol': 2.220446049250313e09, 'gtol': 1e05, 'eps': 1e08, 'maxfun': 15000, 'maxiter': 15000, 'iprint':  1, 'maxls': 20, 'finite_diff_rel_step': None}) Minimize a scalar function of one or more variables using the LBFGSB algorithm.
See also
For documentation for the rest of the parameters, see
scipy.optimize.minimize
 Options
 dispNone or int
If disp is None (the default), then the supplied version of iprint is used. If disp is not None, then it overrides the supplied version of iprint with the behaviour you outlined.
 maxcorint
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.)
 ftolfloat
The iteration stops when
(f^k  f^{k+1})/max{f^k,f^{k+1},1} <= ftol
. gtolfloat
The iteration will stop when
max{proj g_i  i = 1, ..., n} <= gtol
wherepg_i
is the ith component of the projected gradient. epsfloat or ndarray
If jac is None the absolute step size used for numerical approximation of the jacobian via forward differences.
 maxfunint
Maximum number of function evaluations.
 maxiterint
Maximum number of iterations.
 iprintint, optional
Controls the frequency of output.
iprint < 0
means no output;iprint = 0
print only one line at the last iteration;0 < iprint < 99
print also f andproj g
every iprint iterations;iprint = 99
print details of every iteration except nvectors;iprint = 100
print also the changes of active set and final x;iprint > 100
print details of every iteration including x and g. callbackcallable, optional
Called after each iteration, as
callback(xk)
, wherexk
is the current parameter vector. maxlsint, optional
Maximum number of line search steps (per iteration). Default is 20.
 finite_diff_rel_stepNone or array_like, optional
If jac in [‘2point’, ‘3point’, ‘cs’] the relative step size to use for numerical approximation of the jacobian. The absolute step size is computed as
h = rel_step * sign(x0) * max(1, abs(x0))
, possibly adjusted to fit into the bounds. Formethod='3point'
the sign of h is ignored. If None (default) then step is selected automatically.
Notes
The option ftol is exposed via the
scipy.optimize.minimize
interface, but callingscipy.optimize.fmin_l_bfgs_b
directly exposes factr. The relationship between the two isftol = factr * numpy.finfo(float).eps
. I.e., factr multiplies the default machine floatingpoint precision to arrive at ftol.