# Decision Diagrams for Discrete Optimization: A Survey of Recent Advances

arXiv: https://arxiv.org/abs/2201.11536 · HTML: https://mlsanity.com/paper/2201.11536/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2022.

## Built on by (most cited first, 3 of 3)

- [R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents](https://mlsanity.com/paper/2303.00732/) (2023)
- [Decision Diagram-Based Branch-and-Bound with Caching for Dominance and Suboptimality Detection](https://mlsanity.com/paper/2211.13118/) (2022)
- [A Clustering-Based Variable Ordering Framework for Relaxed Decision Diagrams for Maximum Weighted Independent Set Problem](https://mlsanity.com/paper/2512.15198/) (2025)

## 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.