Online Learning for IoT Optimization: A Frank–Wolfe Adam-Based Algorithm
| Autor: | Wei Quan, Ruijuan Zheng, Yangfan Zhou, Qingtao Wu, Junlong Zhu, Mingchuan Zhang |
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| Rok vydání: | 2020 |
| Předmět: |
Computer Networks and Communications
Computer science business.industry 020208 electrical & electronic engineering Regular polygon Approximation algorithm 020206 networking & telecommunications Regret Time horizon 02 engineering and technology Computer Science Applications Projection (relational algebra) Online optimization Hardware and Architecture Signal Processing 0202 electrical engineering electronic engineering information engineering Convex function Internet of Things business Algorithm Information Systems |
| Zdroj: | IEEE Internet of Things Journal. 7:8228-8237 |
| ISSN: | 2372-2541 |
| Popis: | Many problems in the Internet of Things (IoT) can be regarded as online optimization problems. For this reason, an online-constrained problem in IoT is considered in this article, where the cost functions change over time. To solve this problem, many projected online optimization algorithms have been widely used. However, the projections of these algorithms become prohibitive in problems involving high-dimensional parameters and massive data. To address this issue, we propose a Frank–Wolfe Adam online learning algorithm called Frank–Wolfe Adam (FWAdam), which uses a Frank–Wolfe method to eschew costly projection operations. Furthermore, we first give the convergence analysis of the FWAdam algorithm, and prove its regret bound to $O(T^{3/4})$ when cost functions are convex, where $T$ is a time horizon. Finally, we present simulated experiments on two data sets to validate our theoretical results. |
| Databáze: | OpenAIRE |
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