IBM® ILOG® CPLEX® offers C, C++, Java, .NET, and Python libraries that solve linear programming (LP) and related problems. Specifically, it solves linearly or quadratically constrained optimization problems where the objective to be optimized can be expressed as a linear function or a convex quadratic function. The variables in the model may be declared as continuous or further constrained to take only integer values.

References in zbMATH (referenced in 2221 articles , 1 standard article )

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  1. Brech, Claus-Henning; Ernst, Andreas; Kolisch, Rainer: Scheduling medical residents’ training at university hospitals (2019)
  2. Briskorn, Dirk; Dienstknecht, Michael: Mixed-integer programming models for tower crane selection and positioning with respect to mutual interference (2019)
  3. Castro, Jordi; González, José A.: A linear optimization-based method for data privacy in statistical tabular data (2019)
  4. Chassein, André; Dokka, Trivikram; Goerigk, Marc: Algorithms and uncertainty sets for data-driven robust shortest path problems (2019)
  5. Della Croce, Federico; Pferschy, Ulrich; Scatamacchia, Rosario: New exact approaches and approximation results for the penalized knapsack problem (2019)
  6. Ecker, Grit; Yuan, Di; Koster, Arie M. C. A.; Schmeink, Anke: Accurate optimization models for interference constrained bandwidth allocation in cellular networks (2019)
  7. Elloumi, Sourour; Lambert, Amélie: Global solution of non-convex quadratically constrained quadratic programs (2019)
  8. Fanjul-Peyro, Luis; Ruiz, Rubén; Perea, Federico: Reformulations and an exact algorithm for unrelated parallel machine scheduling problems with setup times (2019)
  9. Gambella, Claudio; Maggioni, Francesca; Vigo, Daniele: A stochastic programming model for a tactical solid waste management problem (2019)
  10. Ghasemi Saghand, Payman; Charkhgard, Hadi; Kwon, Changhyun: A branch-and-bound algorithm for a class of mixed integer linear maximum multiplicative programs: a bi-objective optimization approach (2019)
  11. Györgyi, Péter; Kis, Tamás: A probabilistic approach to pickup and delivery problems with time window uncertainty (2019)
  12. Hamdan, Bayan; Diabat, Ali: A two-stage multi-echelon stochastic blood supply chain problem (2019)
  13. Kress, Dominik; Dornseifer, Jan; Jaehn, Florian: An exact solution approach for scheduling cooperative gantry cranes (2019)
  14. Manerba, Daniele; Perboli, Guido: New solution approaches for the capacitated supplier selection problem with total quantity discount and activation costs under demand uncertainty (2019)
  15. Napoletano, Antonio; Martínez-Gavara, Anna; Festa, Paola; Pastore, Tommaso; Martí, Rafael: Heuristics for the constrained incremental graph drawing problem (2019)
  16. Nowak, Maciek; Hewitt, Mike; Bachour, Hussam: Mileage bands in freight transportation (2019)
  17. Reihaneh, Mohammad; Ghoniem, Ahmed: A branch-and-price algorithm for a vehicle routing with demand allocation problem (2019)
  18. Rossi, Roberto; Tomasella, Maurizio; Martin-Barragan, Belen; Embley, Tim; Walsh, Christopher; Langston, Matthew: The dynamic bowser routing problem (2019)
  19. Slawski, Martin; Ben-David, Emanuel: Linear regression with sparsely permuted data (2019)
  20. Sun, Defeng; Tang, Lixin; Baldacci, Roberto: A Benders decomposition-based framework for solving quay crane scheduling problems (2019)

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