A Nonmonotone Quasi-Newton Method for Multiobjective Optimization

We propose a nonmonotone quasi-Newton algorithm for unconstrained multiobjective optimization. We give global convergence and local superlinear convergence under reasonable assumptions. Our numerical results show that the nonmonotone quasi-Newton algorithm uses fewer function evaluations than the monotone quasi-Newton algorithm.