Edge-based AI models will surely be increasingly important as the efficiency of algorithms improves, the power of devices rises and the demand for quick-turnaround applications grows.
Even if some societies remain ostensibly democratic, the increasing efficiency of algorithms will still shift more and more authority from individual humans to networked machines.
Improving the efficiency of algorithms for this purpose is therefore of great importance.
The successful application validates efficiency of algorithms in solving complex aerodynamic optimization problem.
Indeed, the combination of adaptive algorithms and uniform refinement meets the certain mesh-grading properties, thus improving the efficiency of Algorithms 1 3 (see Tables 1 3 in Sect. 6).
In general, the efficiency of algorithms to estimate SNP effects can be improved by avoiding redundant computations.
This might seriously affect the efficiency of Algorithm 1.1.
In this section, we present numerical experiments in order to investigate the efficiency of Algorithm 2.2.
We approximate the solution of SIE by the collocation methods and utilize the FMM to improve the efficiency of algorithm.
Here, let us see how the step size conditions (1.3), (1.5), (1.12), and (1.13) affect the efficiency of Algorithm (1.2).
