# DASH: Dynamic Approach for Switching Heuristics

arXiv: https://arxiv.org/abs/1307.4689 · HTML: https://mlsanity.com/paper/1307.4689/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2013.

## Built on by (most cited first, 13 of 13)

- [A Survey for Solving Mixed Integer Programming via Machine Learning](https://mlsanity.com/paper/2203.02878/) (2022)
- [Sample Complexity of Tree Search Configuration: Cutting Planes and Beyond](https://mlsanity.com/paper/2106.04033/) (2021)
- [Structural Analysis of Branch-and-Cut and the Learnability of Gomory Mixed Integer Cuts](https://mlsanity.com/paper/2204.07312/) (2022)
- [Improved Sample Complexity Bounds for Branch-and-Cut](https://mlsanity.com/paper/2111.11207/) (2021)
- [Enhancing Column Generation by a Machine-Learning-Based Pricing Heuristic for Graph Coloring](https://mlsanity.com/paper/2112.04906/) (2021)
- [Artificial Intelligence for Operations Research: Revolutionizing the Operations Research Process](https://mlsanity.com/paper/2401.03244/) (2024)
- [Branch and Bound in Mixed Integer Linear Programming Problems: A Survey of Techniques and Trends](https://mlsanity.com/paper/2111.06257/) (2021)
- [Learning for Spatial Branching: An Algorithm Selection Approach](https://mlsanity.com/paper/2204.10834/) (2022)
- [Taking the human out of decomposition-based optimization via artificial intelligence: Part II. Learning to initialize](https://mlsanity.com/paper/2310.07082/) (2023)
- [Accelerating process control and optimization via machine learning: A review](https://mlsanity.com/paper/2412.18529/) (2024)
- [FORGE: Foundational Optimization Representations from Graph Embeddings](https://mlsanity.com/paper/2508.20330/) (2025)
- [ParBalans: Parallel Multi-Armed Bandits-based Adaptive Large Neighborhood Search](https://mlsanity.com/paper/2508.06736/) (2025)
- [Influence branching for learning to solve mixed-integer programs online](https://mlsanity.com/paper/2510.04273/) (2025)

## Abstract (arXiv)

Complete tree search is a highly effective method for tackling MIP problems, and over the years, a plethora of branching heuristics have been introduced to further refine the technique for varying problems. Recently, portfolio algorithms have taken the process a step further, trying to predict the best heuristic for each instance at hand. However, the motivation behind algorithm selection can be taken further still, and used to dynamically choose the most appropriate algorithm for each encountered subproblem. In this paper we identify a feature space that captures both the evolution of the problem in the branching tree and the similarity among subproblems of instances from the same MIP models. We show how to exploit these features to decide the best time to switch the branching heuristic and then show how such a system can be trained efficiently. Experiments on a highly heterogeneous collection of MIP instances show significant gains over the pure algorithm selection approach that for a given instance uses only a single heuristic throughout the search.