_minimize_lbfgsb(fun, x0, args=(), jac=None, bounds=None, disp=None, maxcor=10, ftol=2.220446049250313e-09, gtol=1e-05, eps=1e-08, maxfun=15000, maxiter=15000, iprint=-1, callback=None, maxls=20, finite_diff_rel_step=None, **unknown_options)
The option :None:None:`ftol` is exposed via the scipy.optimize.minimize
interface, but calling scipy.optimize.fmin_l_bfgs_b
directly exposes :None:None:`factr`. The relationship between the two is ftol = factr * numpy.finfo(float).eps
. I.e., :None:None:`factr` multiplies the default machine floating-point precision to arrive at :None:None:`ftol`.
If :None:None:`disp is None` (the default), then the supplied version of :None:None:`iprint` is used. If :None:None:`disp is not None`, then it overrides the supplied version of :None:None:`iprint` with the behaviour you outlined.
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.)
The iteration stops when (f^k -
f^{k+1})/max{|f^k|,|f^{k+1}|,1} <= ftol
.
The iteration will stop when max{|proj g_i | i = 1, ..., n}
<= gtol
where pg_i
is the i-th component of the projected gradient.
If :None:None:`jac is None` the absolute step size used for numerical approximation of the jacobian via forward differences.
Maximum number of function evaluations.
Maximum number of iterations.
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 and |proj g|
every iprint iterations; iprint = 99
print details of every iteration except n-vectors; iprint = 100
print also the changes of active set and final x; iprint > 100
print details of every iteration including x and g.
Called after each iteration, as callback(xk)
, where xk
is the current parameter vector.
Maximum number of line search steps (per iteration). Default is 20.
If :None:None:`jac in ['2-point', '3-point', '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. For method='3-point'
the sign of :None:None:`h` is ignored. If None (default) then step is selected automatically.
Minimize a scalar function of one or more variables using the L-BFGS-B algorithm.
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