Solving a Class of Cut-Generating Linear Programs via Machine Learning
Author
Abstract
Suggested Citation
DOI: 10.1287/ijoc.2022.0241
Download full text from publisher
References listed on IDEAS
- Alejandro Marcos Alvarez & Quentin Louveaux & Louis Wehenkel, 2017. "A Machine Learning-Based Approximation of Strong Branching," INFORMS Journal on Computing, INFORMS, vol. 29(1), pages 185-195, February.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Bissan Ghaddar & Ignacio Gómez-Casares & Julio González-Díaz & Brais González-Rodríguez & Beatriz Pateiro-López & Sofía Rodríguez-Ballesteros, 2023. "Learning for Spatial Branching: An Algorithm Selection Approach," INFORMS Journal on Computing, INFORMS, vol. 35(5), pages 1024-1043, September.
- Dimitris Bertsimas & Cheol Woo Kim, 2023. "A Prescriptive Machine Learning Approach to Mixed-Integer Convex Optimization," INFORMS Journal on Computing, INFORMS, vol. 35(6), pages 1225-1241, November.
- Francisco Jara-Moroni & John E. Mitchell & Jong-Shi Pang & Andreas Wächter, 2020. "An enhanced logical benders approach for linear programs with complementarity constraints," Journal of Global Optimization, Springer, vol. 77(4), pages 687-714, August.
- Nikolaus Furian & Michael O’Sullivan & Cameron Walker & Eranda Çela, 2021. "A machine learning-based branch and price algorithm for a sampled vehicle routing problem," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(3), pages 693-732, September.
- Bengio, Yoshua & Lodi, Andrea & Prouvost, Antoine, 2021. "Machine learning for combinatorial optimization: A methodological tour d’horizon," European Journal of Operational Research, Elsevier, vol. 290(2), pages 405-421.
- Andrea Lodi & Giulia Zarpellon, 2017. "On learning and branching: a survey," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 207-236, July.
- Kandula, Shanthan & Krishnamoorthy, Srikumar & Roy, Debjit, 2021. "Learning to Play the Box-Sizing Game: A Machine Learning Approach for Solving the E-commerce Packaging Problem," IIMA Working Papers WP 2021-11-02, Indian Institute of Management Ahmedabad, Research and Publication Department.
- Yu Yang & Natashia Boland & Martin Savelsbergh, 2021. "Multivariable Branching: A 0-1 Knapsack Problem Case Study," INFORMS Journal on Computing, INFORMS, vol. 33(4), pages 1354-1367, October.
- Dimitris Bertsimas & Bartolomeo Stellato, 2022. "Online Mixed-Integer Optimization in Milliseconds," INFORMS Journal on Computing, INFORMS, vol. 34(4), pages 2229-2248, July.
- Xiangyi Zhang & Lu Chen & Michel Gendreau & André Langevin, 2022. "Learning-Based Branch-and-Price Algorithms for the Vehicle Routing Problem with Time Windows and Two-Dimensional Loading Constraints," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1419-1436, May.
- Sebastian Kraul & Markus Seizinger & Jens O. Brunner, 2023. "Machine Learning–Supported Prediction of Dual Variables for the Cutting Stock Problem with an Application in Stabilized Column Generation," INFORMS Journal on Computing, INFORMS, vol. 35(3), pages 692-709, May.
- Gerdus Benadè & John N. Hooker, 2020. "Optimization Bounds from the Branching Dual," INFORMS Journal on Computing, INFORMS, vol. 32(1), pages 3-15, January.
- Álinson S. Xavier & Feng Qiu & Shabbir Ahmed, 2021. "Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems," INFORMS Journal on Computing, INFORMS, vol. 33(2), pages 739-756, May.
- Yang, Yu & Boland, Natashia & Dilkina, Bistra & Savelsbergh, Martin, 2022. "Learning generalized strong branching for set covering, set packing, and 0–1 knapsack problems," European Journal of Operational Research, Elsevier, vol. 301(3), pages 828-840.
- Gregor Hendel & Daniel Anderson & Pierre Le Bodic & Marc E. Pfetsch, 2022. "Estimating the Size of Branch-and-Bound Trees," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 934-952, March.
- Sidhant Misra & Line Roald & Yeesian Ng, 2022. "Learning for Constrained Optimization: Identifying Optimal Active Constraint Sets," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 463-480, January.
More about this item
Keywords
cutting planes; cut-generating linear programs; machine learning; data classification; function approximation;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orijoc:v:36:y:2024:i:3:p:708-722. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.