optim_doc: numgradient

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### 4.3 Another numerical gradient function

#### Helptext:

    numgradient(f, {args}, minarg)

    Numeric central difference gradient of f with respect
    to argument "minarg".
    * first argument: function name (string)
    * second argument: all arguments of the function (cell array)
    * third argument: (optional) the argument to differentiate w.r.t.
            (scalar, default=1)

    "f" may be vector-valued. If "f" returns
    an n-vector, and the argument is a k-vector, the gradient
    will be an nxk matrix

    Example:
    function a = f(x);
            a = [x'*x; 2*x];
    endfunction
    numgradient("f", {ones(2,1)})
    ans =

      2.00000  2.00000
      2.00000  0.00000
      0.00000  2.00000