The nine quadratically convergent algorithms for function minimization appearing in Ref. 1 are tested through numerical examples. Both a quadratic function and a nonquadratic function are investigated. For the quadratic function, the results show that, if high-precision arithmetic together with high accuracy in the one-dimensional search is employed, all the algorithms behave identically: they all produce the same sequence of points and they all lead to the minimal point in the same number of iterations (this number is equal at most to the number of variables). For the nonquadratic function, the results show that some of the algorithms behave identically and, therefore, any one of them can be considered to be representative of the entire class. The effect of different restarting conditions on the convergence characteristics of the algorithms is studied. Proper restarting conditions for faster convergence are given.