scipy.optimize.root_scalar¶

scipy.optimize.
root_scalar
(f, args=(), method=None, bracket=None, fprime=None, fprime2=None, x0=None, x1=None, xtol=None, rtol=None, maxiter=None, options=None)[source]¶ Find a root of a scalar function.
 Parameters
 fcallable
A function to find a root of.
 argstuple, optional
Extra arguments passed to the objective function and its derivative(s).
 methodstr, optional
Type of solver. Should be one of
‘bisect’ (see here)
‘brentq’ (see here)
‘brenth’ (see here)
‘ridder’ (see here)
‘toms748’ (see here)
‘newton’ (see here)
‘secant’ (see here)
‘halley’ (see here)
 bracket: A sequence of 2 floats, optional
An interval bracketing a root. f(x, *args) must have different signs at the two endpoints.
 x0float, optional
Initial guess.
 x1float, optional
A second guess.
 fprimebool or callable, optional
If fprime is a boolean and is True, f is assumed to return the value of the objective function and of the derivative. fprime can also be a callable returning the derivative of f. In this case, it must accept the same arguments as f.
 fprime2bool or callable, optional
If fprime2 is a boolean and is True, f is assumed to return the value of the objective function and of the first and second derivatives. fprime2 can also be a callable returning the second derivative of f. In this case, it must accept the same arguments as f.
 xtolfloat, optional
Tolerance (absolute) for termination.
 rtolfloat, optional
Tolerance (relative) for termination.
 maxiterint, optional
Maximum number of iterations.
 optionsdict, optional
A dictionary of solver options. E.g.,
k
, seeshow_options()
for details.
 Returns
 solRootResults
The solution represented as a
RootResults
object. Important attributes are:root
the solution ,converged
a boolean flag indicating if the algorithm exited successfully andflag
which describes the cause of the termination. SeeRootResults
for a description of other attributes.
See also
show_options
Additional options accepted by the solvers
root
Find a root of a vector function.
Notes
This section describes the available solvers that can be selected by the ‘method’ parameter.
The default is to use the best method available for the situation presented. If a bracket is provided, it may use one of the bracketing methods. If a derivative and an initial value are specified, it may select one of the derivativebased methods. If no method is judged applicable, it will raise an Exception.
Examples
Find the root of a simple cubic
>>> from scipy import optimize >>> def f(x): ... return (x**3  1) # only one real root at x = 1
>>> def fprime(x): ... return 3*x**2
The
brentq
method takes as input a bracket>>> sol = optimize.root_scalar(f, bracket=[0, 3], method='brentq') >>> sol.root, sol.iterations, sol.function_calls (1.0, 10, 11)
The
newton
method takes as input a single point and uses the derivative(s)>>> sol = optimize.root_scalar(f, x0=0.2, fprime=fprime, method='newton') >>> sol.root, sol.iterations, sol.function_calls (1.0, 11, 22)
The function can provide the value and derivative(s) in a single call.
>>> def f_p_pp(x): ... return (x**3  1), 3*x**2, 6*x
>>> sol = optimize.root_scalar(f_p_pp, x0=0.2, fprime=True, method='newton') >>> sol.root, sol.iterations, sol.function_calls (1.0, 11, 11)
>>> sol = optimize.root_scalar(f_p_pp, x0=0.2, fprime=True, fprime2=True, method='halley') >>> sol.root, sol.iterations, sol.function_calls (1.0, 7, 8)