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# 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, 'maxls': 20, 'iprint': -1, 'gtol': 1e-05, 'eps': 1e-08, 'maxiter': 15000, 'ftol': 2.220446049250313e-09, 'maxcor': 10, 'maxfun': 15000})

  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.)

  factr : float

  > The iteration stops when `(f^k - f^{k+1})/max{|f^k|,|f^{k+1}|,1} <= factr * eps`, where `eps` is the machine precision, which is automatically generated by the code. Typical values for factr are: 1e12 for low accuracy; 1e7 for moderate accuracy; 10.0 for extremely high accuracy.

  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 `pg_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.