Employing standard optimization techniques, optimal conditions are determined for typical operating parameters.
Therefore, the problem can be solved using standard optimization methods and optimal can thereby be obtained.
Our optimization problem is very similar to the standard optimization problem solved for SVMs, hence we use a standard optimizer for SVMs to solve our problem.
Moreover, we noted that the objective function that needs to be optimized is highly discontinuous and nonlinear so that standard optimization techniques, such as genetic algorithms, simulated annealing and MCMC do not provide an optimal solution.
Similar to reference [19], the optimal solution can be obtained based on waterfilling distribution via standard optimization techniques.
Despite efforts to optimize the methodology, misleading results are still possible, even when standard optimization approaches are followed.
We use binary linear programming and show that the design of a regulated, optimal metabolic network of minimal functionality can be formulated as a standard optimization problem, where EM and regulation show up as constraints.
In fact, in the increasing-returns environment I've just sketched, standard optimization makes little sense.
Thus, it can be calculated by the standard optimization toolbox.
In order to perform parameter identification, the forward simulation method is coupled to standard optimization tools.
The filter design is formulated as a standard optimization problem with linear matrix inequality (LMI) constraints.
