A Levenberg–Marquardt algorithm for unconstrained multicriteria optimization

To compute one of the nonisolated Pareto-critical points of an unconstrained multicriteria optimization problem a Levenberg–Marquardt algorithm is applied. Sufficient conditions for an error bound are provided to prove its fast local convergence. A globalized version is shown to converge to a Pareto-optimal point under convexity assumptions.