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# scipy.optimize.fmin_cobyla

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

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

  Parameters : | func : callable Function to minimize. In the form func(x, *args). x0 : ndarray Initial guess. cons : sequence Constraint functions; must all be `>=0` (a single function if only 1 constraint). Each function takes the parameters x as its first argument. args : tuple Extra arguments to pass to function. consargs : tuple Extra arguments to pass to constraint functions (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 : {0, 1, 2, 3} Controls the frequency of output; 0 implies no output. Deprecated. disp : {0, 1, 2, 3} Over-rides the iprint interface. Preferred. maxfun : int Maximum number of function evaluations.
  Returns : | x : ndarray The argument that minimises f.

  Examples

  Minimize the objective function f(x,y) = x*y subject to the constraints x**2 + y**2 < 1 and y > 0:

      >>> def objective(x):
      ...     return x[0]*x[1]
      ...
      >>> def constr1(x):
      ...     return 1 - (x[0]**2 + x[1]**2)
      ...
      >>> def constr2(x):
      ...     return x[1]
      ...
      >>> fmin_cobyla(objective, [0.0, 0.1], [constr1, constr2], rhoend=1e-7)

         Normal return from subroutine COBYLA

         NFVALS =   64   F =-5.000000E-01    MAXCV = 1.998401E-14
         X =-7.071069E-01   7.071067E-01
      array([-0.70710685,  0.70710671])

  The exact solution is (-sqrt(2)/2, sqrt(2)/2).

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