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Optimization problems for machine learning: A survey

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Cited by:

  1. 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.
  2. Andreas Dellnitz & Andreas Kleine & Madjid Tavana, 2024. "An integrated data envelopment analysis and regression tree method for new product price estimation," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 46(4), pages 1189-1211, December.
  3. Navarro-García, Manuel & Guerrero, Vanesa & Durban, María, 2023. "On constrained smoothing and out-of-range prediction using P-splines: A conic optimization approach," Applied Mathematics and Computation, Elsevier, vol. 441(C).
  4. de Oliveira, Raphael Mazzine Barbosa & Sörensen, Kenneth & Martens, David, 2024. "A model-agnostic and data-independent tabu search algorithm to generate counterfactuals for tabular, image, and text data," European Journal of Operational Research, Elsevier, vol. 317(2), pages 286-302.
  5. Philippe Jardin, 2023. "Designing topological data to forecast bankruptcy using convolutional neural networks," Annals of Operations Research, Springer, vol. 325(2), pages 1291-1332, June.
  6. Yen, Benjamin P.-C. & Luo, Yu, 2023. "Navigational guidance – A deep learning approach," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1179-1191.
  7. Maggioni, Francesca & Spinelli, Andrea, 2025. "A novel robust optimization model for nonlinear Support Vector Machine," European Journal of Operational Research, Elsevier, vol. 322(1), pages 237-253.
  8. Ítalo Santana & Breno Serrano & Maximilian Schiffer & Thibaut Vidal, 2025. "Support vector machines with the hard-margin loss: optimal training via combinatorial Benders’ cuts," Journal of Global Optimization, Springer, vol. 92(1), pages 205-225, May.
  9. Santini, Alberto & Malaguti, Enrico, 2024. "The min-Knapsack problem with compactness constraints and applications in statistics," European Journal of Operational Research, Elsevier, vol. 312(1), pages 385-397.
  10. Sobrie, Léon & Verschelde, Marijn & Roets, Bart, 2024. "Explainable real-time predictive analytics on employee workload in digital railway control rooms," European Journal of Operational Research, Elsevier, vol. 317(2), pages 437-448.
  11. Doumpos, Michalis & Zopounidis, Constantin & Gounopoulos, Dimitrios & Platanakis, Emmanouil & Zhang, Wenke, 2023. "Operational research and artificial intelligence methods in banking," European Journal of Operational Research, Elsevier, vol. 306(1), pages 1-16.
  12. Dimitris Bertsimas & Georgios Margaritis, 2025. "Global optimization: a machine learning approach," Journal of Global Optimization, Springer, vol. 91(1), pages 1-37, January.
  13. Bodo Rosenhahn, 2023. "Optimization of Sparsity-Constrained Neural Networks as a Mixed Integer Linear Program," Journal of Optimization Theory and Applications, Springer, vol. 199(3), pages 931-954, December.
  14. Fang, Chao & Han, Zonglei & Wang, Wei & Zio, Enrico, 2023. "Routing UAVs in landslides Monitoring: A neural network heuristic for team orienteering with mandatory visits," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
  15. Astorino, Annabella & Avolio, Matteo & Fuduli, Antonio, 2022. "A maximum-margin multisphere approach for binary Multiple Instance Learning," European Journal of Operational Research, Elsevier, vol. 299(2), pages 642-652.
  16. Wang, Mingsheng & Huang, Yong, 2024. "A digital Technology–Cultural resource strategy to drive innovation in cultural industries: A dynamic analysis based on machine learning," Technology in Society, Elsevier, vol. 77(C).
  17. Benati, Stefano & Ponce, Diego & Puerto, Justo & Rodríguez-Chía, Antonio M., 2022. "A branch-and-price procedure for clustering data that are graph connected," European Journal of Operational Research, Elsevier, vol. 297(3), pages 817-830.
  18. Ruomiao Yang & Tianfang Xie & Zhentao Liu, 2022. "The Application of Machine Learning Methods to Predict the Power Output of Internal Combustion Engines," Energies, MDPI, vol. 15(9), pages 1-16, April.
  19. Carolina Saavedra Sueldo & Ivo Perez Colo & Mariano Paula & Sebastián A. Villar & Gerardo G. Acosta, 2025. "Simulation-based metaheuristic optimization algorithm for material handling," Journal of Intelligent Manufacturing, Springer, vol. 36(3), pages 1689-1709, March.
  20. Phuong-Nam Nguyen, 2025. "Quantum technology: a financial risk assessment," Digital Finance, Springer, vol. 7(2), pages 133-172, June.
  21. Miguel Angel Ortíz-Barrios & Dayana Milena Coba-Blanco & Juan-José Alfaro-Saíz & Daniela Stand-González, 2021. "Process Improvement Approaches for Increasing the Response of Emergency Departments against the COVID-19 Pandemic: A Systematic Review," IJERPH, MDPI, vol. 18(16), pages 1-31, August.
  22. Fu, Kun & Chen, Meiqian & Li, Qinghai, 2024. "Decontamination performance of metallic radionuclides in irradiated graphite via a fluidized bed reactor," Energy, Elsevier, vol. 305(C).
  23. Akhtar, Pervaiz & Ghouri, Arsalan Mujahid & Ashraf, Aniqa & Lim, Jia Jia & Khan, Naveed R & Ma, Shuang, 2024. "Smart product platforming powered by AI and generative AI: Personalization for the circular economy," International Journal of Production Economics, Elsevier, vol. 273(C).
  24. Corrado Coppola & Lorenzo Papa & Marco Boresta & Irene Amerini & Laura Palagi, 2024. "Tuning parameters of deep neural network training algorithms pays off: a computational study," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(3), pages 579-620, October.
  25. Emilio Carrizosa & Vanesa Guerrero & Dolores Romero Morales, 2023. "On mathematical optimization for clustering categories in contingency tables," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(2), pages 407-429, June.
  26. Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).
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