Because students use a standard approach to solve this problem, it is a lower-order level 3 question.
The characteristic values of the DAE system are evaluated using a standard routine to solve the generalized characteristic value problem.
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.
In this paper we solve the problem using a standard regularized least square framework in an approximation space spanned by the shifts and dilates of a single compactly supported function ϕ.
In this work, we have used genetic algorithm (GA) for solving the integer programming problem, while the lower level non-linear programming problem is solved using a standard least square optimization routine.
The separation problems are solved using a standard stochastic dual dynamic programming (SDDP) algorithm.
With a low number of parameters, the inverse problem is efficiently solved using a standard, second-order gradient-based optimization algorithm.
The estimation problem is posed as a weighted least squares problem and solved by using a standard Levenberg Marquardt algorithm.
Instead of solving it directly, here, we study an equivalent problem for (7) and reveal that the nonlinear optimization problem can be solved by using a standard iteration algorithm.
Using these schemes, the optimization problems for models with up to six QTL can be solved using a standard computer.
