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Data Assimilation and Online Optimization With Performance Guarantees
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DOI：10.1109/TAC.2020.3005681.png)
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Abstract
En 中文
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.
Keywords:
Optimization
Uncertainty
Data assimilation
Random variables
Probability distribution
Real-time systems
Decision making
Computational methods
intelligent systems
optimization
optimization algorithms
stochastic systems
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