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Using Predictions in Online Optimization
2016
Proceedings of the 2016 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Science - SIGMETRICS '16
We consider online convex optimization (OCO) problems with switching costs and noisy predictions. While the design of online algorithms for OCO problems has received considerable attention, the design of algorithms in the context of noisy predictions is largely open. To this point, two promising algorithms have been proposed: Receding Horizon Control (RHC) and Averaging Fixed Horizon Control (AFHC). The comparison of these policies is largely open. AFHC has been shown to provide better
doi:10.1145/2896377.2901464
dblp:conf/sigmetrics/ChenCLGW16
fatcat:x6gsq7ny4zdvlgivzo2bd26gyy