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 501 articles )

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  1. Ahmadi, Amir Ali; Hall, Georgina: On the complexity of detecting convexity over a box (2020)
  2. 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)
  3. Arana-Jiménez, Manuel; Blanco, Víctor; Fernández, Elena: On the fuzzy maximal covering location problem (2020)
  4. 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)
  5. Aslan, Ayse; Bakir, Ilke; Vis, Iris F. A.: A dynamic Thompson sampling hyper-heuristic framework for learning activity planning in personalized learning (2020)
  6. Bärmann, Andreas; Gemander, Patrick; Merkert, Maximilian: The clique problem with multiple-choice constraints under a cycle-free dependency graph (2020)
  7. Bayless, Sam; Kodirov, Nodir; Iqbal, Syed M.; Beschastnikh, Ivan; Hoos, Holger H.; Hu, Alan J.: Scalable constraint-based virtual data center allocation (2020)
  8. Ben Hermans, Andreas Themelis, Panagiotis Patrinos: QPALM: A Proximal Augmented Lagrangian Method for Nonconvex Quadratic Programs (2020) arXiv
  9. Bereg, Sergey; Mojica, Luis Gerardo; Morales, Linda; Sudborough, Hal: Constructing permutation arrays using partition and extension (2020)
  10. Beresnev, Vladimir; Melnikov, Andrey: (\varepsilon)-Constraint method for bi-objective competitive facility location problem with uncertain demand scenario (2020)
  11. Bertsimas, Dimitris; Dunning, Iain: Relative robust and adaptive optimization (2020)
  12. Bertsimas, Dimitris; Lamperski, Jourdain; Pauphilet, Jean: Certifiably optimal sparse inverse covariance estimation (2020)
  13. Bhuiyan, Tanveer Hossain; Medal, Hugh R.; Harun, Sarah: A stochastic programming model with endogenous and exogenous uncertainty for reliable network design under random disruption (2020)
  14. Borgwardt, Steffen; Viss, Charles: An implementation of steepest-descent augmentation for linear programs (2020)
  15. Brantley, Halley L.; Guinness, Joseph; Chi, Eric C.: Baseline drift estimation for air quality data using quantile trend filtering (2020)
  16. Burlacu, Robert; Geißler, Björn; Schewe, Lars: Solving mixed-integer nonlinear programmes using adaptively refined mixed-integer linear programmes (2020)
  17. Çavdaroğlu, Burak; Atan, Tankut: Determining matchdays in sports league schedules to minimize rest differences (2020)
  18. Cea, Sebastián; Durán, Guillermo; Guajardo, Mario; Sauré, Denis; Siebert, Joaquín; Zamorano, Gonzalo: An analytics approach to the FIFA ranking procedure and the world cup final draw (2020)
  19. Chen, Qian Matteo; Finzi, Alberto; Mancini, Toni; Melatti, Igor; Tronci, Enrico: MILP, pseudo-Boolean, and OMT solvers for optimal fault-tolerant placements of relay nodes in mission critical wireless networks (2020)
  20. Christian D. Hubbs, Hector D. Perez, Owais Sarwar, Nikolaos V. Sahinidis, Ignacio E. Grossmann, John M. Wassick: OR-Gym: A Reinforcement Learning Library for Operations Research Problem (2020) arXiv

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