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} <= gtol where proj g_i is 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.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.