LibMOON: A Gradient-based MultiObjective OptimizatioN Library in PyTorch

• URL: http://arxiv.org/abs/2409.02969v3
• Date: Fri, 11 Oct 2024 16:31:46 GMT
• Title: LibMOON: A Gradient-based MultiObjective OptimizatioN Library in PyTorch
• Authors: Xiaoyuan Zhang, Liang Zhao, Yingying Yu, Xi Lin, Yifan Chen, Han Zhao, Qingfu Zhang,
• Abstract summary: Multiobjective optimization problems (MOPs) are prevalent in machine learning. This paper introduces LibMOON, the first multiobjective optimization library that supports state-of-the-art gradient-based methods.
• Score: 19.499639344055275
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multiobjective optimization problems (MOPs) are prevalent in machine learning, with applications in multi-task learning, learning under fairness or robustness constraints, etc. Instead of reducing multiple objective functions into a scalar objective, MOPs aim to optimize for the so-called Pareto optimality or Pareto set learning, which involves optimizing more than one objective function simultaneously, over models with thousands / millions of parameters. Existing benchmark libraries for MOPs mainly focus on evolutionary algorithms, most of which are zeroth-order / meta-heuristic methods that do not effectively utilize higher-order information from objectives and cannot scale to large-scale models with thousands / millions of parameters. In light of the above gap, this paper introduces LibMOON, the first multiobjective optimization library that supports state-of-the-art gradient-based methods, provides a fair benchmark, and is open-sourced for the community.

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