Unconstrained Steepest Descent Method for Multicriteria Optimization on Riemannian Manifolds
In this paper, we present a steepest descent method with Armijos rule for multicriteria optimization in the Riemannian context. The sequence generated by the method is guaranteed to be well defined. Under mild assumptions on the multicriteria function, we prove that each accumulation point (if any) satisfies first-order necessary conditions for Pareto optimality. Moreover, assuming quasiconvexity of the multicriteria function and nonnegative curvature of the Riemannian manifold, we prove full convergence of the sequence to a critical Pareto point.
steepest descent riemannian manifolds pareto optimality quasiconvexity vector optimization quasi-fejr convergence