Decision Diagrams for Discrete Optimization: A Survey of Recent Advances
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Abstract (arXiv)
In the last decade, decision diagrams (DDs) have been the basis for a large array of novel approaches for modeling and solving optimization problems. Many techniques now use DDs as a key tool to achieve state-of-the-art performance within other optimization paradigms, such as integer programming and constraint programming. This paper provides a survey of the use of DDs in discrete optimization, particularly focusing on recent developments. We classify these works into two groups based on the type of diagram (i.e., exact or approximate) and present a thorough description of their use. We discuss the main advantages of DDs, point out major challenges, and provide directions for future work.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Jan 2022.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 1.
3 papers.
- 2023R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intentscited by 10
- 2022Decision Diagram-Based Branch-and-Bound with Caching for Dominance and Suboptimality Detectioncited by 1
- 2025A Clustering-Based Variable Ordering Framework for Relaxed Decision Diagrams for Maximum Weighted Independent Set Problemcited by 0
- 2025A Clustering-Based Variable Ordering Framework for Relaxed Decision Diagrams for Maximum Weighted Independent Set Problem17 Dec 2025
- 2023R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents1 Mar 2023
- 2022Decision Diagram-Based Branch-and-Bound with Caching for Dominance and Suboptimality Detection22 Nov 2022