With the hierarchical architecture, the number of required cost-function computations per second reduces to Figure 7 Illustration of hierarchical, local, window-based disparity estimation.
Second, the classical infinite-horizon approach to control requires a cost function and this requires subjective input.
The iteration criterion requires the cost function J to be as small as possible.
To apply heuristic search algorithms like simulated annealing, we require a cost function that allows the desirability of different codes to be compared.
It is typical for these large datasets and complex computations to require cost prohibitive High Performance Computing (HPC) to function.
Study of their potential functions clearly requires cost-efficient method for expression analysis.
To investigate potential functions of the large number of NATs clearly requires cost-efficient technology for genome-wide expression analysis in a strand-specific manner.
Based on the above gradient algorithm, the pseudo code of the MRO algorithm is described in Algorithm 2. For the gradient method used in Algorithm 2, the number of iterations required to minimize the cost function C is upper bounded by [25] C ( w 0 ) - C ∗ γ + log 2 log 2 ε 0 ε (11).
Estimating a cost function requires complex information on how each farm's outputs (milk, meat and crops) and inputs (labour, energy, feed, capital) interact.But once the cost function has been pinned down, it can be used to identify scale economies.
The advanced exergoeconomic method can explicitly determine the unavoidable costs of both investment and operation and component interdependencies and does not require the application of cost function.
Minimizing this cost function requires the minimum electric motor size and weight to be specified, while still retaining acceptable levels of output torque.
