root_scalar(method=’newton’)¶

scipy.optimize.root_scalar(args=(), method='newton', x0=None, options={})

See also

For documentation for the rest of the parameters, see scipy.optimize.root_scalar

Options:

Options:	args : tuple, optional Extra arguments passed to the objective function and its derivative. xtol : float, optional Tolerance (absolute) for termination. rtol : float, optional Tolerance (relative) for termination. maxiter : int, optional Maximum number of iterations. x0 : float, required Initial guess. fprime : bool or callable, optional If fprime is a boolean and is True, f is assumed to return the value of derivative along with the objective function. fprime can also be a callable returning the derivative of f. In this case, it must accept the same arguments as f. options: dict, optional Specifies any method-specific options not covered above

args : tuple, optional: Extra arguments passed to the objective function and its derivative.
xtol : float, optional: Tolerance (absolute) for termination.
rtol : float, optional: Tolerance (relative) for termination.
maxiter : int, optional: Maximum number of iterations.
x0 : float, required: Initial guess.
fprime : bool or callable, optional: If fprime is a boolean and is True, f is assumed to return the value of derivative along with the objective function. fprime can also be a callable returning the derivative of f. In this case, it must accept the same arguments as f.
options: dict, optional: Specifies any method-specific options not covered above