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 111 articles )

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  1. Ahmadi, Amir Ali; Majumdar, Anirudha: Some applications of polynomial optimization in operations research and real-time decision making (2016)
  2. Alabdulmohsin, Ibrahim; Cisse, Moustapha; Gao, Xin; Zhang, Xiangliang: Large margin classification with indefinite similarities (2016)
  3. Anjos, Miguel F.; Fischer, Anja; Hungerländer, Philipp: Solution approaches for the double-row equidistant facility layout problem (2016)
  4. Bamberg, John; Lee, Melissa; Swartz, Eric: A note on relative hemisystems of Hermitian generalised quadrangles (2016)
  5. Bertsimas, Dimitris; de Ruiter, Frans J.C.T.: Duality in two-stage adaptive linear optimization: faster computation and stronger bounds (2016)
  6. Bertsimas, Dimitris; Dunning, Iain: Multistage robust mixed-integer optimization with adaptive partitions (2016)
  7. Bertsimas, Dimitris; King, Angela: OR forum: An algorithmic approach to linear regression (2016)
  8. Bertsimas, Dimitris; King, Angela; Mazumder, Rahul: Best subset selection via a modern optimization lens (2016)
  9. Borndörfer, Ralf; Schenker, Sebastian; Skutella, Martin; Strunk, Timo: PolySCIP (2016)
  10. Buchheim, Christoph; De Santis, Marianna; Lucidi, Stefano; Rinaldi, Francesco; Trieu, Long: A feasible active set method with reoptimization for convex quadratic mixed-integer programming (2016)
  11. Burdakov, Oleg P.; Kanzow, Christian; Schwartz, Alexandra: Mathematical programs with cardinality constraints: reformulation by complementarity-type conditions and a regularization method (2016)
  12. Castillo-Salazar, J.Arturo; Landa-Silva, Dario; Qu, Rong: Workforce scheduling and routing problems: literature survey and computational study (2016)
  13. Craciunas, Silviu S.; Oliver, Ramon Serna: Combined task- and network-level scheduling for distributed time-triggered systems (2016)
  14. Eckstein, Jonathan; Eskandani, Deniz; Fan, Jingnan: Multilevel optimization modeling for risk-averse stochastic programming (2016)
  15. Fischetti, Matteo; Lodi, Andrea; Monaci, Michele; Salvagnin, Domenico; Tramontani, Andrea: Improving branch-and-cut performance by random sampling (2016)
  16. Friberg, Henrik A.: CBLIB 2014: a benchmark library for conic mixed-integer and continuous optimization (2016)
  17. Gassmann, Horand; Ma, Jun; Martin, Kipp: Communication protocols for options and results in a distributed optimization environment (2016)
  18. Gleixner, Ambros M.; Steffy, Daniel E.; Wolter, Kati: Iterative refinement for linear programming (2016)
  19. Goerigk, Marc; Westphal, Stephan: A combined local search and integer programming approach to the traveling tournament problem (2016)
  20. Grimstad, Bjarne; Sandnes, Anders: Global optimization with spline constraints: a new branch-and-bound method based on B-splines (2016)

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