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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

      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.

      Deprecated since version 1.15.0: This keyword is deprecated and will be removed from SciPy 1.17.0.

    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

      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.

      Deprecated since version 1.15.0: This keyword is deprecated and will be removed from SciPy 1.17.0.

    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.