### Navigation

- index
- modules |
- next |
- previous |
- SciPy v0.7 Reference Guide (DRAFT) »
- Optimization and root finding (`scipy.optimize`) »

# scipy.optimize.newton

`scipy.optimize.``newton`(func, x0, fprime=None, args=(), tol=1.48e-08, maxiter=50)

  Given a function of a single variable and a starting point, find a nearby zero using Newton-Raphson.

  fprime is the derivative of the function. If not given, the Secant method is used.

  See also:

  > fmin, fmin_powell, fmin_cg,
  >   fmin_bfgs, fmin_ncg – multivariate local optimizers

  > leastsq – nonlinear least squares minimizer

  > fmin_l_bfgs_b, fmin_tnc,
  >   fmin_cobyla – constrained multivariate optimizers

  > anneal, brute – global optimizers

  > fminbound, brent, golden, bracket – local scalar minimizers

  > fsolve – n-dimenstional root-finding

  > brentq, brenth, ridder, bisect, newton – one-dimensional root-finding

  > fixed_point – scalar and vector fixed-point finder

### Navigation

- index
- modules |
- next |
- previous |
- SciPy v0.7 Reference Guide (DRAFT) »
- Optimization and root finding (`scipy.optimize`) »