Implementations and performance of nonlinear CG methods by TAO on Dawning2000+

Nonlinear conjugate gradient methods (CG) are typical unconstrained optimization methods. As the optimization problems to be solved become larger, the dependence on efficient and scalable software is severe. Toolkit for Advanced Optimization (TAO) is a parallel package that can currently solve several kinds of optimization problems. In this paper, we give the framework of several variants of CG: CGFR, CGPR, CGPRP and their implementations in TAO 1.5, which have been tested up to 64 processors on Dawning2000 to solve problems with up to 10 variables. The results show that the scalability of CG implementations in TAO 1.5 is excellent.