An Application of Online Learning to Spacecraft Memory Dump Optimization
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Abstract (arXiv)
In this paper, we present a real-world application of online learning with expert advice to the field of Space Operations, testing our theory on real-life data coming from the Copernicus Sentinel-6 satellite. We show that in Spacecraft Memory Dump Optimization, a lightweight Follow-The-Leader algorithm leads to an increase in performance of over 60% when compared to traditional techniques.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 26 references that are in the map. First posted on arXiv 14 Feb 2022.
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12 of 26 references.
References · 12
- 2021Bilateral Trade: A Regret Minimization Perspectivecited by 29
- 2018Dynamic Pricing with Finitely Many Unknown Valuationscited by 23
- 2019Cooperative Online Learning: Keeping your Neighbors Updatedcited by 22
- 2021A Regret Analysis of Bilateral Tradecited by 19
- 2021Nonstochastic Bandits with Composite Anonymous Feedbackcited by 16
- 2020Regret analysis of the Piyavskii-Shubert algorithm for global Lipschitz optimizationcited by 11
- 2020A Nearest Neighbor Characterization of Lebesgue Points in Metric Measure Spacescited by 8
- 2021Instance-Dependent Bounds for Zeroth-order Lipschitz Optimization with Error Certificatescited by 7
- 2020An Efficient Algorithm for Cooperative Semi-Banditscited by 6
- 2020The sample complexity of level set approximationcited by 6
- 2021Cooperative Online Learning with Feedback Graphscited by 4
- 2019ROI Maximization in Stochastic Online Decision-Makingcited by 3
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