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 2583 articles , 1 standard article )

Showing results 1 to 20 of 2583.
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  1. Dandurand, Brian C.; Kim, Kibaek; Leyffer, Sven: A bilevel approach for identifying the worst contingencies for nonconvex alternating current power systems (2021)
  2. Dan, Teodora; Lodi, Andrea; Marcotte, Patrice: An exact algorithmic framework for a class of mixed-integer programs with equilibrium constraints (2021)
  3. Okuno, Takayuki; Ikebe, Yoshiko: A new approach for solving mixed integer DC programs using a continuous relaxation with no integrality gap and smoothing techniques (2021)
  4. Vitor, Fabio; Easton, Todd: Approximate and exact merging of knapsack constraints with cover inequalities (2021)
  5. Wolsey, Laurence A.: Integer programming (2021)
  6. Adasme, Pablo; Dehghan Firoozabadi, Ali: Degree-constrained (k)-minimum spanning tree problem (2020)
  7. Alfieri, Arianna; Matta, Andrea; Pastore, Erica: The time buffer approximated buffer allocation problem: a row-column generation approach (2020)
  8. Aparicio, Juan; Kapelko, Magdalena; Monge, Juan F.: A well-defined composite indicator: an application to corporate social responsibility (2020)
  9. Arbex Valle, Cristiano; Beasley, John E.: Order batching using an approximation for the distance travelled by pickers (2020)
  10. Atashi Khoei, Arsham; Süral, Haldun; Tural, Mustafa Kemal: Multi-facility Green Weber problem (2020)
  11. Basso, S.; Ceselli, Alberto; Tettamanzi, Andrea: Random sampling and machine learning to understand good decompositions (2020)
  12. Bautista-Valhondo, Joaquín; Alfaro-Pozo, Rocío: Mixed integer linear programming models for flow shop scheduling with a demand plan of job types (2020)
  13. Bayless, Sam; Kodirov, Nodir; Iqbal, Syed M.; Beschastnikh, Ivan; Hoos, Holger H.; Hu, Alan J.: Scalable constraint-based virtual data center allocation (2020)
  14. Bereg, Sergey; Mojica, Luis Gerardo; Morales, Linda; Sudborough, Hal: Constructing permutation arrays using partition and extension (2020)
  15. Berger, J.; Lo, N.; Barkaoui, M.: QUEST -- a new quadratic decision model for the multi-satellite scheduling problem (2020)
  16. Bertsimas, Dimitris; Lamperski, Jourdain; Pauphilet, Jean: Certifiably optimal sparse inverse covariance estimation (2020)
  17. Bertsimas, Dimitris; Pauphilet, Jean; van Parys, Bart: Sparse regression: scalable algorithms and empirical performance (2020)
  18. 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)
  19. Borenich, Andrea; Greistorfer, Peter; Reimann, Marc: Model-based production cost estimation to support bid processes: an automotive case study (2020)
  20. Borghini, Fabrizio; Méndez-Díaz, Isabel; Zabala, Paula: An exact algorithm for the edge coloring by total labeling problem (2020)

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