CPLEX

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 2432 articles , 1 standard article )

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  1. Alfieri, Arianna; Matta, Andrea; Pastore, Erica: The time buffer approximated buffer allocation problem: a row-column generation approach (2020)
  2. Basso, S.; Ceselli, Alberto; Tettamanzi, Andrea: Random sampling and machine learning to understand good decompositions (2020)
  3. Bautista-Valhondo, Joaquín; Alfaro-Pozo, Rocío: Mixed integer linear programming models for flow shop scheduling with a demand plan of job types (2020)
  4. Bayless, Sam; Kodirov, Nodir; Iqbal, Syed M.; Beschastnikh, Ivan; Hoos, Holger H.; Hu, Alan J.: Scalable constraint-based virtual data center allocation (2020)
  5. Bereg, Sergey; Mojica, Luis Gerardo; Morales, Linda; Sudborough, Hal: Constructing permutation arrays using partition and extension (2020)
  6. Berger, J.; Lo, N.; Barkaoui, M.: QUEST -- a new quadratic decision model for the multi-satellite scheduling problem (2020)
  7. Bettiol, Enrico; Létocart, Lucas; Rinaldi, Francesco; Traversi, Emiliano: A conjugate direction based simplicial decomposition framework for solving a specific class of dense convex quadratic programs (2020)
  8. De Santis, Marianna; Grani, Giorgio; Palagi, Laura: Branching with hyperplanes in the criterion space: the frontier partitioner algorithm for biobjective integer programming (2020)
  9. Egging-Bratseth, Ruud; Baltensperger, Tobias; Tomasgard, Asgeir: Solving oligopolistic equilibrium problems with convex optimization (2020)
  10. Firat, Murat; Crognier, Guillaume; Gabor, Adriana F.; Hurkens, C. A. J.; Zhang, Yingqian: Column generation based heuristic for learning classification trees (2020)
  11. Forghani, Asefe; Dehghanian, Farzad; Salari, Majid; Ghiami, Yousef: A bi-level model and solution methods for partial interdiction problem on capacitated hierarchical facilities (2020)
  12. Fügenschuh, Armin R.; Craparo, Emily M.; Karatas, Mumtaz; Buttrey, Samuel E.: Solving multistatic sonar location problems with mixed-integer programming (2020)
  13. Gleixner, Ambros; Maher, Stephen J.; Müller, Benjamin; Pedroso, João Pedro: Price-and-verify: a new algorithm for recursive circle packing using Dantzig-Wolfe decomposition (2020)
  14. Hamilton, William T.; Husted, Mark A.; Newman, Alexandra M.; Braun, Robert J.; Wagner, Michael J.: Dispatch optimization of concentrating solar power with utility-scale photovoltaics (2020)
  15. Hooshmand, F.; Amerehi, F.; MirHassani, S. A.: Logic-based Benders decomposition algorithm for contamination detection problem in water networks (2020)
  16. Li, Xiangyong; Aneja, Y. P.: A branch-and-Benders-cut approach for the fault tolerant regenerator location problem (2020)
  17. Li, Xudong; Sun, Defeng; Toh, Kim-Chuan: On the efficient computation of a generalized Jacobian of the projector over the Birkhoff polytope (2020)
  18. Morshed, Muhammad Sarowar; Noor-E-Alam, Muhammad: Generalized affine scaling algorithms for linear programming problems (2020)
  19. Poirion, Pierre-Louis; Toubaline, Sonia; D’Ambrosio, Claudia; Liberti, Leo: Algorithms and applications for a class of bilevel MILPs (2020)
  20. Rezakhah, Mojtaba; Moreno, Eduardo; Newman, Alexandra: Practical performance of an open pit mine scheduling model considering blending and stockpiling (2020)

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