7 research outputs found
High performance computing for global optimization problems
In the present work, the multiextremal optimization problems and
a high-performance parallel algorithm for solving these ones are considered. The
investigation of the algorithm scalability has been carried out on the problem class, in
which the computation costs of the functions depended on the iteration point. The
algorithm proposed in the present work can utilize the CPUs (for solving more
complex subproblems) as well as the GPUs (for solving the simple subproblems).
The results of numerical experiments demonstrating the speedup when solving a
series of multiextremal constrained problems are presented.This study was supported by the Russian Science Foundation, project No 16-11-10150
Sequential and parallel algorithms for global minimizing functions with Lipschitzian derivatives
A partition-based global optimization algorithm
Global optimization, Partition-based algorithm, DIRECT-type algorithm,