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A sequential quadratic programming algorithm that combines merit function and filter ideas

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Autor(es):
Gomes, Francisco A. M.
Número total de Autores: 1
Tipo de documento: Artigo Científico
Fonte: COMPUTATIONAL & APPLIED MATHEMATICS; v. 26, n. 3, p. 43-pg., 2007-01-01.
Resumo

A sequential quadratic programming algorithm for solving nonlinear programming problems is presented. The new feature of the algorithm is related to the definition of the merit function. Instead of using one penalty parameter per iteration and increasing it as the algorithm progresses, we suggest that a new point is to be accepted if it stays sufficiently below the piecewise linear function defined by some previous iterates on the (f, parallel to C parallel to(2)(2))-space. Therefore, the penalty parameter is allowed to decrease between successive iterations. Besides, one need not to decide how to update the penalty parameter. This approach resembles the filter method introduced by Fletcher and Leyffer [Math. Program., 91 (2001), pp. 239-269], but it is less tolerant since a merit function is still used. Numerical comparison with standard methods shows that this strategy is promising. (AU)

Processo FAPESP: 04/05891-1 - Dois algoritimos para programacao nao linear de grande porte.
Beneficiário:Francisco de Assis Magalhães Gomes Neto
Modalidade de apoio: Auxílio à Pesquisa - Regular