  Interior point methods are among the most popular techniques for large scale
nonlinear optimization, owing to their intrinsic ability of scaling to
arbitrary large problem sizes. Their efficiency has attracted in recent years a
lot of attention due to increasing demand for large scale optimization in
industry and engineering. A parallel interior point method is discussed that
exploits the intrinsic structure of large-scale nonlinear optimization problems
so that the solution process can employ massively parallel high-performance
computing infastructures. Since the overall performance of interior point
methods relies heavily on scalable sparse linear algebra solvers, particular
emphasis is given to the underlying algorithms for the distributed solution of
the associated sparse linear systems obtained at each iteration from the
linearization of the optimality conditions. The interior point algorithm is
implemented in a object-oriented parallel IPM solver and applied for the
solution of large scale optimal control problems solved in a daily basis for
the secure transmission and distribution of electricity in modern power grids.
