التفاصيل البيبلوغرافية
| العنوان: |
Data Assimilation and Online Optimization With Performance Guarantees. |
| المؤلفون: |
Li, Dan, Martinez, Sonia |
| المصدر: |
IEEE Transactions on Automatic Control; May2021, Vol. 66 Issue 5, p2115-2129, 15p |
| مصطلحات موضوعية: |
DISTRIBUTION (Probability theory), ROBUST optimization, SURETYSHIP & guaranty, RANDOM variables, STOCHASTIC systems, ALGORITHMS |
| مستخلص: |
This article considers a class of real-time stochastic optimization problems dependent on an unknown probability distribution. In the considered scenario, data are streaming frequently while trying to reach a decision. Thus, we aim to devise a procedure that incorporates samples (data) of the distribution sequentially and adjusts decisions accordingly. We approach this problem in a distributionally robust optimization framework and propose a novel Online Data Assimilation Algorithm (OnDA Algorithm) for this purpose. This algorithm guarantees out-of-sample performance of decisions with high probability, and gradually improves the quality of the decisions by incorporating the streaming data. We show that the OnDA Algorithm converges under a sufficiently slow data streaming rate, and provide a criteria for its termination after certain number of data have been collected. Simulations illustrate the results. [ABSTRACT FROM AUTHOR] |
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