LibMOON: A Gradient-based MultiObjective OptimizatioN Library in PyTorch
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917800360017920 |
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| author | Zhang, Xiaoyuan Zhao, Liang Yu, Yingying Lin, Xi Chen, Yifan Zhao, Han Zhang, Qingfu |
| author_facet | Zhang, Xiaoyuan Zhao, Liang Yu, Yingying Lin, Xi Chen, Yifan Zhao, Han Zhang, Qingfu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_02969 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LibMOON: A Gradient-based MultiObjective OptimizatioN Library in PyTorch Zhang, Xiaoyuan Zhao, Liang Yu, Yingying Lin, Xi Chen, Yifan Zhao, Han Zhang, Qingfu Mathematical Software Machine Learning Optimization and Control 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. |
| title | LibMOON: A Gradient-based MultiObjective OptimizatioN Library in PyTorch |
| topic | Mathematical Software Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2409.02969 |