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# root_scalar(method=’newton’)

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:
    ——-
    argstuple, optional

      Extra arguments passed to the objective function and its derivative.

    xtolfloat, optional

      Tolerance (absolute) for termination.

    rtolfloat, optional

      Tolerance (relative) for termination.

    maxiterint, optional

      Maximum number of iterations.

    x0float, required

      Initial guess.

    fprimebool 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.