Sentence examples for both computational time and from inspiring English sources
Parallel computing provides alternative design options for heuristic algorithms, as well as the opportunity to obtain performance benefits in both computational time and solution quality of these heuristics.
The suggested approaches outperformed the previous study in terms of both computational time and the solution quality by reducing the overall system cost.
The results indicate clearly the significant superiority of the proposed approach in terms of both computational time and the accuracy of the results.
The high order of accuracy reduces the number of grid points required in smooth parts of the flow which leads to efficiency in both computational time and memory.
Experiments show that the proposed algorithm is efficient to produce longer – non redundant – patterns, and that the new data representation is efficient for both computational time and memory usage.
Although the solution of these systems can be carried out using either direct or iterative methods, in practice the matrices involved are usually very large and sparse (particularly for 3D problems) so an iterative approach is often advantageous in terms of both computational time and memory requirements.
We demonstrate through multiple case studies that the proposed approach outperforms direct application of commercial solvers, significantly reducing both computational times and memory usage.
We compared both the computational time and the performance of all three algorithms across all 10 published datasets.
The comparison study includes both the computational time and the quality of the concept-based front representation.
Taking both the computational time and solution accuracy into consideration, the model by Tenneti et al. (2011) seems to be the optimal choice for the considered cases, which is closely followed by the model of Gidaspow et al. (1994).
Real-coded GAs (RCGAs) utilize real values that allow for both improved computational time and memory as compared to BCGAs, making the optimization of multi-dimensional and high-precision problems more feasible.
