DASH: Dynamic Approach for Switching Heuristics
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DOI: 10.1016/j.ejor.2015.08.018
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- L. G. Mitten, 1970. "Branch-and-Bound Methods: General Formulation and Properties," Operations Research, INFORMS, vol. 18(1), pages 24-34, February.
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- 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.
- 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.
- 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.
- 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.
- 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.
- Robin Kemminer & Jannick Lange & Jens Peter Kempkes & Kevin Tierney & Dimitri Weiß, 2024. "Configuring Mixed-Integer Programming Solvers for Large-Scale Instances," SN Operations Research Forum, Springer, vol. 5(2), pages 1-14, June.
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Keywords
Mixed-Integer Programming; Algorithm selection; Dynamic search heuristics;All these keywords.
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