تقرير
Online data assimilation in distributionally robust optimization
| العنوان: | Online data assimilation in distributionally robust optimization |
|---|---|
| المؤلفون: | Li, Dan, Martinez, Sonia |
| سنة النشر: | 2018 |
| المجموعة: | Computer Science Mathematics Statistics |
| مصطلحات موضوعية: | Mathematics - Optimization and Control, Electrical Engineering and Systems Science - Signal Processing, Electrical Engineering and Systems Science - Systems and Control, Statistics - Computation, Statistics - Methodology |
| الوصف: | This paper considers a class of real-time decision making problems to minimize the expected value of a function that depends on a random variable $\xi$ under an unknown distribution $\mathbb{P}$. In this process, samples of $\xi$ are collected sequentially in real time, and the decisions are made, using the real-time data, to guarantee out-of-sample performance. We approach this problem in a distributionally robust optimization framework and propose a novel Online Data Assimilation Algorithm for this purpose. This algorithm guarantees the out-of-sample performance in high probability, and gradually improves the quality of the data-driven decisions by incorporating the streaming data. We show that the Online Data Assimilation Algorithm guarantees convergence under the streaming data, and a criteria for termination of the algorithm after certain number of data has been collected. Comment: Appeared in CDC 2018 |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1109/CDC.2018.8619159 |
| URL الوصول: | http://arxiv.org/abs/1803.07984 |
| رقم الأكسشن: | edsarx.1803.07984 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1109/CDC.2018.8619159 |
|---|