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|Type:||Artigo de periódico|
|Title:||A direct search method for nonlinear programming|
|Abstract:||An iterative model algorithm Sbr minimizing a Lipschitz-continuous function subject to continuous constraints is introduced. Each iteration, of the method proceeds in two phases. In the first phase, feasibility is improved and, as a result, a more feasible intermediate point is obtained. In the second phase the algorithm tries to obtain a decrease of the objective function on an auxiliary feasible set. The output of the second phase is a trial point that is compared with the current iterate by means of a suitable merit function. If the merit function is sufficiently decreased, the trial point is accepted. Otherwise, it is rejected and the second phase is repeated in a reduced domain. Global convergence results are proved and practical applications are commented.|
direct search methods
|Editor:||Wiley-v C H Verlag Gmbh|
|Appears in Collections:||Artigos e Materiais de Revistas Científicas - Unicamp|
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