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 2370 articles , 1 standard article )

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  1. Forghani, Asefe; Dehghanian, Farzad; Salari, Majid; Ghiami, Yousef: A bi-level model and solution methods for partial interdiction problem on capacitated hierarchical facilities (2020)
  2. Morshed, Muhammad Sarowar; Noor-E-Alam, Muhammad: Generalized affine scaling algorithms for linear programming problems (2020)
  3. Ahmadi, Amir Ali; Majumdar, Anirudha: DSOS and SDSOS optimization: more tractable alternatives to sum of squares and semidefinite optimization (2019)
  4. Andersson, Joel A. E.; Gillis, Joris; Horn, Greg; Rawlings, James B.; Diehl, Moritz: CasADi: a software framework for nonlinear optimization and optimal control (2019)
  5. Andrade, Tiago; Oliveira, Fabricio; Hamacher, Silvio; Eberhard, Andrew: Enhancing the normalized multiparametric disaggregation technique for mixed-integer quadratic programming (2019)
  6. Becker, Henrique; Buriol, Luciana S.: An empirical analysis of exact algorithms for the unbounded knapsack problem (2019)
  7. Bernardi, Simona; Mahulea, Cristian; Albareda, Jorge: Toward a decision support system for the clinical pathways assessment (2019)
  8. Boland, Natashia; Christiansen, Jeffrey; Dandurand, Brian; Eberhard, Andrew; Oliveira, Fabricio: A parallelizable augmented Lagrangian method applied to large-scale non-convex-constrained optimization problems (2019)
  9. Bonami, Pierre; Lodi, Andrea; Schweiger, Jonas; Tramontani, Andrea: Solving quadratic programming by cutting planes (2019)
  10. Brech, Claus-Henning; Ernst, Andreas; Kolisch, Rainer: Scheduling medical residents’ training at university hospitals (2019)
  11. Briskorn, Dirk; Dienstknecht, Michael: Mixed-integer programming models for tower crane selection and positioning with respect to mutual interference (2019)
  12. Brunner, Florian David; Heemels, W. P. M. H.; Allgöwer, Frank: Event-triggered and self-triggered control for linear systems based on reachable sets (2019)
  13. Buchheim, Christoph; Pruente, Jonas: (K)-adaptability in stochastic combinatorial optimization under objective uncertainty (2019)
  14. Camara, Marcus Vinicius Oliveira; Ribeiro, Glaydston Mattos: The support unit location problem to road traffic surveys with multi-stages (2019)
  15. Carè, Algo; Garatti, Simone; Campi, Marco C.: The wait-and-judge scenario approach applied to antenna array design (2019)
  16. Casazza, Marco: New formulations for variable cost and size bin packing problems with item fragmentation (2019)
  17. Castro, Jordi; González, José A.: A linear optimization-based method for data privacy in statistical tabular data (2019)
  18. Cay, Pelin; Mancilla, Camilo; Storer, Robert H.; Zuluaga, Luis F.: Operational decisions for multi-period industrial gas pipeline networks under uncertainty (2019)
  19. Ceselli, Alberto; Fiore, Marco; Premoli, Marco; Secci, Stefano: Optimized assignment patterns in mobile edge cloud networks (2019)
  20. Chassein, André; Dokka, Trivikram; Goerigk, Marc: Algorithms and uncertainty sets for data-driven robust shortest path problems (2019)

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