Convex AMOO is analyzed under Lipschitz and smooth assumptions with a maximum-gap metric, giving simple gradient methods with rates independent of the number of objectives, plus a flawed equal-weights lower bound.
Robust optimization--methodology and applications
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Simple Optimizers for Convex Aligned Multi-Objective Optimization
Convex AMOO is analyzed under Lipschitz and smooth assumptions with a maximum-gap metric, giving simple gradient methods with rates independent of the number of objectives, plus a flawed equal-weights lower bound.