On finite horizons and for arbitrary exo-systems, we prove that our control is an agreeable plan as it approximates the computational expensive, time-varying optimal control of any suitably large horizon.
We use GPU CUDA programming to perform all the computational expensive algorithmic processing, with the ultimate goal to connect the acquired medical imaging data with the final diagnostic outcome for the patient.
While it is imperative the fitness function be effectively parallelizable on the GPU, because it is the most computational expensive task of the algorithm, results indicate that if this is the case, speedups of several orders of magnitude are possible compared to conventional multi-core CPUs.
However, as the experiments shown in [5],[11],[15], applying the MC simulation to estimate the EIS is computational expensive, and the standard greedy algorithm with MC simulation (run 10,000 times to get the average) requires days to select 50 seeds in some real-world social networks with tens of thousands of nodes.
(2) The commonly used diffusion model in optical imaging breaks down, in general, for probing the subsurface and computational expensive methods have to be used to model backscattering light at a close source-detector separation [ 9]. (3) The unknown surface properties of the biological sample such as superficial structure and roughness impact the spatial distribution of the backscattering light.
Both methods are suboptimal feature selection in comparison to the exhaustive search; however, they have the advantage of being less computational expensive than the exhaustive search method, and they are simple to implement.
Since the precise optimization model is typically time consuming and computational expensive, the surrogate model is constructed using data drawn from the precise model, and provides fast approximation of the objective at new design points.
The drawback of the method is that it is computational expensive, due to the particle filtering sampling process.
However, except BWs, all the other methods are based on geodesic distance computation, which is computational expensive.
On the other hand, the first Volterra and the Fokker Planck CDF are less computational expensive.
Advanced uncertainty propagation methods are applied in order to face the difficulties by using computational expensive models and by using individual-based models.
