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# scipy.optimize.bisect

`scipy.optimize.``bisect`(f, a, b, args=(), xtol=1e-12, rtol=4.4408920985006262e-16, maxiter=100, full_output=False, disp=True)

  Find root of a function within an interval.

  Basic bisection routine to find a zero of the function f between the arguments a and b. f(a) and f(b) cannot have the same signs. Slow but sure.

  Parameters: | f : function Python function returning a number. f must be continuous, and f(a) and f(b) must have opposite signs. a : number One end of the bracketing interval [a,b]. b : number The other end of the bracketing interval [a,b]. xtol : number, optional The routine converges when a root is known to lie within xtol of the value return. Should be >= 0. The routine modifies this to take into account the relative precision of doubles. rtol : number, optional The routine converges when a root is known to lie within rtol times the value returned of the value returned. Should be >= 0. Defaults to `np.finfo(float).eps * 2`. maxiter : number, optional if convergence is not achieved in maxiter iterations, an error is raised. Must be >= 0. args : tuple, optional containing extra arguments for the function f. f is called by `apply(f, (x)+args)`. full_output : bool, optional If full_output is False, the root is returned. If full_output is True, the return value is `(x, r)`, where x is the root, and r is a RootResults object. disp : bool, optional If True, raise RuntimeError if the algorithm didn’t converge.
  Returns: | x0 : float Zero of f between a and b. r : RootResults (present if `full_output = True`) Object containing information about the convergence. In particular, `r.converged` is True if the routine converged.

  See also

  `brentq`, `brenth`, `bisect`, `newton`

  `fixed_point`
    scalar fixed-point finder
  `fsolve`
    n-dimensional root-finding