Online Mirror Descent for Tchebycheff Scalarization in Multi-Objective Optimization
It addresses the limitation of linear scalarization in capturing non-convex Pareto fronts for researchers and practitioners in optimization and machine learning, offering an incremental improvement with a novel adaptive conversion scheme.
The paper tackles the problem of multi-objective optimization by proposing an online mirror descent algorithm for Tchebycheff scalarization, achieving a convergence rate of O(√(log m/T)) and demonstrating state-of-the-art performance on synthetic and federated learning tasks.
The goal of multi-objective optimization (MOO) is to learn under multiple, potentially conflicting, objectives. One widely used technique to tackle MOO is through linear scalarization, where one fixed preference vector is used to combine the objectives into a single scalar value for optimization. However, recent work (Hu et al., 2024) has shown linear scalarization often fails to capture the non-convex regions of the Pareto Front, failing to recover the complete set of Pareto optimal solutions. In light of the above limitations, this paper focuses on Tchebycheff scalarization that optimizes for the worst-case objective. In particular, we propose an online mirror descent algorithm for Tchebycheff scalarization, which we call OMD-TCH. We show that OMD-TCH enjoys a convergence rate of $O(\sqrt{\log m/T})$ where $m$ is the number of objectives and $T$ is the number of iteration rounds. We also propose a novel adaptive online-to-batch conversion scheme that significantly improves the practical performance of OMD-TCH while maintaining the same convergence guarantees. We demonstrate the effectiveness of OMD-TCH and the adaptive conversion scheme on both synthetic problems and federated learning tasks under fairness constraints, showing state-of-the-art performance.