1 - 5 of 5 Chapters
[The unconstrained optimization problem is presented: the derivative and the derivative-free methods, as well as the optimality conditions are discussed. A short presentation of the two-level random search method for the unconstrained optimization of functions, for which the derivative...
[The purpose of this chapter is to present a two-level random search method for unconstrained optimization and the corresponding algorithm. The idea of the algorithm is to randomly generate a number of trial points in some domains at two levels. At the first level, a number of trial points are...
[The purpose of this chapter is to prove the convergence of the algorithm. It is shown that the evolution of the maximum distance among the trial points and the local trial points tend to zero. For continuous and lower bounded functions, the two-level random search algorithm is convergent to a...
[Some numerical results with an implementation of the DEEPS algorithm, as well as the evolution of the minimizing function values, along the iterations of the optimization process are presented in this chapter. It is shown that the optimization process has two phases. In the first one, the...
[For solving unconstrained optimization problems, a new derivative-free algorithm is presented. The idea of this algorithm is to capture a deep view of the landscape of the minimizing function around the initial point by randomly generating some trial points at two levels. This is a new approach...
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