Near-Optimal Consistency-Robustness Trade-Offs for Learning-Augmented Online Knapsack Problems
- URL: http://arxiv.org/abs/2406.18752v2
- Date: Wed, 09 Jul 2025 14:20:53 GMT
- Title: Near-Optimal Consistency-Robustness Trade-Offs for Learning-Augmented Online Knapsack Problems
- Authors: Mohammadreza Daneshvaramoli, Helia Karisani, Adam Lechowicz, Bo Sun, Cameron Musco, Mohammad Hajiesmaili,
- Abstract summary: This paper introduces a family of learning-augmented algorithms for online knapsack problems.<n>We propose a novel fractional-to-integral conversion procedure, offering new insights for online algorithm design.
- Score: 16.793099279933163
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper introduces a family of learning-augmented algorithms for online knapsack problems that achieve near Pareto-optimal consistency-robustness trade-offs through a simple combination of trusted learning-augmented and worst-case algorithms. Our approach relies on succinct, practical predictions -- single values or intervals estimating the minimum value of any item in an offline solution. Additionally, we propose a novel fractional-to-integral conversion procedure, offering new insights for online algorithm design.
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