Machine Learning for Integer Programming

Khalil, Elias B.

Machine Learning for Integer Programming

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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.

Elias B. Khalil. 2016. Machine Learning for Integer Programming. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16. IJCAI, 4004–4004. https://ijcai.org/proceedings/2016/576

@inproceedings{ijcai2016p576,
  title     = {Machine Learning for Integer Programming},
  author    = {Khalil, Elias B.},
  booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
               Artificial Intelligence, {IJCAI-16}},
  publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
  editor    = {Subbarao Kambhampati},
  pages     = {4004--4004},
  year      = {2016},
  month     = {7},
  url       = {https://ijcai.org/proceedings/2016/576},
}

TY  - CPAPER
TI  - Machine Learning for Integer Programming
AU  - Khalil, Elias B.
T2  - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16
PB  - AAAI Press / International Joint Conferences on Artificial Intelligence
PY  - 2016
SP  - 4004
EP  - 4004
UR  - https://ijcai.org/proceedings/2016/576
ER  -