## Experiences with interior point method across a range of parallel computing architectures

The interior point methods for linear programming in general share the common characteristic that most computational efforts are spent in factorizing and solving the symmetric positive semi-definite matrix that results from the sparse least square equations. The number of iterations of most interior point methods is fairly small and is almost invariant to changes in problem dimensions. For numerical reasons, it is unlikely that the average number of iterations can be greatly reduced. Therefore research into speeding up the computation of the IPM concentrates mostly on improving solution methods for the sparse least square equations. The symmetric matrix resulting frommore »