一个投资组合方法大规模并行贝叶斯优化
Mickaël Binois1, Nicholson Collier2, Jonathan Ozik3,2
1Inria, Université Côte d'Azur, CNRS, LJAD, Sophia Antipolis, France.
概括
这项研究引入了一个可扩展的策略,用于并行贝叶斯优化,显著加速昂贵的黑子函数评估. 新方法有效地处理大量批量批量处理杂的多目标优化任务.
科学领域:
- 计算科学 计算科学
- 优化理论 优化理论
- 机器学习 机器学习
背景情况:
- 传统的优化研究评估设计顺序,这是耗时的.
- 批量贝叶斯优化使用替代模型进行并行评估,但在大规模批量处理方面存在困难.
研究的目的:
- 为大规模批量贝叶斯优化开发一个可扩展的策略.
- 在大型并行评估中解决勘探/开采权衡问题.
- 为了提高噪音和多目标优化问题的效率.
主要方法:
- 为大规模批量贝叶斯优化提出了一个可扩展的策略.
- 专注于勘探/开采权衡和投资组合分配.
- 将这种方法与对单一和多目标任务噪音功能的现有方法进行了比较.
主要成果:
- 与现有方法相比,实现了数量级的速度改进.
- 与当前方法相比,保持了类似或更好的性能.
- 在噪音优化中证明了大规模批处理的可扩展性.
结论:
- 拟议的可扩展策略显著提高了并行贝叶斯优化效率.
- 这种方法对于大规模,杂和多目标的优化问题是有效的.
- 为加速复杂的模拟和设计研究提供了一个有前途的解决方案.
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