The Classified Algorithm With Automatic Differentiation

Evaluation relevant to the partial derivatives of the multivariable functions is often done in the classified method of the support vectors machines, usually by means of the symbolic differentiation or the divided difference. But for the middle and large scale problems, the computation cost by symbolic differentiation is very expensive. When the direction derivative is evaluated, the computation cost by divided difference can be reduced, but it is only one kind of approximate computation. Moreover, it is very difficult to confirm the divided difference interval rightly. The article combines the automatic differentiation with the classified algorithm and researches the classified algorithm with the automatic differentiation, by which the derivatives of the function can be evaluated both exactly and economically. At last, new algorithm is implemented by basic numerical experiments.