Learning MILP Resolution Outcomes Before Reaching Time-Limit-Reference-Cited by-同舟云学术

Learning MILP Resolution Outcomes Before Reaching Time-Limit

Published:2019 Issue: Volume: Page:275-291
ISSN:0302-9743
Container-title:Integration of Constraint Programming, Artificial Intelligence, and Operations Research
language:
Short-container-title:

Author:

Fischetti Martina,Lodi Andrea,Zarpellon Giulia

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-19212-9_18

Reference33 articles.

1. Achterberg, T., Berthold, T., Hendel, G.: Rounding and propagation heuristics for mixed integer programming. In: Klatte, D., Lüthi, H.J., Schmedders, K. (eds.) Operations Research Proceedings 2011, pp. 71–76. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29210-1_12

2. Achterberg, T., Wunderling, R.: Mixed integer programming: analyzing 12 years of progress. In: Jünger, M., Reinelt, G. (eds.) Facets of Combinatorial Optimization, pp. 449–481. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38189-8_18

3. Belov, G., Esler, S., Fernando, D., Bodic, P.L., Nemhauser, G.L.: Estimating the size of search trees by sampling with domain knowledge. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, pp. 473–479 (2017). https://doi.org/10.24963/ijcai.2017/67

4. Bengio, Y., Lodi, A., Prouvost, A.: Machine learning for combinatorial optimization: a methodological tour d’horizon (2018). Preprint: arXiv:1811.06128

5. Berthold, T., Hendel, G., Koch, T.: From feasibility to improvement to proof: three phases of solving mixed-integer programs. Optim. Methods Softw. 33(3), 499–517 (2017). https://doi.org/10.1080/10556788.2017.1392519

Cited by 10 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Machine learning augmented branch and bound for mixed integer linear programming;Mathematical Programming;2024-08-22

2. A Mathematical Programming Approach to Sparse Canonical Correlation Analysis;Expert Systems with Applications;2024-03

3. Machine Learning for Combinatorial Optimization;Encyclopedia of Optimization;2022-10-30

4. Estimating the Size of Branch-and-Bound Trees;INFORMS Journal on Computing;2022-03

5. Deep learning‐based SCUC decision‐making: An intelligent data‐driven approach with self‐learning capabilities;IET Generation, Transmission & Distribution;2021-10-16