Inexact projected gradient method for vector optimization

Autor(es)

Fukuda, Ellen H. ^[1] ; Grana Drummond, L. M. ^[2]

Afiliação do(s) autor(es)

^[1] Univ Estadual Campinas, Inst Math Stat & Comp Sci, BR-13083859 Campinas, SP - Brazil

^[2] Univ Fed Rio de Janeiro, Fac Business & Adm, BR-22290240 Rio De Janeiro, RJ - Brazil

• Tipo de documento: Artigo Científico
• Fonte: COMPUTATIONAL OPTIMIZATION AND APPLICATIONS; v. 54, n. 3, p. 473-493, APR 2013.
• Citações Web of Science: 11

Resumo

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. (AU)

• Processo FAPESP: 10/20572-0 - Penalidades exatas para otimização não linear e programação cônica de segunda ordem
• Beneficiário: Ellen Hidemi Fukuda
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado