Friday, 10:30 - 10:55 h, Room: H 0107


María Maciel
A trust region algorithm for the nonconvex unconstrained vector optimization problem

Coauthors: Gabriel A. Carrizo, Pablo A. Lotito


A trust-region-based algorithm for the non convex unconstrained vector optimization problem is
considered. It is a generalization of the algorithms proposed by Fliege, Graña Drumond and Svaiter
(2009) for the convex problem. Similarly to the scalar case, at each iteration, a trust region
subproblem is solved and the step is evaluated. The notions of decrease condition and of predicted
reduction are adapted to the vector case. A rule to update the trust region radius is introduced.
Under differentiability assumptions, the algorithm converges to a Pareto point satisfying a necessary condition and in the convex case to a Pareto point satisfying necessary and sufficient conditions like the procedure proposed by the cited authors.


Talk 1 of the invited session Fri.1.H 0107
"Optimality conditions and constraint qualifications" [...]
Cluster 16
"Nonlinear programming" [...]


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