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# minimize(method=’L-BFGS-B’)

scipy.optimize.minimize(fun, x0, args=(), method=None, jac=None, hess=None, hessp=None, bounds=None, constraints=(), tol=None, callback=None, options=None)

  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 None 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.

    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. Note that this function may violate the limit because of evaluating gradients by numerical differentiation.

    maxiter int

      Maximum number of iterations.

    iprint int, 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 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.

    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.

  Notes

  The option ftol is exposed via the `scipy.optimize.minimize` interface, but calling `scipy.optimize.fmin_l_bfgs_b` directly exposes factr. The relationship between the two is `ftol = factr * numpy.finfo(float).eps`. I.e., factr multiplies the default machine floating-point precision to arrive at ftol.