Large scale optimization problems are tractable only if they are somehow decomposed. Hierarchical decompositions are inappropriate for some types of problems and do not parallelize well. Sobieszczanski-Sobieski has proposed a nonhierarchical decomposition strategy for nonlinear constrained optimization that is naturally parallel. Despite some successes on engineering problems, the algorithm as originally proposed fails on simple two dimensional quadratic programs.

Here, the algorithm is carefully analyzed by testing it on simple quadratic programs, thereby recognizing the problems with the algorithm. Different modifications are made to improve its robustness and the best version is tested on a larger dimensional example. Some of the changes made are very fundamental, affecting the updating of the various tuning parameters present in the original algorithm.

The algorithm involves solving a given problem by dividing it into subproblems and a final coordination phase. The results indicate good success with small problems. On testing it with a larger dimensional example, it was discovered that there is a basic flaw in the coordination phase which needs to be rectified.