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

Minimize a scalar function of one or more variables using the L-BFGS-B algorithm.

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 pg_i is the i-th component of the projected gradient. eps : float Step size used for numerical approximation of the jacobian. 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.

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.

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