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The article introduces dp_knapsack_sliding_hirschberg, a new activation memory budget solver for PyTorch that reduces peak RAM usage during memory planning by up to 20x compared to the default dp_knapsack solver, explaining how 0/1 knapsack dynamic programming is used to choose which operations to save or recompute.
The paper introduces BFTR, an algorithmic framework for telecom tariff recommendation that prioritizes customer budgets and prevents overcharging. Experiments on a Nigerian MTN-inspired dataset demonstrate the framework's effectiveness with zero surcharge and high utility.