Gurobi

GUROBI OPTIMIZER: State of the Art Mathematical Programming Solver. The Gurobi Optimizer is a state-of-the-art solver for mathematical programming. It includes the following solvers: linear programming solver (LP), quadratic programming solver (QP), quadratically constrained programming solver (QCP), mixed-integer linear programming solver (MILP), mixed-integer quadratic programming solver (MIQP), and mixed-integer quadratically constrained programming solver (MIQCP). The solvers in the Gurobi Optimizer were designed from the ground up to exploit modern architectures and multi-core processors, using the most advanced implementations of the latest algorithms. To help set you up for success, the Gurobi Optimizer goes beyond fast and reliable solution performance to provide a broad range of interfaces, access to industry-standard modeling languages, flexible licensing together with transparent pricing, and outstanding, easy to reach, support.


References in zbMATH (referenced in 533 articles )

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  1. Anderson, A.; González, A. H.; Ferramosca, A.; Hernandez-Vargas, E. A.: Discrete-time MPC for switched systems with applications to biomedical problems (2021)
  2. Antoine Prouvost, Justin Dumouchelle, Maxime Gasse, Didier Chételat, Andrea Lodi: Ecole: A Library for Learning Inside MILP Solvers (2021) arXiv
  3. Dandurand, Brian C.; Kim, Kibaek; Leyffer, Sven: A bilevel approach for identifying the worst contingencies for nonconvex alternating current power systems (2021)
  4. Grigore, Radu; Kiefer, Stefan: Selective monitoring (2021)
  5. Luo, Xusheng; Pajic, Miroslav; Zavlanos, Michael M.: An optimal graph-search method for secure state estimation (2021)
  6. Mashiyat Zaman, Kotaro Tanahashi, Shu Tanaka: PyQUBO: Python Library for Mapping Combinatorial Optimization Problems to QUBO Form (2021) arXiv
  7. Mattenet, Alex; Davidson, Ian; Nijssen, Siegfried; Schaus, Pierre: Generic constraint-based block modeling using constraint programming (2021)
  8. 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)
  9. Sundar, Kaarthik; Nagarajan, Harsha; Linderoth, Jeff; Wang, Site; Bent, Russell: Piecewise polyhedral formulations for a multilinear term (2021)
  10. Wolsey, Laurence A.: Integer programming (2021)
  11. Ye, Chen-Dong; Tian, Tian; Zeng, Fan-Yang: The MILP-aided conditional differential attack and its application to Trivium (2021)
  12. Achterberg, Tobias; Bixby, Robert E.; Gu, Zonghao; Rothberg, Edward; Weninger, Dieter: Presolve reductions in mixed integer programming (2020)
  13. Ahmadi, Amir Ali; Hall, Georgina: On the complexity of detecting convexity over a box (2020)
  14. Almeida Guimarães, Dilson; Salles da Cunha, Alexandre; Pereira, Dilson Lucas: Semidefinite programming lower bounds and branch-and-bound algorithms for the quadratic minimum spanning tree problem (2020)
  15. Arana-Jiménez, Manuel; Blanco, Víctor; Fernández, Elena: On the fuzzy maximal covering location problem (2020)
  16. Araujo, Janniele A. S.; Santos, Haroldo G.; Gendron, Bernard; Jena, Sanjay Dominik; Brito, Samuel S.; Souza, Danilo S.: Strong bounds for resource constrained project scheduling: preprocessing and cutting planes (2020)
  17. Aslan, Ayse; Bakir, Ilke; Vis, Iris F. A.: A dynamic Thompson sampling hyper-heuristic framework for learning activity planning in personalized learning (2020)
  18. Bärmann, Andreas; Gemander, Patrick; Merkert, Maximilian: The clique problem with multiple-choice constraints under a cycle-free dependency graph (2020)
  19. Bayless, Sam; Kodirov, Nodir; Iqbal, Syed M.; Beschastnikh, Ivan; Hoos, Holger H.; Hu, Alan J.: Scalable constraint-based virtual data center allocation (2020)
  20. Ben Hermans, Andreas Themelis, Panagiotis Patrinos: QPALM: A Proximal Augmented Lagrangian Method for Nonconvex Quadratic Programs (2020) arXiv

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