This paper describes an approach to constructing derivative-free algorithms for unconstrained optimization that are easy to implement on parallel machines. A special feature of this approach is the ease with which algorithms can be generated to take advantage of any number of processors and to adapt to any cost ratio of communication to function evaluation.
Numerical tests show speed-ups on two fronts.
The cost of synchronization being minimal, the speed-up is almost linear with the addition of more processors, i.e., given a problem and a search strategy, the decrease in execution time is proportional to the number of processors added. For this reason we employed an algorithm that used no derivative information. Doc 15 Iterative Methods for Nonlinear Optimization Problems - Jacoby, Kowalik et al. The graph only includes citing articles where the year of publication is known.