scipy.optimize._lbfgsb_py:_minimize_lbfgsb

Parameters

disp : None or int

Deprecated option that previously controlled the text printed on the screen during the problem solution. Now the code does not emit any output and this keyword has no function.

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 where proj g_i is the i-th component of the projected gradient.

eps : float or ndarray

If jac is None the absolute step size used for numerical approximation of the jacobian via forward differences.

maxfun : int

Maximum number of function evaluations before minimization terminates. Note that this function may violate the limit if the gradients are evaluated by numerical differentiation.

maxiter : int

Maximum number of algorithm iterations.

iprint : int, optional

Deprecated option that previously controlled the text printed on the screen during the problem solution. Now the code does not emit any output and this keyword has no function.

maxls : int, optional

Maximum number of line search steps (per iteration). Default is 20.

finite_diff_rel_step : None or array_like, optional

If 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(x) * max(1, abs(x)), possibly adjusted to fit into the bounds. For method='3-point' the sign of h is ignored. If None (default) then step is selected automatically.

workers : int, map-like callable, optional

A map-like callable, such as multiprocessing.Pool.map for evaluating any numerical differentiation in parallel. This evaluation is carried out as workers(fun, iterable).