Partially separable optimization and parallel computing

Parallel processors are becoming an attractive option for meeting the requirements to solve large nonlinear optimization problems and the partially separable methods are ideal candidates for parallel computing. This paper proposes implementation techniques for such methods. Computational experiments on an IBM 3090-200 and on a simulated multiprocessor are presented. The performance of both implementations is compared against a reference serial implementation.