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.
Parallel Algorithms in Computational Science&Engineering - Parallelism as Enabling Technology in CSE Applications