# The MIP Workshop 2023 Computational Competition on Reoptimization

arXiv: https://arxiv.org/abs/2311.14834 · HTML: https://mlsanity.com/paper/2311.14834/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2023.

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

- [Machine Learning Augmented Branch and Bound for Mixed Integer Linear Programming](https://mlsanity.com/paper/2402.05501/) (2024)
- [Influence branching for learning to solve mixed-integer programs online](https://mlsanity.com/paper/2510.04273/) (2025)
- [Teaching LLMs to Generate Challenging MILP Instances via Solver Feedback](https://mlsanity.com/paper/2609.37356/) (2026)

## Abstract (arXiv)

This paper describes the computational challenge developed for a computational competition held in 2023 for the 20^textrmth anniversary of the Mixed Integer Programming Workshop. The topic of this competition was reoptimization, also known as warm starting, of mixed integer linear optimization problems after slight changes to the input data for a common formulation. The challenge was to accelerate the proof of optimality of the modified instances by leveraging the information from the solving processes of previously solved instances, all while creating high-quality primal solutions. Specifically, we discuss the competition's format, the creation of public and hidden datasets, and the evaluation criteria. Our goal is to establish a methodology for the generation of benchmark instances and an evaluation framework, along with benchmark datasets, to foster future research on reoptimization of mixed integer linear optimization problems.