minimize(method=’L-BFGS-B’)¶
-
scipy.optimize.
minimize
(fun, x0, args=(), method='L-BFGS-B', jac=None, bounds=None, tol=None, callback=None, options={'disp': None, 'maxcor': 10, 'ftol': 2.220446049250313e-09, 'gtol': 1e-05, 'eps': 1e-08, 'maxfun': 15000, 'maxiter': 15000, 'iprint': -1, 'maxls': 20}) Minimize a scalar function of one or more variables using the L-BFGS-B algorithm.
See also
For documentation for the rest of the parameters, see
scipy.optimize.minimize
Options: disp : bool
Set to True to print convergence messages.
maxcor : 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.)
ftol : float
The iteration stops when
(f^k - f^{k+1})/max{|f^k|,|f^{k+1}|,1} <= ftol
.gtol : float
The iteration will stop when
max{|proj g_i | i = 1, ..., n} <= gtol
wherepg_i
is the i-th component of the projected gradient.eps : float
Step size used for numerical approximation of the jacobian.
disp : int
Set to True to print convergence messages.
maxfun : int
Maximum number of function evaluations.
maxiter : int
Maximum number of iterations.
maxls : int, optional
Maximum number of line search steps (per iteration). Default is 20.
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 floating-point precision to arrive at ftol.