numhessian (optim_doc)

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### 4.4 Numerical Hessian function

#### Helptext:

    numhessian(f, {args}, minarg)

    Numeric second derivative 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)

    If the argument
    is a k-vector, the Hessian will be a kxk matrix

    function a = f(x, y)
            a = x'*x + log(y);
    endfunction

    numhessian("f", {ones(2,1), 1})
    ans =

        2.0000e+00   -7.4507e-09
       -7.4507e-09    2.0000e+00

    Now, w.r.t. second argument:
    numhessian("f", {ones(2,1), 1}, 2)
    ans = -1.0000