# Exploiting variable associations to configure efficient local search algorithms in large-scale binary integer programs

arXiv: https://arxiv.org/abs/1604.08448 · HTML: https://mlsanity.com/paper/1604.08448/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2016.

## Built on by (most cited first, 2 of 2)

- [Data-driven Mixed Integer Optimization through Probabilistic Multi-variable Branching](https://mlsanity.com/paper/2305.12352/) (2023)
- [New Characterizations and Efficient Local Search for General Integer Linear Programming](https://mlsanity.com/paper/2305.00188/) (2023)

## Abstract (arXiv)

We present a data mining approach for reducing the search space of local search algorithms in a class of binary integer programs including the set covering and partitioning problems. The quality of locally optimal solutions typically improves if a larger neighborhood is used, while the computation time of searching the neighborhood increases exponentially. To overcome this, we extract variable associations from the instance to be solved in order to identify promising pairs of flipping variables in the neighborhood search. Based on this, we develop a 4-flip neighborhood local search algorithm that incorporates an efficient incremental evaluation of solutions and an adaptive control of penalty weights. Computational results show that the proposed method improves the performance of the local search algorithm for large-scale set covering and partitioning problems.