Fast automatic differentiation and its application to flight vehicle parameter optimization

Aim. Traditional automatic differentiation (AD) method is widely used in the optimization of flight vehicle parameters but, in our opinion, it is deficient in speed. We now present a fast AD, called by us improved automatic differentiation (IAD). In the full paper, we explain our IAD in detail. In this abstract, we just add some pertinent remarks to listing the two topics of explanation. The first topic is: traditional AD. The second topic is: our IAD. In the second topic, the most important thing is eq. (4) in the full paper, which is expressed in matrix form and which marks clearly the difference between IAD and traditional AD; the proper utilization of eq. (4) is mainly responsible for making IAD fast through the reduction of time consumed in making very frequent calculations of gradients. Finally we apply our IAD method to the optimization of the parameters of a missile wing. The numerical results are summarized in one table and three figures in the full paper. These numerical results do show preliminarily that our IAD method, as compared with traditional AD method, can reduce the time of calculations of gradients by about 50-65% while retaining the same accuracy.