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Resource-Adaptive Stochastic Gradient Descent for Online Linear Programming without Re-solving
The growth of large language model (LLM) inference and search services increases the scale of online linear programming problems, motivating computationally efficient algorithms. We develop resource-adaptive stochastic gradient descent (RASGD) for stochastic online linear programming. The algorithm
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