  Multi-objective optimization studies the process of seeking multiple
competing desiderata in some operation. Solution techniques highlight marginal
tradeoffs associated with weighing one objective over others. In this paper, we
consider time-varying multi-objective optimization, in which the objectives are
parametrized by a continuously varying parameter and a prescribed computational
budget is available at each time instant to algorithmically adjust the decision
variables to accommodate for the changes. We prove regret bounds indicating the
relative guarantees on performance for the competing objectives.
