期刊
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS
卷 5, 期 4, 页码 1941-1951出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCNS.2017.2774043
关键词
First-order method; network resource allocation; statistical learning; stochastic approximation
资金
- NSF [1509040, 1508993, 1509005]
- NSF China [61573331]
- NSF Anhui [1608085QF130, CAS-XDA06040602]
- Directorate For Engineering
- Div Of Electrical, Commun & Cyber Sys [1509005] Funding Source: National Science Foundation
Network resource allocation shows revived popularity in the era of data deluge and information explosion. Existing stochastic optimization approaches fall short in attaining a desirable cost-delay tradeoff. Recognizing the central role of Lagrange multipliers in a network resource allocation, a novel learn-and-adapt stochastic dual gradient (LA-SDG) method is developed in this paper to learn the sample-optimal Lagrange multiplier from historical data, and accordingly adapt the upcoming resource allocation strategy. Remarkably, an LA-SDG method only requires just an extra sample (gradient) evaluation relative to the celebrated stochastic dual gradient method. LA-SDG can be interpreted as a foresighted learning scheme with an eye on the future, or, a modified heavy-ball iteration from an optimization viewpoint. It has been established-both theoretically and empirically-that LA-SDG markedly improves the cost-delay tradeoff over state-of-the-art allocation schemes.
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