   However, the linear approximations are likely only good
    approximations near the current simplex, so the linear program is
    given the further requirement that the solution, which
    will become x_(j+1), must be within RHO_j from x_j. RHO_j only
    decreases, never increases. The initial RHO_j is rhobeg and the
    final RHO_j is rhoend. In this way COBYLA's iterations behave
    like a trust region algorithm.

    Additionally, the linear program may be inconsistent, or the
    approximation may give poor improvement. For details about
    how these issues are resolved, as well as how the points v_i are
    updated, refer to the source code or the references below.


    References
    ----------
    Powell M.J.D. (1994), "A direct search optimization method that models
    the objective and constraint functions by linear interpolation.", in
    Advances in Optimization and Numerical Analysis, eds. S. Gomez and
    J-P Hennart, Kluwer Academic (Dordrecht), pp. 51-67

    Powell M.J.D. (1998), "Direct search algorithms for optimization
    calculations", Acta Numerica 7, 287-336

    Powell M.J.D. (2007), "A view of algorithms for optimization without
    derivatives", Cambridge University Technical Report DAMTP 2007/NA03


    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]
        ...
        >>> from scipy.optimize import fmin_cobyla
        >>> fmin_cobyla(objective, [0.0, 0.1], [constr1, constr2], rhoend=1e-7)
        array([-0.70710685,  0.70710671])

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



    zLcons must be a sequence of callable functions or a single callable function.Nc