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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.