---
title: "Machine Learning for Integer Programming"
authors:
  - "Elias B. Khalil"
doi: ""
url: "https://ijcai.org/proceedings/2016/576"
pdf: "https://ijcai.org/proceedings/2016/0576.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "4004-4004"
track: ""
keywords:
---

# Machine Learning for Integer Programming

Elias B. Khalil

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 4004-4004.

## Abstract

Mixed Integer Programs (MIP) are solved exactly by tree-based branch-and-bound search. However, various components of the algorithm involve making decisions that are currently addressed heuristically. Instead, I propose to use machine learning (ML) approaches such as supervised ranking and multi-armed bandits to make better-informed, input-specific decisions during MIP branch-and-bound. My thesis aims at improving the overall performance of MIP solvers. To illustrate the potential for ML in MIP, I have so far tackled branching variable selection, a crucial component of the search procedure, showing that ML approaches for variable selection can outperform traditional heuristics.