By solving (5) below, we finally have the probabilities of the MIM capture as a function of distance between the APs as shown in Figure 9.
This maximization problem is solved below.
From the application point of view, the theory of finitary programs shows how the existing technology for answer set programming can be extended from problem solving below the second level of the polynomial hierarchy to all semidecidable problems.
This quantitative measure was used to explore sort rule patterns at a coarser granularity and quantify the direction and magnitude of associations among card sort expertise levels and problem solving performances (discussed below).
Although Jin et al. (2012) previously described adaptive evolution of RNASEL in Primates, we included the gene to allow comparison with bats and mapping of selected sites on the 3D structure, which has recently been solved (see below).
This optimization problem can be solved by below Lagrangian function: (3) L (W, b, α ) = 1 2 W ⋅ W − ∑ i = 1 S α i (y i (W ⋅ X i + b ) − 1 ) In above equation, s shows the number of training samples and α i denotes Lagrange multipliers.
Of course, and similarly to the O -notation case, when the time taken by the algorithm to solve the problem f is unknown, the function g yields an 'approximate' information of the running time of the algorithm in the sense that the algorithm takes a time to solve the problem bounded below by g.
