An inexact strategy for the projected gradient algorithm in vector optimization problems on variable ordered spaces
arXiv:1701.01710
The paper proposes an inexact projected gradient algorithm for smooth constrained vector optimization problems defined on variable ordered spaces, proves convergence to weakly efficient points under convexity assumptions, and presents computational tests comparing its performance to exact methods.
Abstract
Variable order structures model situations in which the comparison between two points depends on a point-to-cone map. In this paper, an inexact projected gradient method for solving smooth constrained vector optimization problems on variable ordered spaces is presented. It is shown that every accumulation point of the generated sequence satisfies the first order necessary optimality condition. The convergence of all accumulation points to a weakly efficient point is established under suitable convexity assumptions for the objective function. The convergence results are also derived in the particular case in which the problem is unconstrained and if exact directions are taken as descent directions. Furthermore, we investigate the application of the proposed method to optimization models where the domain of the variable order map and the objective function are the same. In this case, similar concepts and convergence results are presented. Finally, some computational experiments designed to illustrate the behavior of the proposed inexact methods versus the exact ones (in terms of CPU time) are performed.