An incremental descent method for multi-objective optimization

ABSTRACT Multi-objective steepest descent, under the assumption of lower-bounded objective functions with L-Lipschitz continuous gradients, requires gradient and function computations to produce a measure of proximity to critical conditions akin to in the single-objective setting, where m is the number of objectives considered. We reduce this to with a multi-objective incremental approach that has a computational cost that does not grow with the number of objective functions m.