Scalable Algorithms in Optimization: Computational Experiments

We survey techniques in the Toolkit for Advanced Optimization (TAO) for developing scalable algorithms for mesh-based optimization problems on distributed architectures. We discuss the distribution of the mesh, the computation of the gradient and the Hessian matrix, and the use of preconditioners. We show that these techniques, together with mesh sequencing, can produce results that scale with mesh size.