Resource-Adaptive Stochastic Gradient Descent for Online Linear Programming without Re-solving
Numerical experiments show that RASGD achieves regret competitive with per-arrival LP re-solving and improves upon the tested first-order baselines, while retaining the computational efficiency of first-order methods.
Jiameng Lyu
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