The solution of the optimization problem represents the optimal decision, translated in real-time procedure to the acquisition and control level.
The solution of the optimization function was the optimal protein path vector x → of N elements x i, representing protein i was in the lowest-cost path or not.
According to the solution of the optimization problem, we analyze and present optimal opportunistic spectrum access algorithms in several cases that the idle period follows uniform distribution, exponential distribution, and Pareto or generalized Pareto distribution.
In the following, we use the solution of the optimization problem of Section "The problem of optimal SINR target selection" as a reference case which uses full channel knowledge including fast fading (Rayleigh) information.
Here we consider the stability of the solution of the optimization problem with respect to small perturbation in the data.
The solution of the optimization is a set of process parameters and optimal profiles of heat and material fluxes over the reaction coordinate which minimize the production costs.
The solution of the optimization problem represents the computationally taxing component of the method.
While, for the solution of the optimization problem the Optimization Computing Platform (OCP) was used.
Continuous change of the loading constraint causes bifurcation of the solution of the optimization problem.
For the solution of the optimization problem, a highly efficient evolutionary algorithm is adopted.
We derive a number of theoretical results on the solution of the optimization problem.
