  It is possible to solve unbounded convex vector optimization problems (CVOPs)
in two phases: (1) computing or approximating the recession cone of the upper
image and (2) solving the equivalent bounded CVOP where the ordering cone is
extended based on the first phase (Wagner et al., 2023). In this paper, we
consider unbounded CVOPs and propose an alternative solution methodology to
compute or approximate the recession cone of the upper image. In particular, we
relate the dual of the recession cone with the Lagrange dual of weighted sum
scalarization problems whenever the dual problem can be written explicitly.
Computing this set requires solving a convex (or polyhedral) projection
problem. We show that this methodology can be applied to semidefinite,
quadratic and linear vector optimization problems and provide some numerical
examples.
