_prepare_scalar_function(fun, x0, jac=None, args=(), bounds=None, epsilon=None, finite_diff_rel_step=None, hess=None)

Parameters

fun : callable

The objective function to be minimized.

fun(x, *args) -> float

where x is an 1-D array with shape (n,) and args is a tuple of the fixed parameters needed to completely specify the function.

x0 : ndarray, shape (n,)

Initial guess. Array of real elements of size (n,), where 'n' is the number of independent variables.

jac : {callable, '2-point', '3-point', 'cs', None}, optional

Method for computing the gradient vector. If it is a callable, it should be a function that returns the gradient vector:

jac(x, *args) -> array_like, shape (n,)

If one of :None:None:`{'2-point', '3-point', 'cs'}` is selected then the gradient is calculated with a relative step for finite differences. If :None:None:`None`, then two-point finite differences with an absolute step is used.

args : tuple, optional

Extra arguments passed to the objective function and its derivatives (:None:None:`fun`, :None:None:`jac` functions).

bounds : sequence, optional

Bounds on variables. 'new-style' bounds are required.

eps : float or ndarray

If :None:None:`jac is None` the absolute step size used for numerical approximation of the jacobian via forward differences.

finite_diff_rel_step : None or array_like, optional

If :None:None:`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(x0) * max(1, abs(x0)) , 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.

hess : {callable, '2-point', '3-point', 'cs', None}

Computes the Hessian matrix. If it is callable, it should return the Hessian matrix:

hess(x, *args) -> {LinearOperator, spmatrix, array}, (n, n)

Alternatively, the keywords {'2-point', '3-point', 'cs'} select a finite difference scheme for numerical estimation. Whenever the gradient is estimated via finite-differences, the Hessian cannot be estimated with options {'2-point', '3-point', 'cs'} and needs to be estimated using one of the quasi-Newton strategies.

Returns

sf : ScalarFunction

Creates a ScalarFunction object for use with scalar minimizers (BFGS/LBFGSB/SLSQP/TNC/CG/etc).

Examples

See :

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GitHub : /scipy/optimize/_optimize.py#175
type: <class 'function'>
Commit: