Continuous OptimizationNonmonotone gradient methods for vector optimization with a portfolio optimization application

- Vector optimization is studied.
- Two nonmonotone gradient algorithms are proposed for vector optimization.
- The global and local convergence results for the new algorithms are presented.
- The efficiency of the new algorithm is shown by an application to a portfolio optimization problem.

This paper proposes two nonmonotone gradient algorithms for a class of vector optimization problems with a C−convex objective function. We establish both the global and local convergence results for the new algorithms. We then apply the new algorithms to a portfolio optimization problem under multi-criteria considerations.