The inexact projected gradient method for quasiconvex vector optimization problems
J.Y. Bello Cruz(yunier.bellogmail.com)
Abstract: Vector optimization problems are a generalization of multiobjective optimization in which the preference order is related to an arbitrary closed and convex cone, rather than the nonnegative octant. Due to its real life applications, it is important to have practical solution approaches for computing. In this work, we consider the inexact projected gradient-like method for solving smooth constrained vector optimization problems. Basically, we prove global convergence of any sequence produced by the method to a stationary point assuming that the objective function of the problem is $K$-quasiconvex, instead of the stronger $K$-convexity assumed in the literature.
Keywords: Gradient-like method; Vector optimization; $K$-- quasiconvexity.
Category 1: Other Topics (Multi-Criteria Optimization )
Category 2: Convex and Nonsmooth Optimization (Generalized Convexity/Monoticity )
Category 3: Nonlinear Optimization
Entry Submitted: 12/13/2013
Modify/Update this entry
|Visitors||Authors||More about us||Links|
Search, Browse the Repository
Give us feedback
|Optimization Journals, Sites, Societies|