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# minimize(method=’COBYQA’)

scipy.optimize.minimize(fun, x0, args=(), method=None, jac=None, hess=None, hessp=None, bounds=None, constraints=(), tol=None, callback=None, options=None)

  Minimize a scalar function of one or more variables using the Constrained Optimization BY Quadratic Approximations (COBYQA) algorithm [1].

  Added in version 1.14.0.

  See also

  For documentation for the rest of the parameters, see `scipy.optimize.minimize`

  Options:
    ——-
    disp bool

      Set to True to print information about the optimization procedure. Default is `False`.

    maxfev int

      Maximum number of function evaluations. Default is `500 * n`, where `n` is the number of variables.

    maxiter int

      Maximum number of iterations. Default is `1000 * n`, where `n` is the number of variables.

    f_target float

      Target value for the objective function. The optimization procedure is terminated when the objective function value of a feasible point (see feasibility_tol below) is less than or equal to this target. Default is `-numpy.inf`.

    feasibility_tol float

      Absolute tolerance for the constraint violation. Default is `1e-8`.

    initial_tr_radius float

      Initial trust-region radius. Typically, this value should be in the order of one tenth of the greatest expected change to the variables. Default is `1.0`.

    final_tr_radius float

      Final trust-region radius. It should indicate the accuracy required in the final values of the variables. If provided, this option overrides the value of tol in the `minimize` function. Default is `1e-6`.

    scale bool

      Set to True to scale the variables according to the bounds. If True and if all the lower and upper bounds are finite, the variables are scaled to be within the range \([-1, 1]\). If any of the lower or upper bounds is infinite, the variables are not scaled. Default is `False`.

  References

  [1]

  COBYQA https://www.cobyqa.com/stable/