On a multiprocessor computer, the concurrent optimizer launches distinct LP and QP optimizers on multiple threads, terminating as soon as the first optimizer finishes. The first thread uses the same strategy as the single-processor automatic LPMethod setting (0). If a second thread is available, the concurrent optimizer runs the barrier optimizer on it. If a third processor is available, dual simplex, primal simplex, and barrier are all run. All further available threads are devoted to making the barrier optimization parallel. It should be noted that the barrier optimization is not considered complete until the crossover step has been performed and simplex re-optimization has converged; in other words, regardless of which optimizer turns out to be the fastest, the concurrent optimizer always returns a basic solution at optimality.

The concurrent optimizer requires more memory than any individual optimizer, and of course it adds system load by consuming more aggregate CPU time than the fastest individual optimizer would alone. But the advantages offered in terms of robust solution of models, and assurance in most cases of the minimum solution time, will make it attractive in many situations.