### Navigation

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

# scipy.optimize.fmin_cobyla

`scipy.optimize.``fmin_cobyla`(func, x0, cons, args=(), consargs=None, rhobeg=1.0, rhoend=0.0001, iprint=1, maxfun=1000)

  Minimize a function using the Constrained Optimization BY Linear Approximation (COBYLA) method

  Arguments:

  func – function to minimize. Called as func(x, *args)

  x0 – initial guess to minimum

  cons – a sequence of functions that all must be >=0 (a single function
    if only 1 constraint)

  args – extra arguments to pass to function

  consargs – extra arguments to pass to constraints (default of None means
    use same extra arguments as those passed to func). Use () for no extra arguments.

  rhobeg – reasonable initial changes to the variables

  rhoend – final accuracy in the optimization (not precisely guaranteed)

  iprint – controls the frequency of output: 0 (no output),1,2,3

  maxfun – maximum number of function evaluations.

  Returns:

  x – the minimum

  See also:

  > scikits.openopt, which offers a unified syntax to call this and other solvers

  > 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 fixed-point finder

### Navigation

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