Artículos de revistas
Inexact projected gradient method for vector optimization
Registro en:
Computational Optimization And Applications. Springer, v. 54, n. 3, n. 473, n. 493, 2013.
0926-6003
WOS:000316079700002
10.1007/s10589-012-9501-z
Autor
Fukuda, EH
Drummond, LMG
Institución
Resumen
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) In this work, we propose an inexact projected gradient-like method for solving smooth constrained vector optimization problems. In the unconstrained case, we retrieve the steepest descent method introduced by Graa Drummond and Svaiter. In the constrained setting, the method we present extends the exact one proposed by Graa Drummond and Iusem, since it admits relative errors on the search directions. At each iteration, a decrease of the objective value is obtained by means of an Armijo-like rule. The convergence results of this new method extend those obtained by Fukuda and Graa Drummond for the exact version. For partial orders induced by both pointed and nonpointed cones, under some reasonable hypotheses, global convergence to weakly efficient points of all sequences generated by the inexact projected gradient method is established for convex (respect to the ordering cone) objective functions. In the convergence analysis we also establish a connection between the so-called weighting method and the one we propose. 54 3 473 493 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Fundacao de Amparo a Pesquisa do Estado de Rio de Janeiro Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) FAPESP [2010/20572-0] CNPq [480101/2008-6]