Abstract
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.
Original language | English (US) |
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Article number | 8110688 |
Pages (from-to) | 1941-1951 |
Number of pages | 11 |
Journal | IEEE Transactions on Control of Network Systems |
Volume | 5 |
Issue number | 4 |
DOIs | |
State | Published - Dec 2018 |
Bibliographical note
Publisher Copyright:© 2017 IEEE.
Keywords
- First-order method
- network resource allocation
- statistical learning
- stochastic approximation