Furthermore because sensitivity matrices that are necessary for obtaining this solution can be computed recursively, this technique is computationally efficient and is appropriate for online implementation.
But simply obtaining this solution does not mean we understand the essential mechanism of the dynamics, and the computational effort may be huge when the mean number of molecules is quite large in this system.
The large-scale nature of real-world systems results in computational difficulties in obtaining this exact solution, and so we provide an approximate formulation that is easier to solve and which becomes exact as the fleet size becomes large.
The key step toward obtaining this general solution is the derivation of a simple and compact boundary integral expression for the eigenfunctions in the extended Stroh formalism applied to Eshelby's problem.
In order to obtain the solution to this problem it is necessary to find the solution of the system of linear inequalities (that is, the set of n values of the variables xi that simultaneously satisfies all the inequalities).
Genetic algorithm is applied to obtain the solution of this model.
The NBMF model has applied NBMF-PSO algorithms to iteratively obtain the solution of this game for searching for the optimal solution of bilevel programming models.
Since BLC-IRK minimizes the number of nodes needed to obtain the solution, in this problem we achieve speed close to that of the traditional explicit multistep methods.
In this section, we obtain the solution of (1)–(3) in terms of the corresponding difference equations system.
If a partition (vehicle-subset assignment satisfying the capacity and the distance (time) constraints) can be obtained for this solution, a solution to the original problem is obtained.
