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 2281 articles , 1 standard article )

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  1. Ahmadi, Amir Ali; Majumdar, Anirudha: DSOS and SDSOS optimization: more tractable alternatives to sum of squares and semidefinite optimization (2019)
  2. Andersson, Joel A. E.; Gillis, Joris; Horn, Greg; Rawlings, James B.; Diehl, Moritz: CasADi: a software framework for nonlinear optimization and optimal control (2019)
  3. Becker, Henrique; Buriol, Luciana S.: An empirical analysis of exact algorithms for the unbounded knapsack problem (2019)
  4. Bonami, Pierre; Lodi, Andrea; Schweiger, Jonas; Tramontani, Andrea: Solving quadratic programming by cutting planes (2019)
  5. Brech, Claus-Henning; Ernst, Andreas; Kolisch, Rainer: Scheduling medical residents’ training at university hospitals (2019)
  6. Briskorn, Dirk; Dienstknecht, Michael: Mixed-integer programming models for tower crane selection and positioning with respect to mutual interference (2019)
  7. Castro, Jordi; González, José A.: A linear optimization-based method for data privacy in statistical tabular data (2019)
  8. Ceselli, Alberto; Fiore, Marco; Premoli, Marco; Secci, Stefano: Optimized assignment patterns in mobile edge cloud networks (2019)
  9. Chassein, André; Dokka, Trivikram; Goerigk, Marc: Algorithms and uncertainty sets for data-driven robust shortest path problems (2019)
  10. Clautiaux, François; Guillot, Jérémy; Pesneau, Pierre: Exact approaches for solving a covering problem with capacitated subtrees (2019)
  11. Della Croce, Federico; Pferschy, Ulrich; Scatamacchia, Rosario: New exact approaches and approximation results for the penalized knapsack problem (2019)
  12. de Matta, Renato: Product costing in the strategic formation of a supply chain (2019)
  13. Ecker, Grit; Yuan, Di; Koster, Arie M. C. A.; Schmeink, Anke: Accurate optimization models for interference constrained bandwidth allocation in cellular networks (2019)
  14. Elloumi, Sourour; Lambert, Amélie: Global solution of non-convex quadratically constrained quadratic programs (2019)
  15. Fanjul-Peyro, Luis; Ruiz, Rubén; Perea, Federico: Reformulations and an exact algorithm for unrelated parallel machine scheduling problems with setup times (2019)
  16. Gambella, Claudio; Maggioni, Francesca; Vigo, Daniele: A stochastic programming model for a tactical solid waste management problem (2019)
  17. 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)
  18. Grimm, Veronika; Kleinert, Thomas; Liers, Frauke; Schmidt, Martin; Zöttl, Gregor: Optimal price zones of electricity markets: a mixed-integer multilevel model and global solution approaches (2019)
  19. Györgyi, Péter; Kis, Tamás: A probabilistic approach to pickup and delivery problems with time window uncertainty (2019)
  20. Hamdan, Bayan; Diabat, Ali: A two-stage multi-echelon stochastic blood supply chain problem (2019)

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