Summary of Wait-less Offline Tuning and Re-solving For Online Decision Making, by Jingruo Sun et al.
Wait-Less Offline Tuning and Re-solving for Online Decision Making
by Jingruo Sun, Wenzhi Gao, Ellen Vitercik, Yinyu Ye
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Optimization and Control (math.OC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A new online linear programming algorithm combines the strengths of low-regret LP-based methods and computationally efficient first-order OLP algorithms to achieve a balanced solution. The proposed approach re-solves LP subproblems periodically while using the latest dual prices to guide online decision-making, alongside a parallel running first-order method that smooths resource consumption. This “wait-less” algorithm achieves an improved regret guarantee of O(log(T/f) + sqrt(f)), making it suitable for large-scale applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to solve big optimization problems in real-time! The current best methods are too slow, but some faster ones aren’t as good at finding the best solution. This research combines the best parts of both approaches to make a new algorithm that’s fast and finds good solutions. It works by solving smaller optimization problems periodically and using what it learns to make better decisions in between. This helps balance speed and accuracy, making it useful for big applications. |
Keywords
* Artificial intelligence * Optimization
