Summary of Online Linear Programming with Batching, by Haoran Xu et al.
Online Linear Programming with Batching
by Haoran Xu, Peter W. Glynn, Yinyu Ye
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This paper investigates Online Linear Programming (OLP) with batching, where the planning horizon is divided into K batches, allowing decisions to be delayed until the end of each batch. The study focuses on two research questions: (1) finding a lower bound of regret as a function of K and (2) determining algorithms that can achieve this regret lower bound. The paper explores these questions in settings where the conditional distribution of reward given resource consumption is continuous, showing that the answers differ from those in previous studies with finite-support distributions. The authors propose algorithms with O(log K) regret upper bounds for single-resource and multi-resource settings, as well as a Poisson arrival process. These bounds are independent of planning horizon length, and all proposed algorithms delay decisions on customers arriving in only the first and last batch. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make better decisions when you have many things to choose from. It’s like trying to decide what to buy at a store with lots of items. The authors want to know how to make good choices when you can’t look at everything at once, but instead have to make decisions in small groups. They found that if you delay making decisions until the end of each group, it helps you make better choices overall. They also came up with new ways to make these kinds of decisions and showed that they work well. |
