Skip to main content
`Ctrl`+`K`

# root_scalar(method=’halley’)

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)

  See also

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

  Options:
    ——-
    args tuple, optional

      Extra arguments passed to the objective function and its derivatives.

    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, required

      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.

    fprime2 bool or callable, required

      If fprime2 is a boolean and is True, f is assumed to return the value of 1st and 2nd derivatives along with the objective function. fprime2 can also be a callable returning the 2nd 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.