在线双层优化:对在线交替梯度方法的遗憾分析
Davoud Ataee Tarzanagh1, Parvin Nazari2, Bojian Hou1
1University of Pennsylvania.
概括
本研究介绍了在线双级优化对于时间变化的问题. 它扩展了单级遗憾的界限,并开发了一种新的梯度方法,以有效地限制双级遗憾.
科学领域:
- 优化理论 优化理论
- 机器学习 机器学习
- 运营研究 运营研究
背景情况:
- 在线优化处理的是随着时间的推移到达数据的顺序决策.
- 双级优化涉及嵌套优化问题,在等级决策中很常见.
- 现有的遗憾界限主要用于单级在线优化.
研究的目的:
- 为时间变化的问题引入和分析在线双层优化框架.
- 将现有的遗憾分析从单级扩展到双级在线优化.
- 开发和评估一个新的算法用于这个设置.
主要方法:
- 为在线环境量身定制的新双层遗憾概念的定义.
- 在线交替时间平均梯度方法的开发.
- 对遗憾边界的分析利用问题的平滑性和路径长度属性.
主要成果:
- 延伸遗憾的界限从单级到双级在线优化.
- 介绍了一种新的在线交替梯度方法.
- 遗憾的边界是建立在内外最小化器的路径长度方面.
结论:
- 提出的方法有效地处理时间变化的双层优化问题.
- 新的双层遗憾界限为开发的算法提供了理论上的保证.
- 这项工作为在动态,层次系统中应用在线双层优化打开了道路.
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