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:
- ——-
- dispNone 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. 
- maxcorint
- 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.) 
- ftolfloat
- The iteration stops when - (f^k - f^{k+1})/max{|f^k|,|f^{k+1}|,1} <= ftol.
- gtolfloat
- The iteration will stop when - max{|proj g_i | i = 1, ..., n} <= gtolwhere- proj g_iis the i-th component of the projected gradient.
- epsfloat or ndarray
- If jac is None the absolute step size used for numerical approximation of the jacobian via forward differences. 
- maxfunint
- Maximum number of function evaluations. Note that this function may violate the limit because of evaluating gradients by numerical differentiation. 
- maxiterint
- Maximum number of iterations. 
- iprintint, 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. 
- maxlsint, optional
- Maximum number of line search steps (per iteration). Default is 20. 
- finite_diff_rel_stepNone 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.minimizeinterface, but calling- scipy.optimize.fmin_l_bfgs_bdirectly 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.