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Improved Compression Bounds for Scenario Decision Making

arXiv 2501.08884 ↗First posted 15 Jan 2025math.OC cs.LGcited by 0

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Abstract (arXiv)

Scenario decision making offers a flexible way of making decision in an uncertain environment while obtaining probabilistic guarantees on the risk of failure of the decision. The idea of this approach is to draw samples of the uncertainty and make a decision based on the samples, called "scenarios". The probabilistic guarantees take the form of a bound on the probability of sampling a set of scenarios that will lead to a decision whose risk of failure is above a given maximum tolerance. This bound can be expressed as a function of the number of sampled scenarios, the maximum tolerated risk, and some intrinsic property of the problem called the "compression size". Several such bounds have been proposed in the literature under various assumptions on the problem. We propose new bounds that improve upon the existing ones without requiring stronger assumptions on the problem.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 3 of its 20 references that are in the map. First posted on arXiv 15 Jan 2025.

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References · 3

  1. 2023Compression, Generalization and Learningcited by 21
  2. 2020On the exact feasibility of convex scenario programs with discarded constraintscited by 15
  3. 2013Performance Bounds for the Scenario Approach and an Extension to a Class of Non-convex Programscited by 9

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